diff --git "a/eva02_tiny_patch14_224.mim_in22k_ep_60_full_suite.html" "b/eva02_tiny_patch14_224.mim_in22k_ep_60_full_suite.html"
--- "a/eva02_tiny_patch14_224.mim_in22k_ep_60_full_suite.html"
+++ "b/eva02_tiny_patch14_224.mim_in22k_ep_60_full_suite.html"
@@ -295,8 +295,8 @@
 <body>
     <div class="navbar">
         <a href=https://gitlab.com/mosquitoscan>MosquitoScan Project</a>
-        <a href="https://mosquitoscan.gitlab.io/experiments-reports/eva02_tiny_patch14_224.mim_in22k-exp_5_objects-subs_1_v_5-run_4/">Current Report Home</a>
-        <a href="https://gitlab.com/mosquitoscan/experiments-reports/eva02_tiny_patch14_224.mim_in22k-exp_5_objects-subs_1_v_5-run_4/">Current Report Repository</a>
+        <a href="https://mosquitoscan.gitlab.io/experiments-reports/eva02_tiny_patch14_224.mim_in22k-exp_5_objects-subs_1_v_5-run_5/">Current Report Home</a>
+        <a href="https://gitlab.com/mosquitoscan/experiments-reports/eva02_tiny_patch14_224.mim_in22k-exp_5_objects-subs_1_v_5-run_5/">Current Report Repository</a>
         <a href="https://gitlab.com/mosquitoscan/experiments-reports/">All Reports Repository</a>
     </div>
     
@@ -652,10 +652,10 @@ define("@jupyter-widgets/html-manager/dist/libembed-amd",[],(function(){return f
     
 
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Read more about\n    <a href=https://docs.deepchecks.com/stable/general/usage/customizations/auto_examples/plot_create_a_custom_suite.html?utm_source=display_output&utm_medium=referral&utm_campaign=suite_link target=\"_blank\">custom suites</a>.\n</p>\n\n"}}, "e7861a6c999740dcb7944e19286638b7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "57d6ccff557c46d693ac4b9cd555c2cb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fa02472c4dcd4863985b78178e1939f0": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_b759618fb42443f2ada12ef2ab68161a", "IPY_MODEL_d70c56ff8b354b7c9e5754a806353d5a"], "layout": "IPY_MODEL_d7b76a99707245df88188a6532936014"}}, "b759618fb42443f2ada12ef2ab68161a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9f3a94bad010455eb0dcf3e085d5eaad", "style": "IPY_MODEL_b2724b51881040b89f9dc0af9018a680", "value": "<span id=\"summary_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\"></span>"}}, "9f3a94bad010455eb0dcf3e085d5eaad": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b2724b51881040b89f9dc0af9018a680": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d70c56ff8b354b7c9e5754a806353d5a": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Didn't Pass"}, "children": ["IPY_MODEL_5e25a3c9157c4f5f91a992cf726123ce"], "layout": "IPY_MODEL_39c31fb1ab184334bfd880a1e4131a48", "selected_index": null}}, "5e25a3c9157c4f5f91a992cf726123ce": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_ee3779f835f84077b4b34917c34611d7", "IPY_MODEL_bd5ac83c4eb94f738ff973534dcc9e5b", "IPY_MODEL_37573062751348cb84bfe5fa235c8bda", "IPY_MODEL_d4aa115975fe4c0da17342183d780c0e", "IPY_MODEL_6336c8f5050142f49f8ed46dec0bc77d", "IPY_MODEL_d4aa115975fe4c0da17342183d780c0e", "IPY_MODEL_88190672fc4649739dc01168d1e312d7", "IPY_MODEL_d4aa115975fe4c0da17342183d780c0e", "IPY_MODEL_59939aad452d48a4bcbcaeb69b06b3fe", "IPY_MODEL_d4aa115975fe4c0da17342183d780c0e", "IPY_MODEL_f657d50d5f4748f585c720a1b0c77acf"], "layout": "IPY_MODEL_463f2f905b314e3688712a2c88dfe794"}}, "ee3779f835f84077b4b34917c34611d7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_17e30e555af04a3187e398a9bfafa7d9", "style": "IPY_MODEL_87e64ec9711941a39a4806abd16cdf7d", "value": "<style type=\"text/css\">\n#T_f3598 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f3598 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f3598 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f3598 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f3598 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_f3598\">\n  <thead>\n    <tr>\n      <th id=\"T_f3598_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_f3598_level0_col1\" class=\"col_heading level0 col1\" >Check</th>\n      <th id=\"T_f3598_level0_col2\" class=\"col_heading level0 col2\" >Condition</th>\n      <th id=\"T_f3598_level0_col3\" class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_f3598_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_f3598_row0_col1\" class=\"data row0 col1\" ><a href=\"#ClassPerformance_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\">Class Performance</a></td>\n      <td id=\"T_f3598_row0_col2\" class=\"data row0 col2\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_f3598_row0_col3\" class=\"data row0 col3\" >13 scores failed. Found max degradation of 77.55% for metric Recall and class anopheles_arabiensis.</td>\n    </tr>\n    <tr>\n      <td id=\"T_f3598_row1_col0\" class=\"data row1 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_f3598_row1_col1\" class=\"data row1 col1\" ><a href=\"#PredictionDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\">Prediction Drift</a></td>\n      <td id=\"T_f3598_row1_col2\" class=\"data row1 col2\" >categorical drift score < 0.15 and numerical drift score < 0.15 for prediction drift</td>\n      <td id=\"T_f3598_row1_col3\" class=\"data row1 col3\" >Failed for 1 out of 1 prediction properties.\nFound 1 categorical prediction properties with Cramer's V above threshold: {'Samples Per Class': '0.16'}</td>\n    </tr>\n    <tr>\n      <td id=\"T_f3598_row2_col0\" class=\"data row2 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_f3598_row2_col1\" class=\"data row2 col1\" ><a href=\"#SimpleModelComparison_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\">Simple Model Comparison</a></td>\n      <td id=\"T_f3598_row2_col2\" class=\"data row2 col2\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_f3598_row2_col3\" class=\"data row2 col3\" >Found metrics with gain below threshold: {'F1': {'anopheles_arabiensis': '6.67%'}}</td>\n    </tr>\n    <tr>\n      <td id=\"T_f3598_row3_col0\" class=\"data row3 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_f3598_row3_col1\" class=\"data row3 col1\" ><a href=\"#WeakSegmentsPerformance_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\">Weak Segments Performance</a></td>\n      <td id=\"T_f3598_row3_col2\" class=\"data row3 col2\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_f3598_row3_col3\" class=\"data row3 col3\" >Found a segment with cross entropy of -4.253 in comparison to an average score of -1.265 in sampled data.</td>\n    </tr>\n    <tr>\n      <td id=\"T_f3598_row4_col0\" class=\"data row4 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_f3598_row4_col1\" class=\"data row4 col1\" ><a href=\"#WeakSegmentsPerformance_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\">Weak Segments Performance</a></td>\n      <td id=\"T_f3598_row4_col2\" class=\"data row4 col2\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_f3598_row4_col3\" class=\"data row4 col3\" >Found a segment with cross entropy of -2.106 in comparison to an average score of -0.483 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "17e30e555af04a3187e398a9bfafa7d9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "87e64ec9711941a39a4806abd16cdf7d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bd5ac83c4eb94f738ff973534dcc9e5b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e3dd04a6f62b41ce8380ded9cb1fc9d0", "style": "IPY_MODEL_029ecc357c1c4ebcb89580037397c5a5", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "e3dd04a6f62b41ce8380ded9cb1fc9d0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "029ecc357c1c4ebcb89580037397c5a5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "37573062751348cb84bfe5fa235c8bda": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_0b018dd00c314e879ec4b2c781311fb2", "IPY_MODEL_8262a34ddcec49ffaf229e4fa50fef82", "IPY_MODEL_f33bd950351d443ba89569b4905e7d4c", "IPY_MODEL_2e11bc2dbb354a25bb8952d63f594838"], "layout": "IPY_MODEL_48a16045fdae4c92baf97d1d098348da"}}, "0b018dd00c314e879ec4b2c781311fb2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8d521b6c1fb64c7aa3b2f3f2bcf59e47", "style": "IPY_MODEL_963b80740376452f8e1fad546b178919", "value": "<h4 id=\"ClassPerformance_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\"><b>Class Performance</b></h4>"}}, "8d521b6c1fb64c7aa3b2f3f2bcf59e47": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "963b80740376452f8e1fad546b178919": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8262a34ddcec49ffaf229e4fa50fef82": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_83102c3ca4724db3ab396201f5324298", "style": "IPY_MODEL_a2e81c09fd604f2baa0491cdb68d0a07", "value": "<p>Summarize given metrics on a dataset and model. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_class_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "83102c3ca4724db3ab396201f5324298": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a2e81c09fd604f2baa0491cdb68d0a07": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f33bd950351d443ba89569b4905e7d4c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1af57b4d51c443a88644b6f37efc26b8", "style": "IPY_MODEL_bd8450f222494ae9afdd92d34d39a703", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_87a11 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_87a11 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_87a11 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_87a11 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_87a11 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_87a11\">\n  <thead>\n    <tr>\n      <th id=\"T_87a11_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_87a11_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_87a11_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_87a11_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_87a11_row0_col1\" class=\"data row0 col1\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_87a11_row0_col2\" class=\"data row0 col2\" >13 scores failed. Found max degradation of 77.55% for metric Recall and class anopheles_arabiensis.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "1af57b4d51c443a88644b6f37efc26b8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bd8450f222494ae9afdd92d34d39a703": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2e11bc2dbb354a25bb8952d63f594838": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_b0e999e21f454e278d59c28973f8305d", "IPY_MODEL_876bfdbb95a44d9bad3ebf3439efd59d", "IPY_MODEL_0effac9f82f5417a8177cdd4423edc73", "IPY_MODEL_ba24059ddcd5435fa0067ee5203d0f67"], "layout": "IPY_MODEL_3bb24a3d46d14a36957e4e5de1a150f7"}}, "b0e999e21f454e278d59c28973f8305d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", 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{"shape": ""}}, "name": "Train", "offsetgroup": "Train", "orientation": "v", "showlegend": true, "x": ["anopheles_arabiensis", "aedes_koreicus", "aedes_albopictus", "aedes_aegypti", "culex_tritaeniorhynchus", "anopheles_sinensis", "anopheles_freeborni", "culex_pipiens", "culex_inatomii", "aedes_dorsalis", "aedes_vexans", "culex_quinquefasciatus"], "xaxis": "x", "y": {"dtype": "float64", "shape": [12]}, "yaxis": "y", "type": "histogram", "uid": "259010dc-2b82-4de2-b749-1d4b65690fc7"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Train<br>Metric=Recall<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Train", "marker": {"color": "#003b47", "pattern": {"shape": ""}}, "name": "Train", "offsetgroup": "Train", "orientation": "v", "showlegend": false, "x": ["culex_inatomii", "culex_quinquefasciatus", "culex_pipiens", "aedes_vexans", "aedes_albopictus", "aedes_dorsalis", "anopheles_sinensis", "culex_tritaeniorhynchus", "aedes_koreicus", "anopheles_freeborni", "aedes_aegypti", "anopheles_arabiensis"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [12]}, "yaxis": "y2", "type": "histogram", "uid": "bae8144f-beea-4224-8adc-9ed1ef7faf16"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Test<br>Metric=Precision<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Test", "marker": {"color": "#818800", "pattern": {"shape": ""}}, "name": "Test", "offsetgroup": "Test", "orientation": "v", "showlegend": true, "x": ["anopheles_arabiensis", "aedes_koreicus", "anopheles_freeborni", "aedes_aegypti", "aedes_albopictus", "culex_tritaeniorhynchus", "aedes_dorsalis", "aedes_vexans", "anopheles_sinensis", "culex_pipiens", "culex_inatomii", "culex_quinquefasciatus"], "xaxis": "x", "y": {"dtype": "float64", "shape": [12]}, "yaxis": "y", "type": "histogram", "uid": "3feb50e5-73cd-4620-bf8e-ba0767020f3c"}, {"alignmentgroup": "True", "bingroup": "x", 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"AAAAAAAA8D/HcRzHcRzvPwAAAAAAAO4/fdYNpshn7T/RItv5fmrsP57xjGc84+k/mpmZmZmZ6T9l/HapJ+PnP60pa8qasuY/6k1vetOb5j9Ob3rTm97kP5Tx26WejN8/"}, {"encoding": "base64", "path": ["_data", 3, "y", "buffer"], "data": "x3Ecx3Ec7z/43nvvvffuP83MzMzMzOw/0SLb+X5q7D+7wRT5rBvsP9C6wRT5rOs/xm+XejJ+6z/kOI7jOI7nPwAAAAAAAOQ/Yid2Yid24j/u3Llz587dP5Z7GmG5p6E/"}]}, "0effac9f82f5417a8177cdd4423edc73": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_59129880981d4991b45c79a10e5f8ad0", "style": "IPY_MODEL_2e958ceb2ef447d59a4968189e735c0a", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "59129880981d4991b45c79a10e5f8ad0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2e958ceb2ef447d59a4968189e735c0a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ba24059ddcd5435fa0067ee5203d0f67": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f6f4477e353948a7b3e5a115e2012a94", "style": "IPY_MODEL_9a08eb0b65244f279041801d58d942da", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\" style=\"font-size: 14px;\">Go to top</a>"}}, "f6f4477e353948a7b3e5a115e2012a94": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9a08eb0b65244f279041801d58d942da": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3bb24a3d46d14a36957e4e5de1a150f7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "48a16045fdae4c92baf97d1d098348da": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d4aa115975fe4c0da17342183d780c0e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c5c33e994bd54bedbe5cfc3152d566a0", "style": "IPY_MODEL_f9996fb7822e4cb5a905ef8a72cb95b1", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "c5c33e994bd54bedbe5cfc3152d566a0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f9996fb7822e4cb5a905ef8a72cb95b1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6336c8f5050142f49f8ed46dec0bc77d": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_73721e8536e14f8c817dea24804978d6", "IPY_MODEL_8fc79175feb34730928059c6890a0d5c", "IPY_MODEL_8559386312864645a7de5c5b6a0c01eb", "IPY_MODEL_5d52d7b0d8c94aa88d8b13fad27cfe01"], "layout": "IPY_MODEL_f75a5053f4ff4ceb88e911a5aaae8bf8"}}, "73721e8536e14f8c817dea24804978d6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_922ff7c58247443787a2bebc1a538089", "style": "IPY_MODEL_564c5621761b419198076c2b8519b9c2", "value": "<h4 id=\"PredictionDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\"><b>Prediction Drift</b></h4>"}}, "922ff7c58247443787a2bebc1a538089": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "564c5621761b419198076c2b8519b9c2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8fc79175feb34730928059c6890a0d5c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_647fc4890aa64e8c9a497240edbb1691", "style": "IPY_MODEL_5ee1b5a257924ec2bedd8798478b917d", "value": "<p>    Calculate prediction drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_prediction_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "647fc4890aa64e8c9a497240edbb1691": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5ee1b5a257924ec2bedd8798478b917d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8559386312864645a7de5c5b6a0c01eb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_873c538e0c664895bc16c3830adb935c", "style": "IPY_MODEL_877ef31fc19e481a893244d5b016e617", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_f27d7 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f27d7 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f27d7 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f27d7 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f27d7 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_f27d7\">\n  <thead>\n    <tr>\n      <th id=\"T_f27d7_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_f27d7_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_f27d7_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_f27d7_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_f27d7_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.15 and numerical drift score < 0.15 for prediction drift</td>\n      <td id=\"T_f27d7_row0_col2\" class=\"data row0 col2\" >Failed for 1 out of 1 prediction properties.\nFound 1 categorical prediction properties with Cramer's V above threshold: {'Samples Per Class': '0.16'}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "873c538e0c664895bc16c3830adb935c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "877ef31fc19e481a893244d5b016e617": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5d52d7b0d8c94aa88d8b13fad27cfe01": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_674a304fbda04b2ca53dcfa4fbf6ed97", "IPY_MODEL_3555e26b9fb94ff4812daf34beac5785", "IPY_MODEL_c8483252ef354ab7b051eb0382ce3e63", "IPY_MODEL_7377e3418a9344189f9dfcef18b7495f", "IPY_MODEL_3747729387334a8f82bf637df11c42c4", "IPY_MODEL_8356b305572045c4a208ea5ba866e8ab"], "layout": "IPY_MODEL_07435124b11a4dcc8040d22f328cca6b"}}, "674a304fbda04b2ca53dcfa4fbf6ed97": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3918ce66fbba446f9b2e2741bd33d72c", "style": "IPY_MODEL_3e05b221398343519921d74c7fbc619a", "value": "<h5><b>Additional Outputs</b></h5>"}}, "3918ce66fbba446f9b2e2741bd33d72c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3e05b221398343519921d74c7fbc619a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3555e26b9fb94ff4812daf34beac5785": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4bbabe44ba804affb66597e51ece0c0f", "style": "IPY_MODEL_fbfde3790615461185d81dd320b3cfff", "value": "<div><span>The Drift score is a measure for the difference between two distributions. In this check, drift is measured for the distribution of the following prediction properties: ['Samples Per Class']. </span></div>"}}, "4bbabe44ba804affb66597e51ece0c0f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fbfde3790615461185d81dd320b3cfff": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c8483252ef354ab7b051eb0382ce3e63": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_59c56f31727e423886236441ed958258", "style": "IPY_MODEL_ddd0a42298084135b2d70727bb934192", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "59c56f31727e423886236441ed958258": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ddd0a42298084135b2d70727bb934192": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7377e3418a9344189f9dfcef18b7495f": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#6DB36D"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.1], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "784dfc01-4a79-4866-b146-ae54d9af04c0"}, {"base": 0.1, "marker": {"color": "#F9F871"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.05887818768054612], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "63226125-ce10-447d-a588-0c3f0391f08e"}, {"marker": {"color": "#003b47"}, "name": "Train Dataset", "x": ["aedes_albopictus", "aedes_dorsalis", "aedes_koreicus", "anopheles_arabiensis", "anopheles_freeborni", "anopheles_sinensis", "culex_inatomii", "culex_pipiens", "culex_quinquefasciatus", "culex_tritaeniorhynchus"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [10]}, "yaxis": "y2", "type": "bar", "uid": "ad268339-d135-45e0-b250-03734181d6c8"}, {"marker": {"color": "#818800"}, "name": "Test Dataset", "x": ["aedes_albopictus", "aedes_dorsalis", "aedes_koreicus", "anopheles_arabiensis", "anopheles_freeborni", "anopheles_sinensis", "culex_inatomii", "culex_pipiens", "culex_quinquefasciatus", "culex_tritaeniorhynchus"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [10]}, "yaxis": "y2", "type": "bar", "uid": "ed2fd651-1f7b-43c0-b122-b6bb159c9c5c"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"annotations": [{"font": {"size": 16}, "showarrow": false, "text": "Drift Score (Cramer's V)", "x": 0.5, "xanchor": "center", "xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}, {"font": {"size": 16}, "showarrow": false, "text": "Distribution Plot", "x": 0.5, "xanchor": "center", "xref": "paper", "y": 0.7200000000000001, "yanchor": "bottom", "yref": "paper"}], "bargroupgap": 0, "height": 400, "legend": {"title": {"text": "Legend"}, "y": 0.6, "yanchor": "top"}, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram": [{"marker": {"pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "histogram"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Samples Per Class", "x": 0.5, "xanchor": "center"}, "xaxis": {"anchor": "y", "domain": [0.0, 1.0], "dtick": 0.05, "fixedrange": true, "gridcolor": "black", "linecolor": "black", "range": [0, 0.4], "showgrid": false}, "yaxis": {"anchor": "x", "autorange": true, "color": "black", "domain": [0.9200000000000002, 1.0], "fixedrange": true, "rangemode": "normal", "showgrid": false, "showline": false, "showticklabels": false, "zeroline": false}, "xaxis2": {"anchor": "y2", "domain": [0.0, 1.0], "range": [-3, 12], "type": "category"}, "yaxis2": {"anchor": "x2", "autorange": true, "domain": [0.0, 0.7200000000000001], "fixedrange": true, "rangemode": "normal", "title": {"text": "Frequency"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 2, "y", "buffer"], "data": "WdrnkZ1qvT9f2/Ct+p20PziFx9ta7K8//a8M1o6Chj+MZ8KuzhqvP15xsohMbrc/pXQCfHbftD/Ei2fCrs66P4AwEWQ9HMI//a8M1o6Ctj8="}, {"encoding": "base64", "path": ["_data", 3, "y", "buffer"], "data": "INEKmwOJxj8LmwOJVtisP4oLUHvn9ak/CXycbXgTVz8JfJxteBOXP6xGStMpAr8/JnnIjv6VsD8meciO/pXAP5qZmZmZmbk/Ps42vIkLwD8="}]}, "3747729387334a8f82bf637df11c42c4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a5da9799f92a406cb65aed7f501d1a00", "style": "IPY_MODEL_8a95857ea1c541b29265ade4c712484d", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "a5da9799f92a406cb65aed7f501d1a00": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8a95857ea1c541b29265ade4c712484d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8356b305572045c4a208ea5ba866e8ab": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0fa672748fde4bc8874ae424cb2489e7", "style": "IPY_MODEL_1a364a1ad21f4d9180fd0b77f37bc9b0", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\" style=\"font-size: 14px;\">Go to top</a>"}}, "0fa672748fde4bc8874ae424cb2489e7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1a364a1ad21f4d9180fd0b77f37bc9b0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "07435124b11a4dcc8040d22f328cca6b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f75a5053f4ff4ceb88e911a5aaae8bf8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "88190672fc4649739dc01168d1e312d7": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_5a54dc885b984f35ab99957fc19f25d5", "IPY_MODEL_db5113367a174a9e870ab2e0aef402bb", "IPY_MODEL_2c837745aabb413b834b22a20ebe36a3", "IPY_MODEL_56c3ef02b715476fb064978354d6adda"], "layout": "IPY_MODEL_1ad5855d51794b6594405466ef24e140"}}, "5a54dc885b984f35ab99957fc19f25d5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2f9fc2ac00a5401d91061591fd8f5355", "style": "IPY_MODEL_cf4dbe0704dd4939a558cc95a92c7741", "value": "<h4 id=\"SimpleModelComparison_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\"><b>Simple Model Comparison</b></h4>"}}, "2f9fc2ac00a5401d91061591fd8f5355": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cf4dbe0704dd4939a558cc95a92c7741": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "db5113367a174a9e870ab2e0aef402bb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_037319aa44b249a68e2423f0a1116f9a", "style": "IPY_MODEL_e08b8fa9cc2f4a499b302c424de6af8d", "value": "<p>Compare given model score to simple model score (according to given model type). <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_simple_model_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "037319aa44b249a68e2423f0a1116f9a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e08b8fa9cc2f4a499b302c424de6af8d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2c837745aabb413b834b22a20ebe36a3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3e0e5e1111bb4cba9ae19127fb893dee", "style": "IPY_MODEL_2cdcc21f1525413ca8a540c44b3708b3", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_80421 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_80421 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_80421 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_80421 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_80421 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_80421\">\n  <thead>\n    <tr>\n      <th id=\"T_80421_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_80421_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_80421_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_80421_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_80421_row0_col1\" class=\"data row0 col1\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_80421_row0_col2\" class=\"data row0 col2\" >Found metrics with gain below threshold: {'F1': {'anopheles_arabiensis': '6.67%'}}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "3e0e5e1111bb4cba9ae19127fb893dee": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2cdcc21f1525413ca8a540c44b3708b3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "56c3ef02b715476fb064978354d6adda": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_94f7ab17e42d46b6bed92cfeae9cb0b7", "IPY_MODEL_ad3c0e35b6a74274aa94ec8d21abba23", "IPY_MODEL_2499bee8eed243fca4263db1fc4431c6", "IPY_MODEL_18ce47c5514c4e8f9e55e7ad7e2a7b4f"], "layout": "IPY_MODEL_9a7329119229460097f809586def657e"}}, "94f7ab17e42d46b6bed92cfeae9cb0b7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bd080d3b5ad5484881679767ef7fea51", "style": "IPY_MODEL_343432dce44243b09ac0ed331db45128", "value": "<h5><b>Additional Outputs</b></h5>"}}, "bd080d3b5ad5484881679767ef7fea51": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "343432dce44243b09ac0ed331db45128": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ad3c0e35b6a74274aa94ec8d21abba23": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Model=Simple Model<br>Metric=F1<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Simple Model", "marker": {"color": "#637a0a", "pattern": {"shape": ""}}, "name": "Simple Model", "offsetgroup": "Simple Model", "orientation": "v", "showlegend": true, "x": ["aedes_albopictus", "aedes_aegypti", "aedes_dorsalis", "aedes_koreicus", "aedes_vexans", "anopheles_arabiensis", "anopheles_freeborni", "anopheles_sinensis", "culex_inatomii", "culex_pipiens", "culex_quinquefasciatus", "culex_tritaeniorhynchus"], "xaxis": "x", "y": {"dtype": "float64", "shape": [12]}, "yaxis": "y", "type": "histogram", "uid": "8163a86c-ea7e-4bd5-9e0a-699a9d782a11"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Model=Given Model<br>Metric=F1<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Given Model", "marker": {"color": "#e5cb75", "pattern": {"shape": ""}}, "name": "Given Model", "offsetgroup": "Given Model", "orientation": "v", "showlegend": true, "x": ["aedes_albopictus", "culex_tritaeniorhynchus", "aedes_dorsalis", "culex_pipiens", "culex_inatomii", "anopheles_sinensis", "aedes_koreicus", "aedes_vexans", "anopheles_freeborni", "culex_quinquefasciatus", "aedes_aegypti", "anopheles_arabiensis"], "xaxis": "x", "y": {"dtype": "float64", "shape": [12]}, "yaxis": "y", "type": "histogram", "uid": "0c3c8e22-c731-405f-91b0-a41440e9c3fc"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"annotations": [{"showarrow": false, "text": "F1", "x": 0.5, "xanchor": "center", "xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}], "barmode": "group", "legend": {"title": {"text": "Model"}, "tracegroupgap": 0}, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram": [{"marker": {"pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "histogram"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Simple Model (Strategy: most_frequent) vs. Given Model"}, "xaxis": {"anchor": "y", "domain": [0.0, 1.0], "type": "category"}, "yaxis": {"anchor": "x", "domain": [0.0, 1.0], "showticklabels": true}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "uSEGpGMp0z8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "0SLb+X5q7D+5FglqKkTrP75jamDvmOo/eqOp82sP6T+CvWNqYO/oP36PyAnC3eg/hSxkIQtZ6D/NknJAZLjnP7dt27Zt2+Y/UhofmUDv5D/IU4I8JcjjPxEREREREbE/"}]}, "2499bee8eed243fca4263db1fc4431c6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bada9243b69c4357a5b5d2e4ca708fc7", "style": "IPY_MODEL_7072c5839e344c768e9b1e41b1a9d0b9", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "bada9243b69c4357a5b5d2e4ca708fc7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7072c5839e344c768e9b1e41b1a9d0b9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "18ce47c5514c4e8f9e55e7ad7e2a7b4f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4b924717940a434093f313b7889f1ae7", "style": "IPY_MODEL_86b586366ec748d9a1d6ad6e1f770970", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\" style=\"font-size: 14px;\">Go to top</a>"}}, "4b924717940a434093f313b7889f1ae7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "86b586366ec748d9a1d6ad6e1f770970": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9a7329119229460097f809586def657e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1ad5855d51794b6594405466ef24e140": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "59939aad452d48a4bcbcaeb69b06b3fe": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_bbba81ce53a14c8dba3b74a0693a60f8", "IPY_MODEL_bc4270c65b3a4de7ae221effb071c463", "IPY_MODEL_539b54a1c8a142b2b287bf5ea7ec7a57", "IPY_MODEL_f7522f850c27495591c169d29f0929da"], "layout": "IPY_MODEL_863ab93d56e94cf59a0a96193d133e09"}}, "bbba81ce53a14c8dba3b74a0693a60f8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_41e1c439d4ed452aae88d3e1fa1cb87e", "style": "IPY_MODEL_bb82923a8f3940f8a61e14f37237ec3a", "value": "<h4 id=\"WeakSegmentsPerformance_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\"><b>Weak Segments Performance</b></h4>"}}, "41e1c439d4ed452aae88d3e1fa1cb87e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bb82923a8f3940f8a61e14f37237ec3a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bc4270c65b3a4de7ae221effb071c463": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_391f620a2f8f44a69f4f53e6a7d02fee", "style": "IPY_MODEL_f9b106fa714f492591d55aaa023ea075", "value": "<p>Search for segments with low performance scores. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_weak_segments_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "391f620a2f8f44a69f4f53e6a7d02fee": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f9b106fa714f492591d55aaa023ea075": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "539b54a1c8a142b2b287bf5ea7ec7a57": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_da344f884c69483ebb9afe5f47734a71", "style": "IPY_MODEL_5373f58ac52a40f09c88dfb11b0257cc", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_6c17a table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6c17a thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6c17a tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6c17a th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6c17a td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_6c17a\">\n  <thead>\n    <tr>\n      <th id=\"T_6c17a_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_6c17a_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_6c17a_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_6c17a_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_6c17a_row0_col1\" class=\"data row0 col1\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_6c17a_row0_col2\" class=\"data row0 col2\" >Found a segment with cross entropy of -4.253 in comparison to an average score of -1.265 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "da344f884c69483ebb9afe5f47734a71": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5373f58ac52a40f09c88dfb11b0257cc": 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"model_module_version": "1.2.0", "state": {}}, "24c38bea4547412c91c0cd48c23b8d1f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "276885cfbd0440bcbb184b9796d4ee9c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d7599631abf54c1f98f9435a39332e11", "style": "IPY_MODEL_014c7c5748f144bf880f560050703f97", "value": "<div>Showcasing intersections of properties with weakest detected segments.<br> The full list of weak segments can be observed in the check result value. </div>"}}, "d7599631abf54c1f98f9435a39332e11": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "014c7c5748f144bf880f560050703f97": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, 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"ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Cross Entropy (percent of data)"}, "xaxis": {"anchor": "y", "constrain": "domain", "domain": [0.0, 1.0], "scaleanchor": "y", "showgrid": false, "title": {"text": "RMS Contrast"}}, "yaxis": {"anchor": "x", "autorange": "reversed", "constrain": "domain", "domain": [0.0, 1.0], "showgrid": false}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "0cb2b4f790df40bea7cd98d6cbadaef1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_957544ab743c45acb6a892495c63f330", "style": "IPY_MODEL_91795caa6de44142bede4dd088cc5c93", "value": "<div>Check ran on 710 data samples. Cross Entropy on the full data set is -1.26.</div>"}}, "957544ab743c45acb6a892495c63f330": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "91795caa6de44142bede4dd088cc5c93": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8a1787c9ac5942519c09ad3deec7db16": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "089084ce723b46e5ae25d7a25959b825": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "eab98f1744544c718bf5a60b75ee9bc5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a700286832a54713a4fec74ac6986733": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f33cc38abd1f4a14aefc96ea919ab220", "style": "IPY_MODEL_ce150e83c1724507891f2d794d675912", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\" style=\"font-size: 14px;\">Go to top</a>"}}, "f33cc38abd1f4a14aefc96ea919ab220": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ce150e83c1724507891f2d794d675912": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0f7fd296eb054f8c936ccf8f0d48402f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "863ab93d56e94cf59a0a96193d133e09": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f657d50d5f4748f585c720a1b0c77acf": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_7bb171bc596c47da80ebf2907b2c5ae7", "IPY_MODEL_c79e37d9898443b4ac0d5c950f1b6cea", "IPY_MODEL_17cf8987190f4c4b993b7006bbd89815", "IPY_MODEL_8afd8c47cde54e98a6912632b06d12cd"], "layout": "IPY_MODEL_b56dd63177774779bcd4b6b13bf991a3"}}, "7bb171bc596c47da80ebf2907b2c5ae7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4c01c170c1934d0789b74a0638e1f959", "style": "IPY_MODEL_e11ec854d820479fb6393121b523654f", "value": "<h4 id=\"WeakSegmentsPerformance_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\"><b>Weak Segments Performance</b></h4>"}}, "4c01c170c1934d0789b74a0638e1f959": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e11ec854d820479fb6393121b523654f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c79e37d9898443b4ac0d5c950f1b6cea": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_44120285dbfe4f24acd11bd34a811d96", "style": "IPY_MODEL_d3788f177bcf44548e02dbeed7689f5f", "value": "<p>Search for segments with low performance scores. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_weak_segments_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "44120285dbfe4f24acd11bd34a811d96": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d3788f177bcf44548e02dbeed7689f5f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "17cf8987190f4c4b993b7006bbd89815": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_92c21ea5b35c4e8f8ea846a6bab80c3a", "style": "IPY_MODEL_ff537c95110c413eac2c1098f8e3e863", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_35a16 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_35a16 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_35a16 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_35a16 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_35a16 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_35a16\">\n  <thead>\n    <tr>\n      <th id=\"T_35a16_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_35a16_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_35a16_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_35a16_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_35a16_row0_col1\" class=\"data row0 col1\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_35a16_row0_col2\" class=\"data row0 col2\" >Found a segment with cross entropy of -2.106 in comparison to an average score of -0.483 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "92c21ea5b35c4e8f8ea846a6bab80c3a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ff537c95110c413eac2c1098f8e3e863": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8afd8c47cde54e98a6912632b06d12cd": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_1044f5640c2b40b38586e07e230721d4", "IPY_MODEL_340e965971fb44f1b47811a3f74bda95", "IPY_MODEL_7b4e9b9cc77e4a3e905b4bfbfa460a55", "IPY_MODEL_80428d06cdb34ae2b5726af3e96e5da3", "IPY_MODEL_93a110a3485d4ca4a330cbdf79d7bdd0"], "layout": "IPY_MODEL_7c82fcb36fc440c3aec345e263f938b1"}}, "1044f5640c2b40b38586e07e230721d4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_057aafbd133e4d6eaf243aaf70731605", "style": "IPY_MODEL_998d5413e2e34248805d23070ab104e4", "value": "<h5><b>Additional Outputs</b></h5>"}}, "057aafbd133e4d6eaf243aaf70731605": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "998d5413e2e34248805d23070ab104e4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "340e965971fb44f1b47811a3f74bda95": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c2b388dbe9e2414a832ffe65e4a34c3a", "style": "IPY_MODEL_d1a839d8a0244f8882e4b3430e8e2dc6", "value": "<div>Showcasing intersections of properties with weakest detected segments.<br> The full list of weak segments can be observed in the check result value. </div>"}}, "c2b388dbe9e2414a832ffe65e4a34c3a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d1a839d8a0244f8882e4b3430e8e2dc6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7b4e9b9cc77e4a3e905b4bfbfa460a55": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_0229cf4f31884313a80f0099f0cabbde", "IPY_MODEL_d122534f2d9c4d64abd1ed1e13bcb78b"], "layout": "IPY_MODEL_f62a6510c5aa4e46b44743eac1a0a857"}}, "0229cf4f31884313a80f0099f0cabbde": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_13209ec7820f40d79ff16c8f0a10523d", "style": "IPY_MODEL_61eabf51f3e64878bc776c8ddba79821", "value": "<style>.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {min-width: fit-content;}</style>"}}, "13209ec7820f40d79ff16c8f0a10523d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "61eabf51f3e64878bc776c8ddba79821": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d122534f2d9c4d64abd1ed1e13bcb78b": {"model_name": 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"gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], 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"ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Cross Entropy (percent of data)"}, "xaxis": {"anchor": "y", "constrain": "domain", "domain": [0.0, 1.0], "scaleanchor": "y", "showgrid": false, "title": {"text": "Mean Red Relative Intensity"}}, "yaxis": {"anchor": "x", "autorange": "reversed", "constrain": "domain", "domain": [0.0, 1.0], "showgrid": false}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "a9b0e27154a24b27a8bb1c0fc2af87de": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_180a0ff723ea4a3798159db2a33c4509", "style": "IPY_MODEL_e6120e344801456b976114ec7af76daf", "value": "<div>Check ran on 5004 data samples. Cross Entropy on the full data set is -0.48.</div>"}}, "180a0ff723ea4a3798159db2a33c4509": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e6120e344801456b976114ec7af76daf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f8ca9d7a916b40a396ca1645bf5a703d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4e5d83f13b5846f28d968633fcc0536f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f62a6510c5aa4e46b44743eac1a0a857": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "80428d06cdb34ae2b5726af3e96e5da3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_859a6cd049564f909fe2b5804c1d413c", "style": "IPY_MODEL_47471e6b0c2e4a6fa758e1837110aa2f", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place, check used 5000 images from the original dataset. Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "859a6cd049564f909fe2b5804c1d413c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "47471e6b0c2e4a6fa758e1837110aa2f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "93a110a3485d4ca4a330cbdf79d7bdd0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_796652b4f9aa4ca9bbe3ea25624974f1", "style": "IPY_MODEL_5b2eb583a4f74474b8e5d3dd6525ce87", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\" style=\"font-size: 14px;\">Go to top</a>"}}, "796652b4f9aa4ca9bbe3ea25624974f1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5b2eb583a4f74474b8e5d3dd6525ce87": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7c82fcb36fc440c3aec345e263f938b1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b56dd63177774779bcd4b6b13bf991a3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "463f2f905b314e3688712a2c88dfe794": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "39c31fb1ab184334bfd880a1e4131a48": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d7b76a99707245df88188a6532936014": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d3927595440745c8ba6fc01d078d3149": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_e17c66c427a94b8d9c3f32e76d159fef", "IPY_MODEL_cbc9f9d0a5544d7982c7da9c56ecc3ac"], "layout": "IPY_MODEL_1ee3139b024e40d08de9812731020693"}}, "e17c66c427a94b8d9c3f32e76d159fef": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_22a8e92abe8c4068bada7ba5f7f0dc1d", "style": "IPY_MODEL_c2f1f20e0ac3415bbe2d64b859dec9aa", "value": "<span id=\"summary_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\"></span>"}}, "22a8e92abe8c4068bada7ba5f7f0dc1d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c2f1f20e0ac3415bbe2d64b859dec9aa": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cbc9f9d0a5544d7982c7da9c56ecc3ac": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Passed"}, "children": ["IPY_MODEL_92e7fc2008364880b5b55d72c3318d53"], "layout": "IPY_MODEL_33419b9aadee444eb06eb822dbb110c8", "selected_index": null}}, "92e7fc2008364880b5b55d72c3318d53": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_6bd5783c0cd846f6999865935b0d9952", "IPY_MODEL_f5b4d2b4aaed4ef182e633a2a5de0cee", "IPY_MODEL_36f6c059055e47b1a31998701eeade71", "IPY_MODEL_d2de45e7b17b4f1bbc8f637c843a21c0", "IPY_MODEL_b41a2dea580a4733bf6586112b3bee93", "IPY_MODEL_d2de45e7b17b4f1bbc8f637c843a21c0", "IPY_MODEL_edc5447de6824acd8e5ad1b32ab3077b", "IPY_MODEL_d2de45e7b17b4f1bbc8f637c843a21c0", "IPY_MODEL_95403452f2c749948b4df4510f3456a2"], "layout": "IPY_MODEL_799cab6cd58841bd962bb09dfa8847b6"}}, "6bd5783c0cd846f6999865935b0d9952": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3e46568a703741f59d38ba01b9df4a33", "style": "IPY_MODEL_25875c3b36404e848edb559b8ee699bf", "value": "<style type=\"text/css\">\n#T_a2096 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a2096 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a2096 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a2096 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a2096 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_a2096\">\n  <thead>\n    <tr>\n      <th id=\"T_a2096_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_a2096_level0_col1\" class=\"col_heading level0 col1\" >Check</th>\n      <th id=\"T_a2096_level0_col2\" class=\"col_heading level0 col2\" >Condition</th>\n      <th id=\"T_a2096_level0_col3\" class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_a2096_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_a2096_row0_col1\" class=\"data row0 col1\" ><a href=\"#ImagePropertyDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\">Image Property Drift</a></td>\n      <td id=\"T_a2096_row0_col2\" class=\"data row0 col2\" >drift score < 0.2 for image properties drift</td>\n      <td id=\"T_a2096_row0_col3\" class=\"data row0 col3\" >Passed for 7 properties out of 7 properties.\nFound property \"Mean Blue Relative Intensity\" has the highest numerical drift score: 0.13</td>\n    </tr>\n    <tr>\n      <td id=\"T_a2096_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_a2096_row1_col1\" class=\"data row1 col1\" ><a href=\"#PropertyLabelCorrelationChange_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\">Property Label Correlation Change</a></td>\n      <td id=\"T_a2096_row1_col2\" class=\"data row1 col2\" >Train-Test properties' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_a2096_row1_col3\" class=\"data row1 col3\" >0 PPS found for all properties</td>\n    </tr>\n    <tr>\n      <td id=\"T_a2096_row2_col0\" class=\"data row2 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_a2096_row2_col1\" class=\"data row2 col1\" ><a href=\"#NewLabels_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\">New Labels</a></td>\n      <td id=\"T_a2096_row2_col2\" class=\"data row2 col2\" >Percentage of new labels in the test set is less or equal to 0.5%</td>\n      <td id=\"T_a2096_row2_col3\" class=\"data row2 col3\" >No new labels were found in test set.</td>\n    </tr>\n    <tr>\n      <td id=\"T_a2096_row3_col0\" class=\"data row3 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_a2096_row3_col1\" class=\"data row3 col1\" ><a href=\"#LabelDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\">Label Drift</a></td>\n      <td id=\"T_a2096_row3_col2\" class=\"data row3 col2\" >categorical drift score < 0.15 and numerical drift score < 0.15</td>\n      <td id=\"T_a2096_row3_col3\" class=\"data row3 col3\" >Passed for 1 label properties out of 1 label properties.\nFound label property \"Samples Per Class\" has the highest categorical drift score: 0.15</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "3e46568a703741f59d38ba01b9df4a33": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "25875c3b36404e848edb559b8ee699bf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f5b4d2b4aaed4ef182e633a2a5de0cee": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bb18d6fa96914a188211d1585a106ccf", "style": "IPY_MODEL_516dd23539144bdeb220c4ab813dc77f", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "bb18d6fa96914a188211d1585a106ccf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "516dd23539144bdeb220c4ab813dc77f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "36f6c059055e47b1a31998701eeade71": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_2a7b6d9127d646d58a1262caccfdd207", "IPY_MODEL_913195daa3434c5ba6473047726f80b6", "IPY_MODEL_b70727d2a7514addb6c2248553b24e48", "IPY_MODEL_589b85c906004da99c0ab5f1b37e3ed3"], "layout": "IPY_MODEL_20eabc28007a422ab997d06ed510b792"}}, "2a7b6d9127d646d58a1262caccfdd207": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_93d15019044a40258bda578d371e06ae", "style": "IPY_MODEL_32300ca8fd7e4b3aaab71f04b776aa0c", "value": "<h4 id=\"ImagePropertyDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\"><b>Image Property Drift</b></h4>"}}, "93d15019044a40258bda578d371e06ae": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "32300ca8fd7e4b3aaab71f04b776aa0c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "913195daa3434c5ba6473047726f80b6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0f24490a4fa84bc0bf66437f6dab40cf", "style": "IPY_MODEL_135c71b17d5c49c2a36ba4f397fdab75", "value": "<p>    Calculate drift between train dataset and test dataset per image property, using statistical measures. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_image_property_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "0f24490a4fa84bc0bf66437f6dab40cf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "135c71b17d5c49c2a36ba4f397fdab75": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b70727d2a7514addb6c2248553b24e48": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c0f6aa694b0d4fe29ccbb5ec556f3c0b", "style": "IPY_MODEL_137c28a5a1614aac8a47cc45ce12434a", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_104ba table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_104ba thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_104ba tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_104ba th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_104ba td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_104ba\">\n  <thead>\n    <tr>\n      <th id=\"T_104ba_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_104ba_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_104ba_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_104ba_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_104ba_row0_col1\" class=\"data row0 col1\" >drift score < 0.2 for image properties drift</td>\n      <td id=\"T_104ba_row0_col2\" class=\"data row0 col2\" >Passed for 7 properties out of 7 properties.\nFound property \"Mean Blue Relative Intensity\" has the highest numerical drift score: 0.13</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "c0f6aa694b0d4fe29ccbb5ec556f3c0b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "137c28a5a1614aac8a47cc45ce12434a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "589b85c906004da99c0ab5f1b37e3ed3": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_b4ad212472f9404ca86b9d912b0cd791", "IPY_MODEL_8442ad0f4bbc46799a157b79eb8428f1", "IPY_MODEL_04eb12889edb477abf3bac0c56914162", "IPY_MODEL_6c6e18c15cd7487487bbb9befd417cab", "IPY_MODEL_46b5c6d3e9eb4a5ab1f3e93498348072", "IPY_MODEL_89138918ee724c1aa8b53d683de3a8f9", "IPY_MODEL_90c178505c8d453fb4278dc6050968e5", "IPY_MODEL_0d776713c9434db5b462955b532280bd", "IPY_MODEL_c7200f66542b42ecae4eb54f3032c58e", "IPY_MODEL_39552c3bfa5649a8a6088f772772f71e", "IPY_MODEL_5055c75276134449b55f76c34f18fb66", "IPY_MODEL_de028e9d73e5468082848988dc62bf6a"], "layout": "IPY_MODEL_9ade7b9c83f8403b88da3e35a43c52d4"}}, "b4ad212472f9404ca86b9d912b0cd791": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4adaf010ac21439c99c0abb3462c27c7", "style": "IPY_MODEL_abc35295cde242d1ab43c8ced6475b5b", "value": "<h5><b>Additional Outputs</b></h5>"}}, "4adaf010ac21439c99c0abb3462c27c7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "abc35295cde242d1ab43c8ced6475b5b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8442ad0f4bbc46799a157b79eb8428f1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_df4a6766e1164c1e84c14380968a6a4e", "style": "IPY_MODEL_f511eb9bff8f47dfabb6acb0e42116eb", "value": "<div><span> The Drift score is a measure for the difference between two distributions. In this check, drift is measured for the distribution of the following image properties: ['Area', 'Aspect Ratio', 'Brightness', 'Mean Blue Relative Intensity', 'Mean Green Relative Intensity', 'Mean Red Relative Intensity', 'RMS Contrast'].</span></div>"}}, "df4a6766e1164c1e84c14380968a6a4e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f511eb9bff8f47dfabb6acb0e42116eb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "04eb12889edb477abf3bac0c56914162": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b7fd37f445be464a881c7b532d7f62f2", "style": "IPY_MODEL_36e1e0cbeffb4b0988e838d1ecaf45d0", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, 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"38091e60-71f8-465b-90c3-e08d44a5225c"}, {"fill": "tozeroy", "line": {"color": "#003b47", "shape": "linear"}, "name": "Train Dataset", "x": [0.2811262392483882, 0.2824125635025096, 0.283698887756631, 0.28498521201075244, 0.28627153626487384, 0.28755786051899523, 0.28884418477311663, 0.2901305090272381, 0.2914168332813595, 0.2927031575354809, 0.29398948178960227, 0.2952758060437237, 0.2965621302978451, 0.2978484545519665, 0.2991347788060879, 0.30042110306020936, 0.30170742731433076, 0.30299375156845215, 0.30428007582257355, 0.305566400076695, 0.3068527243308164, 0.3081390485849378, 0.3094253728390592, 0.31071169709318064, 0.31199802134730203, 0.31328434560142343, 0.3145706698555448, 0.3158569941096663, 0.31670852614611844, 0.3171433183637877, 0.31810273172076275, 0.31842964261790907, 0.31971596687203047, 0.3197300034633604, 0.3205274966451448, 0.3210022911261519, 0.3210865033867953, 0.3218440814419865, 0.32228439065524367, 0.3222886153802733, 0.3228212648413013, 0.32322402808569156, 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0.3328423506703004, 0.3333326342880304, 0.3333564843115991, 0.333865533667366, 0.3351518579214874, 0.33643818217560884, 0.33772450642973023, 0.3390108306838516, 0.340297154937973, 0.3415834791920945, 0.34286980344621587, 0.34415612770033727], "xaxis": "x2", "y": {"dtype": "float64", "shape": [152]}, "yaxis": "y2", "type": "scatter", "uid": "19384e14-2365-4fd1-b9fd-6d4b29267001"}, {"line": {"color": "#003b47", "dash": "dash"}, "mode": "lines+markers", "name": "Train Mean", "x": [0.32856311709552155, 0.32856311709552155], "xaxis": "x2", "y": [0, 98.275540865552], "yaxis": "y2", "type": "scatter", "uid": "69ac05a0-b7b7-4823-8c54-78ae24401cae"}, {"line": {"color": "#003b47"}, "mode": "lines", "name": "Train Median", "x": [0.32981572260049874, 0.32981572260049874], "xaxis": "x2", "y": [0, 139.90109958292058], "yaxis": "y2", "type": "scatter", "uid": "19b8862d-3243-4e4e-bad1-ebe078962b40"}, {"fill": "tozeroy", "line": {"color": "#818800", "shape": "linear"}, "name": "Test Dataset", "x": 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{"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3a838f86a96b4410aaa7ccd3e4fd5483": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "de028e9d73e5468082848988dc62bf6a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e39e3405f2184aa6b539b2081751865e", "style": "IPY_MODEL_d1418ade30244435b5960421c80d1e1b", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\" style=\"font-size: 14px;\">Go to top</a>"}}, "e39e3405f2184aa6b539b2081751865e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d1418ade30244435b5960421c80d1e1b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9ade7b9c83f8403b88da3e35a43c52d4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "20eabc28007a422ab997d06ed510b792": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d2de45e7b17b4f1bbc8f637c843a21c0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7265ab86bdd64317bb3e16a643c02f2c", "style": "IPY_MODEL_891684e375124461964e2388b282fa1b", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "7265ab86bdd64317bb3e16a643c02f2c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "891684e375124461964e2388b282fa1b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b41a2dea580a4733bf6586112b3bee93": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_83d2791803d14a70abde1b3967969b14", "IPY_MODEL_391fbf8d836b4f9e88da2817e6e59334", "IPY_MODEL_19b4ff6db130466ab5e160e0f16714e0", "IPY_MODEL_ed6e974ce71f4b3d8f4df605feeceded"], "layout": "IPY_MODEL_662dd00926b14da1b371c8f1e177b67c"}}, "83d2791803d14a70abde1b3967969b14": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f7ad9a99082144fd98304561d6d0b488", "style": "IPY_MODEL_5a053ff4df0d4b7c910689ae8d2c2968", "value": "<h4 id=\"PropertyLabelCorrelationChange_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\"><b>Property Label Correlation Change</b></h4>"}}, "f7ad9a99082144fd98304561d6d0b488": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5a053ff4df0d4b7c910689ae8d2c2968": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "391fbf8d836b4f9e88da2817e6e59334": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_167fd4a4f9884c5ab8f1c4524ee3eece", "style": "IPY_MODEL_13606a457308460baea573b524942232", "value": "<p>    Return the Predictive Power Score of image properties, in order to estimate their ability to predict the label. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_property_label_correlation_change.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "167fd4a4f9884c5ab8f1c4524ee3eece": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "13606a457308460baea573b524942232": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "19b4ff6db130466ab5e160e0f16714e0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_646590a9ce6947729f0dccda0cb14232", "style": "IPY_MODEL_3b13ceb8867b478288af767cbb385c3f", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_c3677 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c3677 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c3677 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c3677 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c3677 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_c3677\">\n  <thead>\n    <tr>\n      <th id=\"T_c3677_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_c3677_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_c3677_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_c3677_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_c3677_row0_col1\" class=\"data row0 col1\" >Train-Test properties' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_c3677_row0_col2\" class=\"data row0 col2\" >0 PPS found for all properties</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "646590a9ce6947729f0dccda0cb14232": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3b13ceb8867b478288af767cbb385c3f": {"model_name": "DescriptionStyleModel", "model_module": 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"@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e9d2b02398ff485e97246b224dedbb24", "style": "IPY_MODEL_bceb5aa53e414f1896bd693372a7dca2", "value": "<h5><b>Additional Outputs</b></h5>"}}, "e9d2b02398ff485e97246b224dedbb24": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bceb5aa53e414f1896bd693372a7dca2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "298848d4777d43618fed84b7b843b9bb": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "#003b47"}, "name": "Train", "text": ["<b>0</b>", "<b>0</b>", "<b>0</b>"], "textposition": "outside", "x": ["aedes_albopictus", "anopheles_arabiensis", "aedes_koreicus"], "y": [0, 0, 0], "type": "bar", "uid": "57b7c632-129d-423e-83c5-c254aeef06f9"}, {"marker": {"color": "#818800"}, "name": "Test", "text": ["<b>0.26</b>", "<b>0.0</b>", "<b>0.0</b>"], "textposition": "outside", "x": ["aedes_albopictus", "anopheles_arabiensis", "aedes_koreicus"], "y": {"dtype": "float64", "shape": [3]}, "type": "bar", "uid": "73222307-933f-4226-89b8-60187327f7b3"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"barmode": "group", "height": 500, "legend": {"x": 1.0, "y": 1.0}, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], 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"radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Mean Red Relative Intensity: Predictive Power Score (PPS) Per Class"}, "xaxis": {"range": [-3, 5], "title": {"text": "Class"}, "type": "category"}, "yaxis": {"range": [0, 1.05], "title": {"text": "Predictive Power Score (PPS)"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "Eo2f9vjo0D8AAAAAAAAAAAAAAAAAAAAA"}]}, "704e23bbb6e34d45b2ab2037d5259a7b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f4af9132e525411fa6778fe31eac1c43", "style": "IPY_MODEL_686596ff0933409b851eab64c2201673", "value": "<div>The Predictive Power Score (PPS) is used to estimate the ability of an image property (such as brightness)to predict the label by itself. (Read more about <a href=https://docs.deepchecks.com/stable/vision/auto_checks/train_test_validation/plot_feature_label_correlation_change.html target=\"_blank\">Predictive Power Score</a>)</div>"}}, "f4af9132e525411fa6778fe31eac1c43": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "686596ff0933409b851eab64c2201673": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "23de26c078164de99d8ee7aeb36c54f8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_eca2c77a537a4529bf8e403dcb30439a", "style": "IPY_MODEL_0cd496aa75274d9580cbb8344cc61a8d", "value": "<div><u>In the graph above</u>, we should suspect we have problems in our data if:</div>"}}, "eca2c77a537a4529bf8e403dcb30439a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0cd496aa75274d9580cbb8344cc61a8d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a1b4458f79c04a6ea9843906b08dc241": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8e5fb0da7dfe431487f63c22de562ac5", "style": "IPY_MODEL_a06f4b0a8c3a49b4900b21d1524dd1cf", "value": "<div>1. <b>Train dataset PPS values are high</b>:</div>"}}, "8e5fb0da7dfe431487f63c22de562ac5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a06f4b0a8c3a49b4900b21d1524dd1cf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b7c4b65cfd2040169587bc28ba89cc10": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9aa6ae65a4f148808d7f7dff0d798c15", "style": "IPY_MODEL_bbf5e19e0d184351ab9004a5c87c7b83", "value": "<div>   A high PPS (close to 1) can mean that there's a bias in the dataset, as a single property can predict   the label successfully, using simple classic ML algorithms</div>"}}, "9aa6ae65a4f148808d7f7dff0d798c15": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bbf5e19e0d184351ab9004a5c87c7b83": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d9ae0aea11a8443dafa2f7f5c2907cd8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_93060e0b4679416b8ac262c09f0eba92", "style": "IPY_MODEL_ec82f99b906441e89367c7c10a921d98", "value": "<div>2. <b>Large difference between train and test PPS</b> (train PPS is larger):</div>"}}, "93060e0b4679416b8ac262c09f0eba92": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ec82f99b906441e89367c7c10a921d98": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "89fce36b37204d4195a955f448c62d39": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fc22a321a4e147af89fa3a12f892165f", "style": "IPY_MODEL_fbf34e5fb31c430b82ddb9efc241dfb1", "value": "<div>   An even more powerful indication of dataset bias, as an image property that was powerful in train</div>"}}, "fc22a321a4e147af89fa3a12f892165f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fbf34e5fb31c430b82ddb9efc241dfb1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "986e2d2270bb4e6ea63e35e3a21ef1a7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_893d1e8b824f4df38ab2bbac2ea97398", "style": "IPY_MODEL_474b8898b09a4be3af9b03b4da251795", "value": "<div>   but not in test can be explained by bias in train that is not relevant to a new dataset.</div>"}}, "893d1e8b824f4df38ab2bbac2ea97398": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "474b8898b09a4be3af9b03b4da251795": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4094b6a6ff1749448f5f757ca668448b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_af862b685f1e476cae977cb22af43afa", "style": "IPY_MODEL_5de8b14b73764014891c040e8abb5d66", "value": "<div>3. <b>Large difference between test and train PPS</b> (test PPS is larger):</div>"}}, "af862b685f1e476cae977cb22af43afa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5de8b14b73764014891c040e8abb5d66": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bcc9cdeaf6c04be3912135cfbab5d57e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7931f9f2edff4b7da5e311021ca70a51", "style": "IPY_MODEL_54c8805591b344579d83f98f8f4d3c2c", "value": "<div>   An anomalous value, could indicate drift in test dataset that caused a coincidental correlation to the target label.</div>"}}, "7931f9f2edff4b7da5e311021ca70a51": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "54c8805591b344579d83f98f8f4d3c2c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ed225dc6bd454beb859ab2ee674896f4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c94ed068b5904466bcaa7ddd7a2dd53b", "style": "IPY_MODEL_d937f5648d3c45878e733187d32cc049", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "c94ed068b5904466bcaa7ddd7a2dd53b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d937f5648d3c45878e733187d32cc049": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b27f104254e94f05868068c13d6ab244": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_25459c170ed4450cbf8f51b93009b905", "style": "IPY_MODEL_c43b705ae610488babc11707c22aa181", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\" style=\"font-size: 14px;\">Go to top</a>"}}, "25459c170ed4450cbf8f51b93009b905": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c43b705ae610488babc11707c22aa181": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0919696725c342e4a6c4f1e18303299c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "662dd00926b14da1b371c8f1e177b67c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "edc5447de6824acd8e5ad1b32ab3077b": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_820629be640e4c00a66964282983f404", "IPY_MODEL_ada073c09a984023bc5a1e27db2521b7", "IPY_MODEL_374a18dcf63b4084aa221086bef621bc", "IPY_MODEL_bd3c21d1173d4b9eb5a14d51d46aef39"], "layout": "IPY_MODEL_9d694493cb6a41eeb833351bffb298a9"}}, "820629be640e4c00a66964282983f404": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5f42413d04a54e4cb7640ffe208d9ba8", "style": "IPY_MODEL_be7f77fc8bd847f7bd24b602552a5860", "value": "<h4 id=\"NewLabels_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\"><b>New Labels</b></h4>"}}, "5f42413d04a54e4cb7640ffe208d9ba8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "be7f77fc8bd847f7bd24b602552a5860": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ada073c09a984023bc5a1e27db2521b7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_766956b0560d40239a1b7267607f3013", "style": "IPY_MODEL_d437ba9c86de4120a05c1b8d5b424213", "value": "<p>Detects labels that appear only in the test set. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_new_labels.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "766956b0560d40239a1b7267607f3013": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d437ba9c86de4120a05c1b8d5b424213": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "374a18dcf63b4084aa221086bef621bc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_812e2b0e63f042ebb7be68b49bd0eaa4", "style": "IPY_MODEL_cec467b7b3aa4a06bb991ca4df88f386", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_a41d4 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a41d4 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a41d4 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a41d4 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a41d4 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_a41d4\">\n  <thead>\n    <tr>\n      <th id=\"T_a41d4_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_a41d4_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_a41d4_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_a41d4_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_a41d4_row0_col1\" class=\"data row0 col1\" >Percentage of new labels in the test set is less or equal to 0.5%</td>\n      <td id=\"T_a41d4_row0_col2\" class=\"data row0 col2\" >No new labels were found in test set.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "812e2b0e63f042ebb7be68b49bd0eaa4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cec467b7b3aa4a06bb991ca4df88f386": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bd3c21d1173d4b9eb5a14d51d46aef39": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_dd985db28e9d46de96c816ca28a8af18", "IPY_MODEL_f4e63a6840d84c9c8d0c98c37750ea08", "IPY_MODEL_578f42fee38144f8b3fb270d464512c7"], "layout": "IPY_MODEL_e323ae2604bc43ee811559565d480062"}}, "dd985db28e9d46de96c816ca28a8af18": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5d10ffcb5f7a4d0eaf063d0b0173e9a5", "style": "IPY_MODEL_9c6f16d89e4b4f57bc5e3c6bb3cf43a6", "value": "<h5><b>Additional Outputs</b></h5>"}}, "5d10ffcb5f7a4d0eaf063d0b0173e9a5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9c6f16d89e4b4f57bc5e3c6bb3cf43a6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f4e63a6840d84c9c8d0c98c37750ea08": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8ab60b66e17a4b9e842fbf991b117c54", "style": "IPY_MODEL_21f100dcabdf40f2a99ae9888a4983c1", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "8ab60b66e17a4b9e842fbf991b117c54": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "21f100dcabdf40f2a99ae9888a4983c1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "578f42fee38144f8b3fb270d464512c7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7a0707539e104fec8f3c2296e5a5fbd9", "style": "IPY_MODEL_01bf3a44991448db916eddd7a593abbf", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\" style=\"font-size: 14px;\">Go to top</a>"}}, "7a0707539e104fec8f3c2296e5a5fbd9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "01bf3a44991448db916eddd7a593abbf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e323ae2604bc43ee811559565d480062": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9d694493cb6a41eeb833351bffb298a9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "95403452f2c749948b4df4510f3456a2": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_16b67232a46a499fa2e78b984f0912ea", "IPY_MODEL_cfb8f569d7614bba961d03c703d21a13", "IPY_MODEL_1392cf0161dc416da79cc78b47570f1c", "IPY_MODEL_8432fddc5918485b821bac982ee608c5"], "layout": "IPY_MODEL_37c2b547e28f4852ad8e9b30305794cc"}}, "16b67232a46a499fa2e78b984f0912ea": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_94fe40df63ea41b29a83f40c9573090b", "style": "IPY_MODEL_aa0b43557c0a444a8e040c81a94613cf", "value": "<h4 id=\"LabelDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\"><b>Label Drift</b></h4>"}}, "94fe40df63ea41b29a83f40c9573090b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "aa0b43557c0a444a8e040c81a94613cf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cfb8f569d7614bba961d03c703d21a13": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_11fd8c549aec4e19b61d345b89e25009", "style": "IPY_MODEL_a415fa7f19514aa9bab0e10beee62644", "value": "<p>    Calculate label drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_label_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "11fd8c549aec4e19b61d345b89e25009": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a415fa7f19514aa9bab0e10beee62644": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1392cf0161dc416da79cc78b47570f1c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ecda51ae4a0446df8a6f7ebd94095de8", "style": "IPY_MODEL_1be17bb4f2cc4a3992acb90ce7d55a02", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_74286 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_74286 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_74286 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_74286 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_74286 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_74286\">\n  <thead>\n    <tr>\n      <th id=\"T_74286_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_74286_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_74286_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_74286_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_74286_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.15 and numerical drift score < 0.15</td>\n      <td id=\"T_74286_row0_col2\" class=\"data row0 col2\" >Passed for 1 label properties out of 1 label properties.\nFound label property \"Samples Per Class\" has the highest categorical drift score: 0.15</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "ecda51ae4a0446df8a6f7ebd94095de8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1be17bb4f2cc4a3992acb90ce7d55a02": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8432fddc5918485b821bac982ee608c5": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_ccf0b6a8057b407a8585c3f23ff609dc", "IPY_MODEL_53157d7808a146a6b8f437bddf590e9d", "IPY_MODEL_6f1a139ed9e146d78cd8d065b5537470", "IPY_MODEL_6a1a777add3246c9ad1b681ba5072637", "IPY_MODEL_d69d106047ec4e348193f17bf5edbd32", "IPY_MODEL_3fdb79a1a9014f10b269621af236257b"], "layout": "IPY_MODEL_9c84a3b1d3ba4fe2a7a9ae8758e708ad"}}, "ccf0b6a8057b407a8585c3f23ff609dc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e451b00c00cc428fb84381d299f90183", "style": "IPY_MODEL_2be3127d3af247968b652d90fc4cbd7d", "value": "<h5><b>Additional Outputs</b></h5>"}}, "e451b00c00cc428fb84381d299f90183": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2be3127d3af247968b652d90fc4cbd7d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "53157d7808a146a6b8f437bddf590e9d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c5766e66d8fd4bf986663bf7f095d662", "style": "IPY_MODEL_3633685111d44be4950be3bf649ce49f", "value": "<div><span> The Drift score is a measure for the difference between two distributions. In this check, drift is measured for the distribution of the following label properties: ['Samples Per Class'].</span></div>"}}, "c5766e66d8fd4bf986663bf7f095d662": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3633685111d44be4950be3bf649ce49f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6f1a139ed9e146d78cd8d065b5537470": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a3e51861dcae4a4497e19b795a02539a", "style": "IPY_MODEL_408ccb3aab964666816956dcd341b636", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "a3e51861dcae4a4497e19b795a02539a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "408ccb3aab964666816956dcd341b636": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6a1a777add3246c9ad1b681ba5072637": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#6DB36D"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.1], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "51395b9a-18f6-46fd-a412-9cabb821698f"}, {"base": 0.1, "marker": {"color": "#F9F871"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.04604031048509044], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "811020b1-1b4d-4b11-85b6-10a63bd80e0a"}, {"marker": {"color": "#003b47"}, "name": "Train Dataset", "x": ["aedes_aegypti", "aedes_albopictus", "aedes_dorsalis", 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"center"}, "xaxis": {"anchor": "y", "domain": [0.0, 1.0], "dtick": 0.05, "fixedrange": true, "gridcolor": "black", "linecolor": "black", "range": [0, 0.4], "showgrid": false}, "yaxis": {"anchor": "x", "autorange": true, "color": "black", "domain": [0.9200000000000002, 1.0], "fixedrange": true, "rangemode": "normal", "showgrid": false, "showline": false, "showticklabels": false, "zeroline": false}, "xaxis2": {"anchor": "y2", "domain": [0.0, 1.0], "range": [-3, 12], "type": "category"}, "yaxis2": {"anchor": "x2", "autorange": true, "domain": [0.0, 0.7200000000000001], "fixedrange": true, "rangemode": "normal", "title": {"text": "Frequency"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 2, "y", "buffer"], "data": "Ish6OCSlsz80LLhUtne9P0h2qnULmLM/9P3tx0WZsT/TPo/sVOu2P+t3Um+gULI/Opf/lcHasT8HoOZT4vqyP9hrG75Vv7M/rpBfr234tj8="}, {"encoding": "base64", "path": ["_data", 3, "y", "buffer"], "data": "en0GXTVSuj8g0QqbA4nGP2rvvD6Drqo/iOzoXwkxtD+KC1B75/W5P2jQVSOl6aQ/xyQP2dG/oj8omC+q3FqmP4oLUHvn9ak/C5sDiVbYvD8="}]}, "d69d106047ec4e348193f17bf5edbd32": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dee2ba848e47456f9eef623d2480240c", "style": "IPY_MODEL_edbbf9ff1dd14a59828180423ca9b221", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "dee2ba848e47456f9eef623d2480240c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "edbbf9ff1dd14a59828180423ca9b221": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3fdb79a1a9014f10b269621af236257b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5d34f640346847d7943896bf3bcb8997", "style": "IPY_MODEL_9566f307424d46bf89952f5109cc35e5", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\" style=\"font-size: 14px;\">Go to top</a>"}}, "5d34f640346847d7943896bf3bcb8997": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9566f307424d46bf89952f5109cc35e5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9c84a3b1d3ba4fe2a7a9ae8758e708ad": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "37c2b547e28f4852ad8e9b30305794cc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "799cab6cd58841bd962bb09dfa8847b6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "33419b9aadee444eb06eb822dbb110c8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1ee3139b024e40d08de9812731020693": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "26a76bbef9a643a39bfaf354221ef443": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_4f4b338b5cc343b5b662945f72949789", "IPY_MODEL_7cff5b19d6b94e2baa11f409a6aa0cac"], "layout": "IPY_MODEL_cc9c67984ffd4265a2ed4b8f1b008959"}}, "4f4b338b5cc343b5b662945f72949789": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0a0aa1e572b44a6f8a64f88fc28cf8bd", "style": "IPY_MODEL_e5c3ce81a97344db9acaa22bfe9f8d16", "value": "<span id=\"summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"></span>"}}, "0a0aa1e572b44a6f8a64f88fc28cf8bd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e5c3ce81a97344db9acaa22bfe9f8d16": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7cff5b19d6b94e2baa11f409a6aa0cac": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Other"}, "children": ["IPY_MODEL_9682340636394c26bcf4718e363a7584"], "layout": "IPY_MODEL_18b69ee594e3458bbcda28b402f8ae6b", "selected_index": null}}, "9682340636394c26bcf4718e363a7584": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_acce1f425fde4fa99b92ae33b82a1420", "IPY_MODEL_45125707b7614447bb1f9bf64fac1db0", "IPY_MODEL_c007c989ac0e4f9e82c22d1346d51307", "IPY_MODEL_429eaa7d2eb8434fa8cca3ca92b8070d", "IPY_MODEL_e8378a1d24bd431d9fd310f4b2a8783a", "IPY_MODEL_429eaa7d2eb8434fa8cca3ca92b8070d", "IPY_MODEL_5a4b8eeb6baa46a6b7a7e27b0aee8dd1", "IPY_MODEL_429eaa7d2eb8434fa8cca3ca92b8070d", "IPY_MODEL_fe0dec00f550439e8a164c40f3c66238", "IPY_MODEL_429eaa7d2eb8434fa8cca3ca92b8070d", "IPY_MODEL_1b3eb64144364256b1e4aa640c96293c", "IPY_MODEL_429eaa7d2eb8434fa8cca3ca92b8070d", "IPY_MODEL_4d0f64dff13447489834baef99b9c17f", "IPY_MODEL_429eaa7d2eb8434fa8cca3ca92b8070d", "IPY_MODEL_a8be309e1a044ddbbe98dce25ac97d38", "IPY_MODEL_429eaa7d2eb8434fa8cca3ca92b8070d", "IPY_MODEL_c46d10488d2d49b6b18c1d530943c036"], "layout": "IPY_MODEL_d25456b2e4b24492bca86196fec5b05d"}}, "acce1f425fde4fa99b92ae33b82a1420": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_53e47d558cc8478e988587c412844906", "style": "IPY_MODEL_edf5eb2645be4237adbd5db4263140e1", "value": "<style type=\"text/css\">\n#T_2156b table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2156b thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2156b tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2156b th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2156b td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_2156b\">\n  <thead>\n    <tr>\n      <th id=\"T_2156b_level0_col0\" class=\"col_heading level0 col0\" >Check</th>\n      <th id=\"T_2156b_level0_col1\" class=\"col_heading level0 col1\" >Summary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_2156b_row0_col0\" class=\"data row0 col0\" ><a href=\"#ImageDatasetDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Image Dataset Drift</a></td>\n      <td id=\"T_2156b_row0_col1\" class=\"data row0 col1\" >Calculate drift between the entire train and test datasets (based on image properties) using a trained model. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_image_dataset_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_2156b_row1_col0\" class=\"data row1 col0\" ><a href=\"#ImagePropertyOutliers_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Image Property Outliers</a></td>\n      <td id=\"T_2156b_row1_col1\" class=\"data row1 col1\" >Find outliers images with respect to the given properties. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_image_property_outliers.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_2156b_row2_col0\" class=\"data row2 col0\" ><a href=\"#ImagePropertyOutliers_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Image Property Outliers</a></td>\n      <td id=\"T_2156b_row2_col1\" class=\"data row2 col1\" >Find outliers images with respect to the given properties. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_image_property_outliers.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_2156b_row3_col0\" class=\"data row3 col0\" ><a href=\"#PropertyLabelCorrelation_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Property Label Correlation</a></td>\n      <td id=\"T_2156b_row3_col1\" class=\"data row3 col1\" >    Return the Predictive Power Score of image properties, in order to estimate their ability to predict the label. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_property_label_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_2156b_row4_col0\" class=\"data row4 col0\" ><a href=\"#PropertyLabelCorrelation_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Property Label Correlation</a></td>\n      <td id=\"T_2156b_row4_col1\" class=\"data row4 col1\" >    Return the Predictive Power Score of image properties, in order to estimate their ability to predict the label. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_property_label_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_2156b_row5_col0\" class=\"data row5 col0\" ><a href=\"#ConfusionMatrix_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Confusion Matrix</a></td>\n      <td id=\"T_2156b_row5_col1\" class=\"data row5 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_2156b_row6_col0\" class=\"data row6 col0\" ><a href=\"#ConfusionMatrix_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Confusion Matrix</a></td>\n      <td id=\"T_2156b_row6_col1\" class=\"data row6 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_2156b_row7_col0\" class=\"data row7 col0\" ><a href=\"#HeatmapComparison_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Heatmap Comparison</a></td>\n      <td id=\"T_2156b_row7_col1\" class=\"data row7 col1\" >Check if the average image brightness (or bbox location if applicable) is similar between train and test set. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_heatmap_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n  </tbody>\n</table>\n"}}, "53e47d558cc8478e988587c412844906": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "edf5eb2645be4237adbd5db4263140e1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "45125707b7614447bb1f9bf64fac1db0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_78fa092d377b4bfb997ab10c47249a4e", "style": "IPY_MODEL_50d7986d655a48a8a385db326504bc18", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "78fa092d377b4bfb997ab10c47249a4e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "50d7986d655a48a8a385db326504bc18": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c007c989ac0e4f9e82c22d1346d51307": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_d73827eed86d4f11883b5ad002e4a646", "IPY_MODEL_23995e4d3eac4c9280ec12d56796b68a", "IPY_MODEL_e35b50c8aa734f6194f57f2ac2b87f45"], "layout": "IPY_MODEL_6a37ed87062d4915b918fd001b0d6def"}}, "d73827eed86d4f11883b5ad002e4a646": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c9b7b4d703ad478e90fa9aeb862ca9ec", "style": "IPY_MODEL_7451ca62503949acac919691a199b979", "value": "<h4 id=\"ImageDatasetDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Image Dataset Drift</b></h4>"}}, "c9b7b4d703ad478e90fa9aeb862ca9ec": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7451ca62503949acac919691a199b979": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "23995e4d3eac4c9280ec12d56796b68a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6d2309a09d3448f38a981e754374e65d", "style": "IPY_MODEL_83fc367195f04e8e88934664273651b9", "value": "<p>Calculate drift between the entire train and test datasets (based on image properties) using a trained model. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_image_dataset_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "6d2309a09d3448f38a981e754374e65d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "83fc367195f04e8e88934664273651b9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e35b50c8aa734f6194f57f2ac2b87f45": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_38e8e9d2186c4d41a22b6aa25de41b40", "IPY_MODEL_67d052def8bc4c3d97c8772ca63b1ebf", "IPY_MODEL_6988e384f1324d9890a4e8c22e9b25df", "IPY_MODEL_113b09521ed04f0894eade06aa841322", "IPY_MODEL_44743c550deb4f6684ee7200ead7591e", "IPY_MODEL_d1d978fbc0f94a379ac777fb957756a5", "IPY_MODEL_d875a193d5464e63925108f617839b84", "IPY_MODEL_a0bd8d816e6e4ed2894fb303faaeea82", "IPY_MODEL_79e0b514371f4f3080ab87be5c51d55f", "IPY_MODEL_1a86206a91db472c9e703341f4757c4a", "IPY_MODEL_4cdf408552f7478f84443eb76359d90c", "IPY_MODEL_48c9c1aca5444393a4cb82c83bd418d7"], "layout": "IPY_MODEL_bf2d5b1128a5490aaecfb80076493c78"}}, "38e8e9d2186c4d41a22b6aa25de41b40": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bb7da8dfcca743e888409e91324738e0", "style": "IPY_MODEL_6974be690efe4295896003a0584e932b", "value": "<h5><b>Additional Outputs</b></h5>"}}, "bb7da8dfcca743e888409e91324738e0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6974be690efe4295896003a0584e932b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "67d052def8bc4c3d97c8772ca63b1ebf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e44d3de587e5410fbc4b3ed8a155de35", "style": "IPY_MODEL_aa5cf32b1388405c87ac0e6a189b1714", "value": "<div>\n        <span>\n        The shown features are the image properties (brightness, aspect ratio, etc.) that are most important for the\n        domain classifier - the domain_classifier trained to distinguish between the train and test datasets.<br>\n        </span>\n        </div>"}}, "e44d3de587e5410fbc4b3ed8a155de35": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "aa5cf32b1388405c87ac0e6a189b1714": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6988e384f1324d9890a4e8c22e9b25df": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0953b33c1a214b5da16e1cb59d487ee9", "style": "IPY_MODEL_877fd3fde6e14c408f21f2fd37c8f870", "value": "<div>\n    <span>\n    The percents of explained dataset difference are the importance values for the feature calculated\n    using `permutation_importance`.\n    </span><br><br>\n    </div>"}}, "0953b33c1a214b5da16e1cb59d487ee9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "877fd3fde6e14c408f21f2fd37c8f870": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "113b09521ed04f0894eade06aa841322": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#6DB36D"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.1], "y": ["Drift Score"], "type": "bar", "uid": "c02d529f-93ce-4610-9199-aaa4f357fd9a"}, {"base": 0.1, "marker": {"color": "#F9F871"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.03180806277414114], "y": ["Drift Score"], "type": "bar", "uid": "11d12f55-ac14-4b79-98e9-c95cef897823"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"height": 200, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, 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"linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Feature: Mean Red Relative Intensity - Explains 17.38% of dataset difference"}, "xaxis": {"fixedrange": false, "range": [0.3323589162988808, 0.3509516006595665], "title": {"text": "Mean Red Relative Intensity"}}, "yaxis": {"fixedrange": true, "title": {"text": 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"4cdf408552f7478f84443eb76359d90c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cdd2d2aeec574102b56e17bf7a8f1359", "style": "IPY_MODEL_c796737599a04a14b1b0edb6cf8199ad", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "cdd2d2aeec574102b56e17bf7a8f1359": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c796737599a04a14b1b0edb6cf8199ad": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "48c9c1aca5444393a4cb82c83bd418d7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_01528bf969984152b3fb8245a1870906", "style": "IPY_MODEL_55c9c58b54c544ee881d980d59c2f45f", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "01528bf969984152b3fb8245a1870906": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "55c9c58b54c544ee881d980d59c2f45f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bf2d5b1128a5490aaecfb80076493c78": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6a37ed87062d4915b918fd001b0d6def": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "429eaa7d2eb8434fa8cca3ca92b8070d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_eb3aef97757b4a18866a323b5e047075", "style": "IPY_MODEL_3c21cce7d186490193041cb3ecc7ec94", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "eb3aef97757b4a18866a323b5e047075": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3c21cce7d186490193041cb3ecc7ec94": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e8378a1d24bd431d9fd310f4b2a8783a": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_578815caf7004460bf005968bdcbf4af", "IPY_MODEL_ab33f5129c104bcf9128d708f4681ed9", "IPY_MODEL_c1a5817dcad34777b01e6fdcc628d4e6"], "layout": "IPY_MODEL_dc3bcb6e8cc8478cb6fe6cbca91c1496"}}, "578815caf7004460bf005968bdcbf4af": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4e49f4759e0e4bda900ba200cf803a6f", "style": "IPY_MODEL_1b55499dbacf42429fff925e75fa8a71", "value": "<h4 id=\"ImagePropertyOutliers_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Image Property Outliers</b></h4>"}}, "4e49f4759e0e4bda900ba200cf803a6f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1b55499dbacf42429fff925e75fa8a71": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ab33f5129c104bcf9128d708f4681ed9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5fc47c89ea4e459a8324a1dc59d3ad57", "style": "IPY_MODEL_8f99d4aaf4a7455cbb31b51678db9653", "value": "<p>Find outliers images with respect to the given properties. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_image_property_outliers.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "5fc47c89ea4e459a8324a1dc59d3ad57": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8f99d4aaf4a7455cbb31b51678db9653": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c1a5817dcad34777b01e6fdcc628d4e6": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_321fa6de13fe4fc8ac50ef38c3e0e6e0", "IPY_MODEL_37906442c2774a028430790dcc3d84a3", "IPY_MODEL_62f8b53937af4578b353e21bfe7d639b", "IPY_MODEL_88cf0a20d1e04d969a032fa4daf862e3", "IPY_MODEL_20cc2619a89d4d71b2227a55fff8b771"], "layout": "IPY_MODEL_c2260ad5080f4743bb9546fee3493ed6"}}, "321fa6de13fe4fc8ac50ef38c3e0e6e0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1db0e4054f4b455ca888e7676f74ea9b", "style": "IPY_MODEL_ee97ad819eda45f68f5d0a304dece996", "value": "<h5><b>Additional Outputs</b></h5>"}}, "1db0e4054f4b455ca888e7676f74ea9b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ee97ad819eda45f68f5d0a304dece996": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "37906442c2774a028430790dcc3d84a3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2b32295ad9cf475da22c12f9a5d91484", "style": "IPY_MODEL_2cb3053af10848af94257cc3770099c2", "value": "<div>\n<style>\n    .NKISZP-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .NKISZP-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .NKISZP-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .NKISZP-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Brightness\"</b></h5>\n<div>\nTotal number of outliers: 22\n</div>\n<div>\nNon-outliers range: 196.97 to 250.89\n</div>\n<div class=\"NKISZP-container\">\n    <div class=\"NKISZP-row\">\n        <h5 class=\"NKISZP-item\">Brightness</h5>\n        <div class=\"NKISZP-item\">162.01</div><div class=\"NKISZP-item\">167.13</div><div class=\"NKISZP-item\">177.07</div>\n    </div>\n    <div class=\"NKISZP-row\">\n        <h5 class=\"NKISZP-item\">Image</h5>\n        <div class=\"NKISZP-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"NKISZP-item\"><img 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\"/></div><div 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\"/></div>\n    </div>\n</div>\n\n<style>\n    .XOLNQT-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .XOLNQT-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .XOLNQT-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .XOLNQT-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"RMS Contrast\"</b></h5>\n<div>\nTotal number of outliers: 1\n</div>\n<div>\nNon-outliers range: 21.44 to 99.86\n</div>\n<div class=\"XOLNQT-container\">\n    <div class=\"XOLNQT-row\">\n        <h5 class=\"XOLNQT-item\">RMS Contrast</h5>\n        <div class=\"XOLNQT-item\">100.67</div>\n    </div>\n    <div class=\"XOLNQT-row\">\n        <h5 class=\"XOLNQT-item\">Image</h5>\n        <div class=\"XOLNQT-item\"><img src=\"data:image/png;base64,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\"/></div>\n    </div>\n</div>\n\n<style>\n    .TKXSLT-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .TKXSLT-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .TKXSLT-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .TKXSLT-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Mean Red Relative Intensity\"</b></h5>\n<div>\nTotal number of outliers: 55\n</div>\n<div>\nNon-outliers range: 0.33 to 0.34\n</div>\n<div class=\"TKXSLT-container\">\n    <div class=\"TKXSLT-row\">\n        <h5 class=\"TKXSLT-item\">Mean Red Relative Intensity</h5>\n        <div class=\"TKXSLT-item\">0.33</div><div class=\"TKXSLT-item\">0.33</div><div class=\"TKXSLT-item\">0.33</div><div class=\"TKXSLT-item\">0.36</div><div class=\"TKXSLT-item\">0.37</div><div class=\"TKXSLT-item\">0.38</div>\n    </div>\n    <div class=\"TKXSLT-row\">\n        <h5 class=\"TKXSLT-item\">Image</h5>\n        <div class=\"TKXSLT-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"TKXSLT-item\"><img src=\"data:image/png;base64,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class=\"TKXSLT-item\"><img src=\"data:image/png;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA0JCgsKCA0LCgsODg0PEyAVExISEyccHhcgLikxMC4pLSwzOko+MzZGNywtQFdBRkxOUlNSMj5aYVpQYEpRUk//2wBDAQ4ODhMREyYVFSZPNS01T09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT0//wAARCADIAMgDASIAAhEBAxEB/8QAGwABAAIDAQEAAAAAAAAAAAAAAAMGAgQFBwH/xAA7EAABAwMCBAMFBQcEAwAAAAABAAIDBAUREiEGMUFREyJhFHGBkaEjMkKx0QcVJFLB8PEWMzVyU1SS/8QAGQEBAAMBAQAAAAAAAAAAAAAAAAECAwQF/8QAIBEBAQACAwEBAQADAAAAAAAAAAECEQMhMRJBMhMiUf/aAAwDAQACEQMRAD8A9OREQEREBERAREQERVHiXjFtsMkFIwSSYw2QHIB6qLlJ6mTbuXm80too5J53ZLB90czlebXfjq5Vsv8ADFlPCfusAy4epPdcq53e7XuWOkqXBz55AYy7DQuRUw1NDVy0soAkjOl+khw+ap3kncjum9XMPAkucoPMaTkLo0fEV5gka8VLZw3o4bn4jdVmhZPUzNhpHAE8muIC6Ekc1vqdFVpdIRnDHZwlx6Rt6vYL5Fd6bxBhjxs5h5td2/RdheSWi6Ot9U2og8z3+VzQdiP1Xq1LL41NHIebmgn0KnG/lLEqIiugREQEREBERAREQEREBERBBLV00NRDTyzxsmnz4bHOwX454CnXwgEgkDIXIqbPU+1yVdFc6qOR2/hSSF8Wf+udh7lFtiXYRVS532+WpjTU22neOropCdXqAcLSZ+0Olaz7WnkLu2MYVf8AJjvSfiu9xdVVNHw/PNR58XLW7DJwTg/ReTXW+PmohRxRCPWPtnHcvP8AQKx3HjG4XSVkVBH4Z6ZcAD8Dt818ttghDDUSOgqZ5B9q04do9yxyzly20mOppSYGvq43Mec+CMg9gsJYpIQ0kgh24wclXO9MitdA5op4wZstYWtwR71TWsdNM2OIZe44C1xz2zuGnxviMLZMEdiuhSskMTp3gnV5QSsY6WemrI46sDT0GchdV4kk0uewNib9wDqVN6QztMEbrnTwNBI1An1K9coGllFECMEtyfevJY6qeifG2JrWuLg57sb47L1e2zCooIng58vPOVGPqb420RFoqIiICIiAiIgIiICIiAiIgIiIOLxdRCu4dqmDaRjNbD6hUmhtVDcGQGGlY50oAA9eq9MmaXwvaObmkD5LzywmSCeZkYxLT1Lgwe87fmubm6srbj8ZV3B9NCG+PEIdfIxPz9FzpOGXU72vt1z0kcw7Y/RX2aennt8clzY1tRh5YzBB5kbD5Kre2+0zGCgaxzmHE0pHlj9B3Kzz1PF53O1c4ipa6CCEVdR7QDktxny+9cJkDzE+WNh+z3c4dFcLzL+7qYwU2qaouJwXSHJA5bdlt0VnjpLRU0RAdUyR+c9M42AV8ctKWKnbaCevnic0nSXhuSeStVdDTQTin30xM29SsOHbfC2iimkDmVMchDm52yPRbUxDa6aQtDtW26m5bRrTiRvp2skq6k5cMhjO5Vr4GuhmpfCmfu0kFueXY/0VSvtBIJ6SKEDMxOOwKzFG5k1PR2mtHjNyZZQ7ALu2VpbqbUj1prmuGWkEdwcrJeeUdbxBbZA6qZrp2ka3t2Pv9VeaCpNTBqcPM04J7qcOSZdFx02kRFoqIiICIiAiIgIiICIiAiIg+Lzy4sbaOJZ4my7z4e0noRuPorZxHeRaKAviAkqnjEUXV3c/DK84uArbjV0s9YWx1E73Zx+HqufnsvX624pfXYfNV3WUzVL5Io87gHD5P0C3YIWtj8GnjZGACdI2/wAlc9sldFG0GGKocNiWv0n6r6+5VbAC21yh38xcCB8ly/rZz5M3TinRnTHRjY+4/qrxa4m1dQ/WzAERBIzuTtuqza6PRVVFU6F8bpzklx59/crPaa+CkjdHKHAudkvAztj/ACtsNb7Z5+dOLVNfbrhNG5uqNzsl+MbrVDXSPdMPuuOysHETqerDBG4EsB1OHXljfr1XBj/2BodsE1rKyK29OVxjltspXNJzrIyPcuXbIv4OlcXFgMxJcrFxY5jbLBKYWvDZWnDuS4kNdLXRRlkMTND8MjY36radxS9VbuIHa7A5wJbnTt8VYrVI9sFOwR+WRpLnY69FWrkSbAyOU63eXLu5yrXRzsjjpqbBy6PIPTZU4/6Tl43kUc0zIWa5DgZwswQQCDkFdO2b6iIgIiICIiAiIgIiICIiCCekpql7X1FPFI5gIaXtBIzzwqjx5Qx08FFW07GRmKUNIaMZ7fTKuqqv7QY3S2OJrCA7xgQT7is+WT5tacdv1Ghbm009Qz2p/hw4Jc7ON13W0VlqRpgnDSNzol6fFVaEuEEbCeTRq9VuUVO+rnEEJaHEEjV6Ljxz11rboyx/dsnNHivYJMtaSAe6kh8MysZJkMyNTscgtYbSFvItOCpGu3KpL2WN2+mGKZ5gcHNc0HDTnBXEw5lNqY3cDOlbU7sMDcZJWULcxgc3d10Y3vbHKfjmcR1LJeGgGtD9ZaAM/dPdcujq6a30bqC3ubUVEjNdRVAeWIfyhSV5Nnq5mTMMtNURuAONgSudZqbwbJWXGeU+zF4Z7ONjKff2C0w8VyXB0ntFkpfKcvDcD4q40UUZhp5XAeI1mGnKpr5QY7UIsAS6dPpsr1FCGMjDsOdGMBx5+qjjndRk+zRxys0SgEE7ZON1kxrWNDWjAGwCiqmRSRtZM4ty4aSDg56L4XkvfTuDgwM3kz3W29VVsIvjdmgZztz7r6rIEREBERAREQEREBEWMj2Rsc+Rwa1oySTgAID9Wh3h414OnVyz6qoccVLnR0VG8jW4h72t5Z9D813p7/bII9ftTZM8hH5iqdfrhHdLzTzRxuYxkWwdzPP9VhzZz51K34sLvdj4HDQpI5XRua+Nxa4dQcELXBGFmwjkuJ0pmHclSZxuoWnBUmctUxWpDjQ0nvhSRjSdI3J7KPI8IA912bbJS01EJA1ktYXHSzbV2AH5rXHvplertVOKYJZ7FKyJhc6J4kIxvp6qu2qyyXGmZMy5U+AceASdQ+CvjhLvLJGQJCWnLSAc8x9VXKO2UVBxpDFSOfpERkLH/hd6HqFfDLURnjt06gRxXS00bQQIzz+C9BXn9awv4ioXn7jSd/VegDfkteG72zzmtIK2ndUQBjHBrg4OBKwmbPJbnNka3xcbgH1W2tavBNFKGgk4Gw7ZWmU9qkpQMljpw2XHPy752Wytag1+yMD2lpG2/VbKY+QvoiIrIEREBERAREQFUOMq6UVEVC0lsRYHvwfvHO3ywrcSAMnkvM7vcH3G4PqHjSPutbnOkDp/X4rHny1jptwY7y2hJOQPwrCPW+d739MNb7l8DiWH0UjTsuJ22Jw7LcLNriSB2ULOSzbzUI0n2ypGuICgBUmdkiticO8oyt60vY26QahqaXYHoehXNadlYbDbi1ra2oDC0t1Rtxkj1/vuteOW5dM89Sdt6/GIW0+ICSXDR6O/xlUgxvdxm2oaNmUoJ/JWa9TQ1lNHVQTPcxj/AAywggZxnO/VcOneyaeedg3GmPPu3/qtOTLeSmE1i3HsEjSBjJOoH1Xes1ybVs8BwxJG3vzxzWlQPt3szW1TPtWuJzpO/wAlzql7aG4iroXu8MnckdUwvz3tXKfXS5qCqd4MTp2NaXtGASOmVBBc6eSmbK9+knYgAndTT1DW0Tp48PGNuxzsum5SzqsdWUo6n2mMkt0lpwd1sLStcjJIDpjaxwOHaRgE91uphdwvoiIrIEREBERARF8JABJOAEHE4vq20tilaT5pyIm+bB35/QLz3UAulxNef3tcfsj/AA0OWxbYz3J+PL0+K5TXbcsri5cvrLp6HDh849pmO5jupWHyrXYcv32UjHbYWTRMwlTNctdndSB2+FGhOCpQ7I2KhY4tcHDmDkZC262tfWStlMccTmt0jwxge9T0rfWxFA+BsVRVUxNPIdOXeXII5g9NtwfRXNngU9KNJayFjdjnYBalNJTXK3QSVMTejtMhBwRtlTXKOKW3yxyPaxunIJOMEbhdmGHxLY488vqyVWa+X2iomLJGmASEtaCB054/quHw3IZaGpkznVUO+S6DXj7TUMDw3Z9NlyuEsCzSAHP27t1ze9tvOlnZTao4TDNEZH82F4BBzsvlXSGB7qeVzHZGTpOVrtOCD2UssxnnfK4AF5yQE60rqvtA0wgFxGWHbK2n1M0rS10riw/h6LV5sIUAbJENnEgqZVdLHb6d7CyaJ+IpBlzXbnr6LpLk0dya1kMUuGjBDnuPyXSgmjqIhJE7U09V18dmumOW99pERFdUREQEREBQ1c9PTUsk1W9rIWty8u5YUyr3GkVG+yOlrJJWeE4GMR/iedgCOWPyUZXU2tjN5SKHdH0T7nO+3NLaYu+zBGNsb7dBnOPRa7HYO6hZuck7qQHdcF7elOppOCCdlm04KgAUgPLCqlO0lZg+YKIO7BSA5IRDYaVnlQtKkGSNkQla3I2A+Sn8R8mA57naeWok4Wuz0OCpIyQ4jCKvsr/Cp53HpE78lo8Kf8KSBjVKSukMFu4B9FMwRho8INDOgaMBXnjK+s2nKkA3UTdlK0qCpBkhZg5G6wGxGDzX3ODtupUfXgvbjsu7ZITFQhxdnxDqxnkuCe4K7tj1eyOJPlLzgdu624v6Z5+OkiIulkIiICIiAtavoobhRy0tQ0OZI3G4zg9CPULZRCXTzF/CV0bdhRNiLoycipx5NPc+vp/lal6tclmuHsskzZcsDw5oxkHI5dNwVcH36pHHItbyG0oGhrWt3c4sDgSe25+i4v7QZXG8Qx4bhsAION93HmevL81zZ4Y6tjtwzzuUl/4rudlmzkoQTspQcYWDo2macKRihBUgO4UITx8lNGVAxSsUISt55UjTh6iB2ClbzRVI3dyjilDKySn6FokaPzUg2KgezTc4p+jmFitGd9dAHIUjCoAcKRpSelTArILAFfcqVGeV2rAxwgkeSdLnYA6bdVwtWysFhZI2h1PPle4lg7BbcU/2U5P5dNERdLAREQEREBERBXbzw46qucd1ttQ2mrWHJLm6mvIGB7u3uVBvjq8XeojurtVU1w1EHLcYyNPpg/2V7AqH+0SlibUUlYJB4r2mIszuQMkHHbcj4hY8uM1uOjh5L9aqpZzjHRSDmFCx26kad1zV2Jgd1KDyPZQNKmadlCE7e/RSsORlQMOGKWN+W4UITN3CkGQo2DZSAqFUgOVHW5NIHt5xvDvgvoO6yOCdJ+6eatFama7YZ6qUOAIWu2QPw8dNipR3RCbJKyB7qJrl9ypVZPdjkrhQwGmo4oScljcE+qqNJRz184ZC06QfM8jZv99ldV08M/WPLfx9REWzIREQEREBERAXMutit92kikrIS58fJzXaSR/Ke4XTRRZL6mWzuKjxjYaT92ur6SGOGWnGXBgDQ5md8gdR0VEHNet3mjfcLRU0kbmtfLGWgu5ZVAm4RvUJbinZNn/xSA49+cLDlwu9yOrhzmtZVyGAkhrQXOOwA5k9lbKXg2rdKRUVEbItOWuZkuJ6ZB5Lr8KWd1Db819JE2p8UuaSGuc0YAG/TkevVWFThxTW6ryc13rF5lcrbUWqp8GpALTuyQfdeP76LXY4Y2XqM0EM7Q2eJkjQcgPaCAfiq9e+GjVTsmtwhiJGl7CNI/7DH5KufDZ3E4c8vWSqMdjmpA5dm78Ny0rBNRB0sYb5283NwOfqFwwscsbjdVpMplNxKO6yG5x0WIIwsmuwMKBG0llWW/hkGfitph2wVA5jXkE8wchbMUMs2fBikkLeehpOPkpQ+g7LKMOllZEwZc9waB3JXUpeH6mRzTUPbHGRnynLvdghdi3WenoZPFaXSS4wHO6e4LXHiyvrPLkkbNFSRUUAihbgcyTzce5WwiLqnTmEREBERAREQEREBERAREQEREBERBi4kNJAyQNh3VZuljjfb3VlNSzRVJw90AdqxnmPf12RFXPGWdrY5WXpxKO21lbHK+niLvCIDhnBJ7DPbqutS8MVD2vNTK2J2BoDfNv6/REWOHFjZutc+TKXUSf6Xn/9uL/4P6rsWe2m3QPa6Rsj3uyXBuNugRFrOPHG7jO55WarooiK6giIgIiICIiD/9k=\"/></div>\n    </div>\n</div>\n\n<style>\n    .IXQGIC-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .IXQGIC-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .IXQGIC-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .IXQGIC-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Mean Green Relative Intensity\"</b></h5>\n<div>\nTotal number of outliers: 67\n</div>\n<div>\nNon-outliers range: 0.33 to 0.34\n</div>\n<div class=\"IXQGIC-container\">\n    <div class=\"IXQGIC-row\">\n        <h5 class=\"IXQGIC-item\">Mean Green Relative Intensity</h5>\n        <div class=\"IXQGIC-item\">0.33</div><div class=\"IXQGIC-item\">0.33</div><div class=\"IXQGIC-item\">0.33</div><div class=\"IXQGIC-item\">0.34</div><div class=\"IXQGIC-item\">0.34</div><div class=\"IXQGIC-item\">0.34</div>\n    </div>\n    <div class=\"IXQGIC-row\">\n        <h5 class=\"IXQGIC-item\">Image</h5>\n        <div class=\"IXQGIC-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"IXQGIC-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"IXQGIC-item\"><img src=\"data:image/png;base64,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\"/></div>\n    </div>\n</div>\n\n<style>\n    .NHXPWB-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .NHXPWB-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .NHXPWB-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .NHXPWB-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Mean Blue Relative Intensity\"</b></h5>\n<div>\nTotal number of outliers: 38\n</div>\n<div>\nNon-outliers range: 0.32 to 0.34\n</div>\n<div class=\"NHXPWB-container\">\n    <div class=\"NHXPWB-row\">\n        <h5 class=\"NHXPWB-item\">Mean Blue Relative Intensity</h5>\n        <div class=\"NHXPWB-item\">0.28</div><div class=\"NHXPWB-item\">0.3</div><div class=\"NHXPWB-item\">0.31</div><div class=\"NHXPWB-item\">0.34</div>\n    </div>\n    <div class=\"NHXPWB-row\">\n        <h5 class=\"NHXPWB-item\">Image</h5>\n        <div class=\"NHXPWB-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"NHXPWB-item\"><img 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src=\"data:image/png;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA0JCgsKCA0LCgsODg0PEyAVExISEyccHhcgLikxMC4pLSwzOko+MzZGNywtQFdBRkxOUlNSMj5aYVpQYEpRUk//2wBDAQ4ODhMREyYVFSZPNS01T09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT0//wAARCADIAMgDASIAAhEBAxEB/8QAGwABAAIDAQEAAAAAAAAAAAAAAAUGAwQHAgH/xAA4EAABBAIABAUBBQYGAwAAAAABAAIDBAURBhIhQRMiMVFhcRQygZHRByNCYqHBFRYkM1LwQ7Gy/8QAFwEBAQEBAAAAAAAAAAAAAAAAAAECA//EABsRAQEBAQEBAQEAAAAAAAAAAAABESECMUES/9oADAMBAAIRAxEAPwDpyIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIonIcR4vG2zWtzlsgAJAaTrfptM01LIvLHtkY17HBzXDYIOwQvSAiIgIscczJJJI275oiA7p7jf91kQEREBERAREQEREBERAREQEURkuJcRjJvBt3GiXeixoLiPrr0WHiXLCtw3JbpWAHTANhlYQeru4+iuVNidRc2ocYZPE5OKpmXOlg0GuLm+cb6h3v8Amr1Nl8fFTfaNmN8bGc55HBx19PVL5sJdbzhtpAJG+47LlEXE+Uxmdk8S1NJCJSJGyHm5hv576CzcS8ZT27BGOvSRVC3Qawcrt9yT6/gqu+88sOpXSOcdu5m+q1OM267jQvV8hTZaqv543jp7g+x9iuZ5aE5j9oElOR5aHTeGT/KB+ihK2ev12uFK5JAXa5gw65lnxWX+zcRVsjkHvlDX8z5NeY9uvuk4Xq4YLI2eGck7CZou+yyO/wBJYP3fpv8AEfRXgEEbCicrjKHEmKY1zw5pHPBOzqWH3H6KJ4fys2MuHA5olkzT/p5XHyyN7aP/AH2Wb1qcW1fCdAk9kUfmLMscMdasxzrFp3I3R1yj+J2/gKK+4twnfatskZJDPIDE5vsGhvX52CpBYKdWGlVZXrMDImDQAWdAREQEREBERAREQEREBERBzjjvCxNzle10jiu7jc/0DZOxP1/VVa1LexwOKtlwijk8RsZ6gHWtg+xC6rxdi/8AFcBPCwAzMHiRnXXmHsuZzufl/sTZHDxvDLOZ3u3pr+i6S8c7Mqx8QVoOJ+F481V5fttVnLKxh35QeoI9+4VBEs0JZJFK4OHspKlPextt76kr4Ht6O0dfmtKRjXveA4Eu6gjsVLxXnxhI/b2NkDvMddNe6xSOYX88bdN30aT2XyMNa4c/Nyk+cD1/BTNzE1BVZcpWPtNf0cz7r2H5SdEdZfSNZghjcJB8+n6ry+J7oz25Wc4+Qvlqo+uyPnIDnDZH/H4KySPDBX5/Mx0WnAHXTZUpFp/ZnmLEWTdjDzvrzNLg31DHDur3xFgYM3T5XajsR9YpR6g+30VA4OvYfHZvx5PEhge3liMrtljtaJJHYjYXTJMlRjaHPtwAOIA84O9nQ6fipWorfDWfsQ23YPOgstxdI5Hf+Qduv91M4zd3I2Mi8aY3deEE9QGuPMfxIH5KP4zxVS9QaQGx5B0jW1nt6Oc/sN+2vyWLhfNCuP8AA8uBWv1/K3mOhKOxBUVa0XxfUBEXlzg0tBPVx0P/AH/ZB6REQEXiSWOJvNK9rB7uICr9zi+g2f7Li2uyNwnQih9Pxd6IJ21ar1IvFszMiZvW3HXVFD4zF3bE7chnpGSWB1igYPJD+p+UQTyIiAiL4SANnoEBccyRbT4nvsphvLHK4s67Ddq3cTcWtLJ6ONcQR5XWGnp8gfquZzzuEr5Gkku7BanGL1t2ZHWHyNjJ03q7fq4rVotmNvUcTndPMC30HusdR9gWNwvDXPGtuK2ZYpam5n2nGd3Qhp6a+Sn1cZLNdkU3KdeG/pv+bsvLZ4YqU8ZY/wAZ/lAJ6Ad1lY91ym4Pa0vHotSRx8spALwfMCNgke6S4WD7FmOkyORnNC7ZjL29PnR7rPJA+eKq1g5pDF5GgevUrJk85YydcMuRxjwxywtiZysb79FNcDiN/EuLbIznH2eRzdj0I31S1Iq3IyOXUoe+MdgdHa2H24qs3NjGTwgs83jkOdv4OugVv444dNLLsytZgFSV25A1vSN47n4Koc80k875XuL3uOySlo6jwbZky08WQvzPaKldsMIkP+4Tvmfs+vopjifh+vnanjwO5L0I3BM09x/Cfg/0XOeFZ8QWyQ5exZhjaOZhbIRr40rdi6fC2RfyUsrbLiQAx05YST7AhZae+FeKbBbLQzkUkcsA2Jiw9QOx+fb3W3Z47x0A5jTvuZsgO8HQOvklbTeEMY1rwJbe3kEuM2z0UxWqQ1qzK7AXRsGhznmP9UVUY/2iVZZ+SLHWZGE6AZ5nn8PT+qz/AOdLLmtMfDeScdeYFmuvx0VsbFG07bG0fRoC9IK1S4jyl2UxN4dswHlJDpXgDawSwca3ZHD7TRoRPP8AB53NH10rYvqCtQcIxSyeJm71jJu1rllcWsB+gKm6WPp0IvDpVo4W/wArfX6lbSICIiAiL4TobKATobKo3FHFni+Lj8W7Uf3ZrI9u4b+qx8V8Tm09+NxcpbE3pPYb3/laonAYOXOWeXrHTiP7yQD1/lHuVuTO1i3eRBThxqmfyxxg8rGk9Xn+60xX58fLaDwx0bwN79d9gpbjKww5t9StB4daiPAibr1194/PXar8cga9jpB4kfNsx71tLYuMgrTGu2XlcIydNJ7lY3seDp7iT7bUlfyQtcscW2xMHlBGlHOelPLaxsoY8850NhebQ/fzA+Uc2unuvlGsbU7YgT12Tr2HVXvEcC0chiHWzffJJYbtnKBysPse5P5LOLVGgdL9js1fBDyS0g62WEHt9Vc/2c0pZcq+zaryRmnD4bOZhbou+vxtbfADK9DJX8ZbjjbdjIAc7W3AE+n9PyV+AA9O6t4kU/iniaSOy/DYqn9rta/fAt21rfbXcqBx3BT7j32sy6vSY075ItDmHyrbneEq+Wui7DcsUrRAa6SE/eA9Fps4Ax3LqbIZOXfrzTAb/IKVeoGrw5h6WXYbckF2pM/Q5JCBEfn49FfauHxdR7JKtCtE9n3XMjAI/FQbuAcOI3CCS3E8jQeJd6/DutKPIZnhF4hy0b72Jb5Y7LB54+vTm91BeEWrj79XJU2W6UrZYXjoR2+D8raRRERAREQEREBERB8VE4u4iktPfisW9wYCWTyt9XH/AIt/uVvcY8RMrRPxdKQm3KOV7mH/AGwe2/dU3xYWlteHetBksrR91vcN9yt+fP6x6v42+H8BNmLDYwDFShP7x47n2B7ldPqVYKVZletG2OKMaa0KnDP2BDDS4cxkscDW6aXR7JPx6j8VIC3xPWoySWq1V3I0vLuYbAA36A6S7SZHPeLbDL+SmtwkNjkeeWMDWgPUn5J6qDiMRYNxO6E8zwfX2Guy3buYltN5DFG2Pr91vUrZpX6MeAt03sHjv6sdy/f+PjSuQ2oc6B3rXsPZfCPj1TflAT0OuyxXTGenamo2Gz19CRoI6jY0Roq98EZt2OdBRuaZVu+eGQjWn+hG/wAAqNjqptXIYQ1zmyStjPKOo2dbXao8DQZQo0zHzR0XB0ROtkj1J+vqflXmMWXUFxbg7gylTO4SHxLcLx4kY/iHY6/MFWulLLPThlnhMMr2Bz4yd8h7jayvaHMc0k6I10Vd4WtSQWbuFsOBfUeTETvbmE+6n2L8qyIiKKLxLGyaN0crGvY4ac1w2CF7RBTbfC9/E3H5DhSw2IuO30pP9t/09lZMTbs3KYkuUn1JgdOjcdj6g+y3l8QfUREBERAREQEREFVPBNaVs77FuWSzM7mMoaBob2enufdTNDCY7Hwtjr1Y9hoaXuaC531KkUV/qpkeWtaxoa0BrR6ADQWHIMZJj7LJH8jHRPDna3ygg7OlsLTyxIxFwj1EEn/yVFcRs4uWGoLkB8SuSQHa+ddVpsaDvQ0O5W7WyFs46XGM5XCTqNn7vcr5LJUFOFkBc4jq4O6ebv8AguljMaY0R6aRw0fhHb7oeoJ66A7LDUT3BEr28T0omD70myfjRXZx6Kh8A8LPrGvnLE+nSQkRwhvoHdyfor6lSPEYkDNSua52z1a3Q+O5Ve4mifQu1c/B6wERTtI2DG46J9+m1ZFhuV47dSatKNslYWO+hUV7ikZNE2WJwcx421w7he1X+E7Uja02JtvH2ug8x67uj/hd9OysCAiIgIiICIiAiIgIiICIiAiIgLVycYlxlqMvDA+F7eY+g209VtLRzcrIMLdklOmiB+z9QR/dBxG/QkpOZJ0fA77krD0ctRo24nQHfXsth9qc1G1TI7wmHfJ22ss9eFz60dV3O+SMbAP8Xst1mNP0Z1J9dhTfCUc17KsxrYw6KZ25SRvTR6n+34rRweNtZfLxVIYy4lw8Tp0a3fUldc4d4YpYESyRASWJT5pCNab2AHYLPxam42NjjaxgDWtGgB2C9IiiiIiDQfSqVslLl5HiJ/geHI4kBvKDvZ/JbzXBzQ5pBaRsEehXixDHZgfBMwPjkaWuae4Ki8NDPjZnYuU+JCGeJBIGkabvRaT7jY/NBMIiICIiAiIgIiICIiAiIgIiICqnHmbqUcRNj3O5rNpha1g/hHuVa1yzj7h65BlJMnH4k1ew7bnDqWO7N17eysSqS4kOIcD07FSOMx+Ts8tnHUbEwjf96NhcAVtUcBlc3mTC+NzJXHmmkkboMHuf0XXcFiIMLi46VfqG7c5x9XOPqVbUzWlwpgxiKLpJo2tu2Tzz66gH2H/fVTyIstCIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAtO3bMdiCtA0PmlO9HoGsH3iT+K2nAlpDTykjodb0sVWs2sw+YvkfoySO1zPOtbOkGZfURAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERB//2Q==\"/></div>\n    </div>\n</div>\n</div>"}}, "2b32295ad9cf475da22c12f9a5d91484": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2cb3053af10848af94257cc3770099c2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "62f8b53937af4578b353e21bfe7d639b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1c6318bbd47f4d5ea85a43963c211a73", "style": "IPY_MODEL_0b5dac60dfa2429ca1e615be378a2fdb", "value": "<div><h5><b>Properties With No Outliers Found</h5></b></div>"}}, "1c6318bbd47f4d5ea85a43963c211a73": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0b5dac60dfa2429ca1e615be378a2fdb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "88cf0a20d1e04d969a032fa4daf862e3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b9236b4f31314e62882ad65f17745815", "style": "IPY_MODEL_15b1dac8c4914f51b02d3c28d7c9fd3a", "value": "<style type=\"text/css\">\n#T_28412 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_28412 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_28412 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_28412 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_28412 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_28412\">\n  <thead>\n    <tr>\n      <th id=\"T_28412_level0_col0\" class=\"col_heading level0 col0\" >More Info</th>\n      <th id=\"T_28412_level0_col1\" class=\"col_heading level0 col1\" >Properties</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_28412_row0_col0\" class=\"data row0 col0\" >No outliers found.</td>\n      <td id=\"T_28412_row0_col1\" class=\"data row0 col1\" >Aspect Ratio, Area</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "b9236b4f31314e62882ad65f17745815": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "15b1dac8c4914f51b02d3c28d7c9fd3a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "20cc2619a89d4d71b2227a55fff8b771": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_41c36f880cfc45f5a6eb2c98e989f767", "style": "IPY_MODEL_793276c1ba7b47a5846d8567f21820ff", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "41c36f880cfc45f5a6eb2c98e989f767": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "793276c1ba7b47a5846d8567f21820ff": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c2260ad5080f4743bb9546fee3493ed6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dc3bcb6e8cc8478cb6fe6cbca91c1496": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5a4b8eeb6baa46a6b7a7e27b0aee8dd1": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_a105a329f6974925a03379b83b9fbc6d", "IPY_MODEL_98223860beae4c14ae261430d4ffa8b7", "IPY_MODEL_cff56b1f39e54de2baef6d5486623b95"], "layout": "IPY_MODEL_5f32b23e407a407aa23cbb09494b7d79"}}, "a105a329f6974925a03379b83b9fbc6d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ae2813ba77de401e86e41824fd03ce84", "style": "IPY_MODEL_ea24ac3439854e8fb27f0787f43badae", "value": "<h4 id=\"ImagePropertyOutliers_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Image Property Outliers</b></h4>"}}, "ae2813ba77de401e86e41824fd03ce84": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ea24ac3439854e8fb27f0787f43badae": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "98223860beae4c14ae261430d4ffa8b7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2b2f4c3c622a4bcdaa911510af9d5a1b", "style": "IPY_MODEL_efe10d8abeda4e5f889be659e2d9a4b7", "value": "<p>Find outliers images with respect to the given properties. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_image_property_outliers.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "2b2f4c3c622a4bcdaa911510af9d5a1b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "efe10d8abeda4e5f889be659e2d9a4b7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cff56b1f39e54de2baef6d5486623b95": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_2ed1885fcad944d180ef8198366100c9", "IPY_MODEL_fa5540cc7bde44219f18c51a8ff7a1d4", "IPY_MODEL_3b06f43682bb41118923061ab7e86832", "IPY_MODEL_7c659a69b86d4b25934d70c60d466187", "IPY_MODEL_1712ce0a14b24c979b6de3ff7d1f0885", "IPY_MODEL_3613bfe0b24b4379bf81601706426658"], "layout": "IPY_MODEL_c6105265610342ad955e901ff0f08fa2"}}, "2ed1885fcad944d180ef8198366100c9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ddc8017361674da6a410c02e054bbcef", "style": "IPY_MODEL_256ad809847b4a079e9a096d8dfa97cd", "value": "<h5><b>Additional Outputs</b></h5>"}}, "ddc8017361674da6a410c02e054bbcef": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "256ad809847b4a079e9a096d8dfa97cd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fa5540cc7bde44219f18c51a8ff7a1d4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ddd12ca479564b75af13c5f4b4ab20be", "style": "IPY_MODEL_fcca8110e30b499dbc39bcdaf04bbc4f", "value": "<div>\n<style>\n    .LRDHSN-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .LRDHSN-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .LRDHSN-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .LRDHSN-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Brightness\"</b></h5>\n<div>\nTotal number of outliers: 151\n</div>\n<div>\nNon-outliers range: 198.12 to 250.96\n</div>\n<div class=\"LRDHSN-container\">\n    <div class=\"LRDHSN-row\">\n        <h5 class=\"LRDHSN-item\">Brightness</h5>\n        <div class=\"LRDHSN-item\">150.74</div><div class=\"LRDHSN-item\">163.03</div><div class=\"LRDHSN-item\">161.36</div>\n    </div>\n    <div class=\"LRDHSN-row\">\n        <h5 class=\"LRDHSN-item\">Image</h5>\n        <div class=\"LRDHSN-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"LRDHSN-item\"><img 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\"/></div><div class=\"LRDHSN-item\"><img src=\"data:image/png;base64,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\"/></div>\n    </div>\n</div>\n\n<style>\n    .ODRCXC-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .ODRCXC-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .ODRCXC-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .ODRCXC-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"RMS Contrast\"</b></h5>\n<div>\nTotal number of outliers: 5\n</div>\n<div>\nNon-outliers range: 17.45 to 100.08\n</div>\n<div class=\"ODRCXC-container\">\n    <div class=\"ODRCXC-row\">\n        <h5 class=\"ODRCXC-item\">RMS Contrast</h5>\n        <div class=\"ODRCXC-item\">101.73</div><div class=\"ODRCXC-item\">101.89</div><div class=\"ODRCXC-item\">101.91</div>\n    </div>\n    <div class=\"ODRCXC-row\">\n        <h5 class=\"ODRCXC-item\">Image</h5>\n        <div class=\"ODRCXC-item\"><img src=\"data:image/png;base64,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class=\"ODRCXC-item\"><img 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NLGA3xJHOw0kgZOe+/8AvZarcH1yqO72SyW+zU2ihj3cPNKcFz/TJC2S59031zSUdkipri2aSaDyN0DJc0DYnJA9vosyLqy/Vseu32LUAd3EOIx29PZUTRzmsaXOcGgcknCKJU9nvt7pyzqGs8GmcARBC1ocSNxnbARBL1Eet+no62jkuNNE41cQGoD+tg529d8/RS5eII30Vev4lbBTTE/maUBjiT+tvY/4WP1z03Jd4GVdEzVVxDSWjHnbz+2/3WpvtFUdK3aO52x/8mZ5yzGGt76DvuOfjHqpvbLjTXSiZVUj9THcg8tPcEeqDhOHMLmOHysd3Jxwug9f9NNpy24UETtMjj4zRk6XHcH2Hb7KCxsBDmnnGyyMM5zsr0ZOlJYizCowcBUZDvM3V3HKtFescAV67Y57HhBSqhuMj6hUkZOFXjSNjzypRQdiVW1+SB3VDsYyDuqQ4h2UGa5gazPdHnMTdv0ndeCQPj5wQPurOo5x/pUwCHHzAbZVOcODjwsluTEcDzN5AWO4bZCDzYnI+iymzOi0uA4CwgTlX5T5APZUYcshe8uJ3JXsbsbFWzsdwgWr0Z1NWGlqo54w0vjcHDUMjIOeF1vo3qn+PslhmphFNEM/ywdGnt8FcXAydl1P8Lbc6KhqK95ePEd4bRnYgckj5KCfIiICIiDEudDHcbfNSS/plbjPoexXOqKtrejL7JS1QEkDsGQM4eMbOaT3G4+4XUFpuoun6e+Uul2mKobjRNoyRvx8INoDDV0wILZIZmZBByHNI/sQuX9X9Iy2uV1Zb2ufRnc43MXsfb3/ANO36Ku9RRXOaxXOUjRlkQe4eRzSctzn7cqdyxRzxOilY18bxhzXDIITBwCQgx4k/V2WNup91X0TPDLLWWxjZKcnPhN2czPYDuOMY3UH0+G9zJWYOcfBWRYHr3VxpyMHb0Xj4iDkbhViNwaCU0GtxzyvCcfC9z6qgnVuUFJ3XgHbhekZ4Xnv3CoqY7DsZ2V0AE7lWc5GVUCTyoM1rhCBJHzjcFY5OW59VQ7IAOdj29F7GdjlBQ3aT2Vyc8eypaMyAquo2duM7J6MYjVvpQNaechenJ4VQGOSgu0zGCZo05yRyu9WukhordDBTxeFG1uQ0kkjO+5O/wB1w+005rLpS0odp8aVserGcZOMrvETDHExjnueWtALncu9yrBWiIqCIiAiIggP4i2UYZd6dnmJDJ/fs137Y+3ot70bfP4zacTH/wBVTYjl/wCrbZ313W8qII6mnkgmaHRyNLXA9wVy250lR0f1NHNTFxgc7XHgnD2Z3YfUj69ig6uon1X0pbq6mkrGmOkkiYXOcGgNdxz9v3W3Z1BbHWltxNS1sJHBPmzj9OPVc+6i6qqLtLLFTyvioeBHsC/jnHuOFKIx4Qj2zlUSuAH9gpZ0x0rU3l8dXVMEdADg5JDpB/0/XutzdPw7hnlL7fUiIZ2ZICcDA7/Of2WcVzZrW6ck5PoqHMx2W3uFguVqkLKqnew9iBkH4PB5Wv8ADcD5uFUYwblUkbrJMXcLzRvugsAd1UBhXNG+COV4RjlB6WtMeQd1THyQqSSPhIv+ZygqwQ8AeqqqCQ/C9IHiD5XlQcSZRVsOA4GF5jKai4+yqYx0kga0EuJwAERKOgLS+4X1k5DhFSlspcCBgg7D3zj/AHv19aTpSxssdpZCc+PIA+c5yNWO3xwt2tAiIgIiICIiAtferTT3igkpp2tDnNwyTTksPqFsEQchufSd8ppXQsppZog4hj426tQHfAzj4K3/AE10K+KSKsuxALHZFPgHj/7Hj6f+FP0QUsY2NjWMaGtaMAAYACqREHhAIIIyDyFpLn0parlI2SSIxOaA3+ThoIz6Y9+VvEQQWX8O4y0+FcCDnbVHnbf0Px+6j126SudtYZZIBLEOZITqAG/I5HHx7rra8UwcIMIBwRj5VuSEEeXkLp9+6KguNRJUUcwpnvGSzTlpdnn27/X6rBZ+H7m0TR+eb+YJy7+XlnwO6mDmj4y3nhUM8rx8rcXq2VVrq3wVkRaRwQDpcPUEjce6025OfQorKx5gcd1aqR5lkMIcAqvyc9XVNip4nPc4gBrRkkqT6MGNjnkNaCSdgAF0/ofpB9A9tyukRbUD/lROwdOw8x9DyszpDpCG1wx1VwgY6vB1DJz4f0zjPv8ACly1IgiIqCIiAiIgIiICIiAiIgIiICIiAiIgIiIMK6Wuiu1P4FdA2Ro3aeC045BWkb0JY2vefClIc3ABf+k5zkH9lKETByfq/pqS2XAy0UDvybw0Nx/S45GP2JUq6AtE9voZaitgdFPKcAPZhwb85zv6YClyKYCIioIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIg/9k=\"/></div><div class=\"ODRCXC-item\"><img src=\"data:image/png;base64,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\"/></div>\n    </div>\n</div>\n\n<style>\n    .PSJBFJ-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .PSJBFJ-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .PSJBFJ-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .PSJBFJ-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Mean Red Relative Intensity\"</b></h5>\n<div>\nTotal number of outliers: 457\n</div>\n<div>\nNon-outliers range: 0.33 to 0.34\n</div>\n<div class=\"PSJBFJ-container\">\n    <div class=\"PSJBFJ-row\">\n        <h5 class=\"PSJBFJ-item\">Mean Red Relative Intensity</h5>\n        <div class=\"PSJBFJ-item\">0.32</div><div class=\"PSJBFJ-item\">0.33</div><div class=\"PSJBFJ-item\">0.33</div><div class=\"PSJBFJ-item\">0.4</div><div class=\"PSJBFJ-item\">0.4</div><div class=\"PSJBFJ-item\">0.4</div>\n    </div>\n    <div class=\"PSJBFJ-row\">\n        <h5 class=\"PSJBFJ-item\">Image</h5>\n        <div class=\"PSJBFJ-item\"><img src=\"data:image/png;base64,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\"/></div><div 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\"/></div><div class=\"PSJBFJ-item\"><img src=\"data:image/png;base64,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\"/></div>\n    </div>\n</div>\n\n<style>\n    .MNBVWR-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .MNBVWR-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .MNBVWR-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .MNBVWR-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Mean Green Relative Intensity\"</b></h5>\n<div>\nTotal number of outliers: 239\n</div>\n<div>\nNon-outliers range: 0.33 to 0.34\n</div>\n<div class=\"MNBVWR-container\">\n    <div class=\"MNBVWR-row\">\n        <h5 class=\"MNBVWR-item\">Mean Green Relative Intensity</h5>\n        <div class=\"MNBVWR-item\">0.32</div><div class=\"MNBVWR-item\">0.32</div><div class=\"MNBVWR-item\">0.32</div><div class=\"MNBVWR-item\">0.34</div><div class=\"MNBVWR-item\">0.34</div><div class=\"MNBVWR-item\">0.34</div>\n    </div>\n    <div class=\"MNBVWR-row\">\n        <h5 class=\"MNBVWR-item\">Image</h5>\n        <div class=\"MNBVWR-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"MNBVWR-item\"><img 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src=\"data:image/png;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA0JCgsKCA0LCgsODg0PEyAVExISEyccHhcgLikxMC4pLSwzOko+MzZGNywtQFdBRkxOUlNSMj5aYVpQYEpRUk//2wBDAQ4ODhMREyYVFSZPNS01T09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT0//wAARCADIAMgDASIAAhEBAxEB/8QAHAABAAEFAQEAAAAAAAAAAAAAAAYCAwQFBwEI/8QAOhAAAQQBAgQEBQIFAwMFAAAAAQACAxEEBSEGEjFBE1FhcQciMoGRFKEVI0KxwVLR8SQmsjNDYpLw/8QAFgEBAQEAAAAAAAAAAAAAAAAAAAEC/8QAHBEBAQEAAgMBAAAAAAAAAAAAAAERIVECEjFB/9oADAMBAAIRAxEAPwDpyIiAiIgIiICIiArc8zMeB80rg1jGlziewCuKD8e660RjS8Sb5jf6gtPQdm/7+wQaLijiaXWCYY28mKx1saR811Vk/n8refDTJccfMxfmLWuEgN7C9vL081z95PTe1PvhphnwcvPd0JETd/Ymx/8AX91BO0RFQREQEREBERAREQEREBERAREQEREBERARFi6lmx6fgTZUpHLG0mvM9gg1XFuufwfBayJpORkAhh7NqrPvuFyuSV8krpZnue9xLnOcbJ9z3WVq2pyarqMuXLs55sNG4b2AWATRd3pZtFBJe4Btkk0AO5XZOF9OOl6FBjPAEm73/Jymyb3HmBQ+y5Tw7BDk65jjJlEcEZ8V7ia2bvQ9TS2/FfG02pF2HphfDi7hzujpRt18h17991YJ5ncU6NgyuilzGukb1bGObuNttu/7FZmlariavinIwpC9gcWG20QR/wAhcLaS9xsqY/DGSf8AjM8bXO8Iwkvbe2xFH33/AHKaOooiKgiIgIiICIiAiIgIiICIiAiIgIiIPFzbjPXpdQy3afjksxoXEO3HzuBO9jt0pb7jnXDg4n6DHc9uRMAXOAqmG+h9apc5aSIy4nc9FKLQIDvIdVZe8hpPmq8g0QFiSOs19lBWx5c0saaDuvqqn/I0gVZ7rFa8sJAVbbe4WlFQY5u56f3U5+F7HfxXKePoEBH3Lh/sVCJDsAHd1vdA4mm0HGmZh40b5pnAufJZAaLoACu5639kg7KtRq3Eul6TDzz5DZHkkCOJwc4kf2+65fna5r2pkiTIn5ST8rXFrRd3+xI9lrP0MxdchDSeu9rXA7DoPEuDrvOzGEjJWDmMbxvV1YI28vyszM1bAwmOdkZUYIaXcodbiOmw+645jtOP9D3E962V+KMzSkvNDzWdE5z+O8eJ/Lg4rpgBu6Q8g+wVmLj4ARCfBskHxCx/ftQP+SoiyONkpmO8LfPuVac8uLiGg8woDyHmnsOw4Gdj6hiMyMaRrmuAJANlp8j6rJXKeE9Vm07W4IBOfAlkDJGF1NN7WfUbb+inWZxRp+Lq0Wn8zpJHP5JHNBIZt6dTdCuy1BvEREBERAREQEREBWcqdmLiy5En0xtLjvXQK8oV8QdWlgjZpsBoTM55DRur2APkaNoIdrOpSann5Ga8fUflFVQAAG1mjQ81gSP5WtF9Ba8mlDccHzWvkeZDZcVkVzS8777BWHO3XhKo3JVFXygqoOs0FQGnuFkY8YcTZA/yoPGt8xst3gxsbC1xYAT6LTRmpCPwttC8ljGMG9qUZD5mhprqrLWvkcG+ay48RkbDJM6+9LyI0HSuA36DyU3pFMrWRt8OMDmOxKoIoiGM9vmKuX1e7d7ujVaJNmFu73fUfJB6eWQkNvwWfuV43lDXPd5U1VuADfDafkadyO6szyBrLsV0AQW4ZI48gSlgeGblruh/ClnAekHMzHatlMDo4zUPN3fe5+39z6KH4sEmZlQ4kQJfO8DZpcQPYfldK1DVsfhjSMfT8IRzZrWtY2Fp5je1ki733/IW4NhxBxDh6Fj88x8SZ30RNO59fZY/COq6lq2JNk6hA1kZf/Jc0UCK3AF317rQ8P8AC+ZqecdX4iL7c4PbC8C3H/5A9B2r+1KfAACgAB6Kq9REQEREBERBi6lnQ6dgyZM7mtawbcxqz2H3XG8zLly5Zsqdxc97i435kqffEWdrNMx4KPPJJYPlQXOpzyRBqlGLlSDlbGD0G6sNaHMrv2XkpuQk9F4ySnUoLTraaPVeMFlX54+dvO3qFYB5a2tUVOcfPoqomuouBrdUNF2smOJ36ckedILcPzTCz3UjxPml53AcrBTQo/ixO8Zp9VI4P5UPMeg6rPklVzPEsojJ26lW53fzAwfZGvDIjM4Euf09lbDxE3xX7vd0UWR655iFVcrv2XtNhZyA3K7677K22XwS6V/zyu+m/wCn1VsuL7JJ8yUF1zxVN3aP3WvnkLpCHbNHVXJpy262C2HDHD8/EWcS5wZixOaZTdEgnoPWrVkFrQI8/J1Fv8JPLllrgx11yjlIJvtte66FoPBuFpcrcvKkdl5gIdzu2a0+g/yfIdFu9O0vB0uHwsDGZC3vQ3PuepWYtjxeoiAiIgIiICIvEHPPiJM2TVsWGwTDESfTmPv6f8qD5j9wFuNYzn6lqeRmPDQZHdm1sBQ2s9h5rQZJuQrP6LDiCVbc6uiqVAHM5UZETyQLVEzA13MOhVAPKdlkAiRtHoUFgHoQsznH6cNuwsZkR8TlKrm5eRrWgAjrSDKwGCSfm3NdVt5JByiIHY7lazTh4cLnnuVftz313P7BZovukD3noGN7ql2/8yTp/SFRNytYI422b3VM07YQPE3eOg8kFxwAHiSPodgepWJLkOe8MgFq27xshwLz9RoNHdbHLwv4a1mJyu/iDnU9ootYCBQFb35pBZ0vScjVdTZiY4c9zjcjh0YLAJ9hYXZdK03H0nAjw8QERs7mrcfM11K1XB+gx6PpjXvaP1c7QZXB1jqSAO3fspCtgiIgIiwtS1TC0vH8bNnbG3sOpd7AblBmIoBPr2v8TZTsbh6J2Jiiuad+zu127evYb7IqOgIiKAo3x1qDsLQnRxg8+Q7ww4GuUdT39FJFAviLml2Rj4HKOVrPFcS0XZNCj17G0ELcajBPuVq5tyStjOaZQ6nZa6amggdLpZGO40PdeMHdHH5vZAQqPXVXn6qqJ9Oo9FTYtVBormOyDJFVfcKiOLxJAB3XkT7FLKg5IWueXblQZOzWhrQLHRC/wWbm3HzWK7LH9At57qyWzSP5nHf1UzsZByXElsQsnq49lXHjHxWNIdJLIQAOpJKu4OFkZUrcfFhdNO7o1ouvMldN4U4Xj0iMZeVb86RlPsghm90P23T6NK3S2cKaKdZyYTkZzmiNsbgOWFzu57mq7ef3V7gPRW5DHa7qFTzTOuHn3LKO7vcn8V6qS8S6Sda0WbCZII5CQ5jj0Dge/oo78P8AUJoJcvh/O2nxHExgivlBojp7EXvTvRbk4E3REQF4hIaCXEADqSoNxVxYZXDStCcJ5Zvke+O7BJFNafXz9UGdxNxfHgE4el/9RnEltNbzBnUH7jyWv0DhDIzpxqnEz5pJea2wSGzQ/wBXp02HktrwtwszTKzc9ok1BxJ5g8kMsdvXc2fUqTIKIYY4I2xwxtYxooNaKARXEQEREBct43eH8T5AAI5GtafX5Qf8rqS5XxkP+6svbuz/AMGqUR+Q/UT/AEj9+y1c7t/KhS2Uz/lI2G/MStVLZNKQWhuSvbXhFKuNt7lUUgG99lXRdXkvQzmO52VYbvXQeaChnyHa7V5jC83I40gawDZet6ij7qaLrAGupjdz0W10bSMvV8t2LghjpAOZ73mmsF1v5/ZY2mQY+RqEGNkZH6eOZ/K6YgGvyRQ9V2TR9LxdIwWYmIDyt6udXM43dk/dJNFnQdBw9ExuTHZczwPFlcbLjX9vRbVEWgUH430/9BqGDxFitLTFK1uR4dAkXs7p7jfzGynCxdRwMbUsKTFzIhJE8bjuD5g9iguYuTDmY0eTjSCSGRocxw6EK6TXVQ7hLNbpWoZvDmTOCMaQnHkkcGlzSLIrqe5229tlhcQ6/l69mjReHfnY7d87XFt1ZIvaht962QVcQ8RZGt5Y0Lh353S3zyh3KHNo2N+3r32pbrhrhPE0eGObIYyfP6umJJAO/wBN9Ovusrhzh/G0LD8OK3zP3kkNEk0LA26bLcoCIiAiIgIiIC5ZxjkwZHEc5x2UY6jeR/U5uxP+PsuprjuqMGNrOXHK5ocJHA8rg4dfNSjQzvtzxf8AUsZoLjfmVdyCHSuc3udl41nKoPPCaRfRBFyjurtcrCVaL/PZANNbsqA2+pV7kDmeqCMBvzEAoLQb6lN2u6lenbpZQODUGZiw+NGSd3NPTzW+weItV08sZDlyckf/ALUh5m191HY5ntbTSRsplwlp2JxBoeZgTuYzMjk8SKXlt7QQP22rr37dwkuicZYWbGGag9mLkbk3tGRv0cfTzpSZrmvaHMcHA9CCuMatpObouaYMxjg0kiOSvkkHmPyNuy3HDHFz9LkdDlCSfHeRtzbxm9yPO7/sro6iqJZGQxPllcGMYC5zj0AHUrE0/VcPUML9VjzNLALcCRzM9HAHYrnPEXFc2uSHExScfFY4/MHn+YOlnptXb1VGJxrqGJqmtHK04SPZHGGSvLOUEg7ev5W30PUWfwuPReF4JDqWQwOyMtzeURHuTd3QsD7VZWJhsdqUJ0Hh/EczHmLBl5sjbe4Ak2RdAWDQ9K7rL4UndwxxPkaLnSMcydzWtewWOf8ApPpYNG+m3qUHQcOF+PiRQyzPmexoDpH9XHuVfXi9QEREBERAREQFxPiqJ+Dr+ZDI8PlL+YkEm+bfv7rti5B8RsYxcUzSG/5rGPF+1efogioNuPoqy69+iooD6e68HqDSyMhryWkA7qtuKHMc55dz1Y22WO1wYbb+FW6Vz+5HpaDLxWAOMhHK0eaoyHxCM8rRZP1d1j+OQ3ltx8grbnlxF7UmDwmxZPdex0XCxYXlb7rKxsd0jQWgb9ylFDQTI4tGzRanHwwmA1TMiIovhDhv5Ef7qP4+BA3S8yWScNyGcvKyv/UBNGvbY/lZ3w/yWY/EsQlcxrXxvbzONUav/CQdP1LS8PVIWxZsDZWtdzNvqCub8ScE5emsdlYLjk4zGlzz0cwDzHf3ClGrce6ZgyvixmPy3tH1McAwnbv32vf0UWfJxRxU/mPjRYzg6jTmRAAbgkDfpW61g0EOpZUOBPgiTlhnLTI3lFurpv8A/uiuaRgRZmdHDNkx40b3C3yXR3G23+aHqmhaRJrWsx4MDqaRzPfRPK0dT/j3IW21bhDVtJ55Y4/1GMLPNH8xaN/qHsPKlkdL0jTdP07F5NNjYGOO72nmLiNtz3UZ+IWhnIxRq2LGTNAP53LvbB3+yiGi8RZ+jZI8Nziz+qCQkNOx7dut7LoWj8VabrEYxsgshnkaQ6KQ/K7eqBOxvy9Voe8H8QjW8FzJW8uTjhofvfPt9X3UiXJpI5+DuLoy23QF3Mw70+Mmqs9SP70V1jqg9REQEREBERAXN/iliuGRiZQYeVzCxzuXYEGwL+5XSFr9b0uLWNLmwpTyiQfK6r5SOhQcFNA7FeC+Yb7LJz8OXBy5cadha+N5aR6g0sXoVkV0ASjaHXoV5YI9VU7ZjfJAprTzNNV2Rzm9+ytlwPVW3kmkwXRM3mAN0t1h8joWlsnMPLoo8Fejc5gsEi9lciN5k5cbYyxtOcdqCaBp79Z1iDA8TwmSWXOb1AAs16rTNJNlt7dSt/wW7IbxTgeCQLkpxsC29+vonCul6Zwjo+ncxbj+O51fNP8ANVG9v2Wv4+1pmBpbcHFlaMiY8rmtO7GV6GwdxXsVJ87LhwMKXKyHVHE3mNdT6D1XMtIx3cWcYyZUzScVkhkeObo0fSNzdbDp0tUSngLQjpem/q5wW5GU0Esc2ixtmh0sWKsegUsXjWhrQ0dAK62vUGp1nh7TtZbeVFU1UJWbOH+6her8A5cHNJp0vjxAE8j/AK6Avt132A9l0pFMHDNQxtUjlbFnx5PNGAGiUOJa30voF13hZsreG8Bs4cHiIXzXfp+1LaOa1wpwBFg0fMbhVKgiIgIiICIiAiIg0fE3DuNr2Hyv+TIZvHIAL6HYny3XHdT0vM0zIdDlwPjeP9Q69rHmPVd+WJqenYuqYb8XMjD43D7t9QexUwfP3Regnop3rXw8y4ZC/S3fqGOcKYaDgKN3Zr/lRPM0jUMIgZWJNFzXXOwi68rUGuI2VMg6FZAhcXcoabV+XT8lkQdLjyMa/dpc0i9gdvsR+U0a5oFq61vMd7obo2JwdVbnouscMcH6TLomBk5uK2WdzBISSaN2QCO9WPwFRGeIOGMbQ9BxpY3yzS5Bt8hADRsCB/f91b+Hw/7txq6COT/xKlXxMfyaNiQtDQ0zXXs0jb8qKcEzRYWdm6jKD/0mI97BdWbAr72gkHxH1k/JpGM53M6nShp692tqvQFSPhHSf4RoccLm1LITJJbeU2exFncCh9lCOEdOdxHxHkZ+o80jIiJXkbBzydgfTY/hdSVHqIiAiIgIiICIiAiIgIiICIiAiIgKiSNk0bo5WNexwotcLB+yIgsR6bgRSNkiwcZj2m2ubE0EexpRLirUxrGUzQNLYyaZzwTNYLW7EmiLrtZ9wiINjpHBelYUUL8qBmVksBLnv3aSa7eQrv5lSUAAAAAAdAERBCPid4n6PBrm8Pnfzf6boVfr1/dc/wAeaVuPNjwAXkua0it3AGwPzX4CIs0df4W0YaHpDMcuLpXnxJT0HMQBQ9qpblEWgREQEREBERAREQEREH//2Q==\"/></div>\n    </div>\n</div>\n\n<style>\n    .DJQGCH-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .DJQGCH-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .DJQGCH-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .DJQGCH-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Mean Blue Relative Intensity\"</b></h5>\n<div>\nTotal number of outliers: 304\n</div>\n<div>\nNon-outliers range: 0.32 to 0.34\n</div>\n<div class=\"DJQGCH-container\">\n    <div class=\"DJQGCH-row\">\n        <h5 class=\"DJQGCH-item\">Mean Blue Relative Intensity</h5>\n        <div class=\"DJQGCH-item\">0.28</div><div class=\"DJQGCH-item\">0.28</div><div class=\"DJQGCH-item\">0.28</div><div class=\"DJQGCH-item\">0.34</div><div class=\"DJQGCH-item\">0.34</div><div class=\"DJQGCH-item\">0.34</div>\n    </div>\n    <div class=\"DJQGCH-row\">\n        <h5 class=\"DJQGCH-item\">Image</h5>\n        <div class=\"DJQGCH-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"DJQGCH-item\"><img 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\"/></div><div 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\"/></div>\n    </div>\n</div>\n</div>"}}, "ddd12ca479564b75af13c5f4b4ab20be": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fcca8110e30b499dbc39bcdaf04bbc4f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3b06f43682bb41118923061ab7e86832": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_26e82a38f476497ebbd74bdf6290d52a", "style": "IPY_MODEL_25dc6b8df70e4e3ea4ee23b9067bd980", "value": "<div><h5><b>Properties With No Outliers Found</h5></b></div>"}}, "26e82a38f476497ebbd74bdf6290d52a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "25dc6b8df70e4e3ea4ee23b9067bd980": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7c659a69b86d4b25934d70c60d466187": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7c84c2d3cfea4a96875e4cf7171a9f11", "style": "IPY_MODEL_b1480b7974c34b638bf4da2c9502ca4d", "value": "<style type=\"text/css\">\n#T_24a76 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_24a76 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_24a76 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_24a76 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_24a76 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_24a76\">\n  <thead>\n    <tr>\n      <th id=\"T_24a76_level0_col0\" class=\"col_heading level0 col0\" >More Info</th>\n      <th id=\"T_24a76_level0_col1\" class=\"col_heading level0 col1\" >Properties</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_24a76_row0_col0\" class=\"data row0 col0\" >No outliers found.</td>\n      <td id=\"T_24a76_row0_col1\" class=\"data row0 col1\" >Aspect Ratio, Area</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "7c84c2d3cfea4a96875e4cf7171a9f11": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b1480b7974c34b638bf4da2c9502ca4d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1712ce0a14b24c979b6de3ff7d1f0885": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dd8aafcb12e4438e8e0a7a0fbdcf9444", "style": "IPY_MODEL_254d793d482f44e483851e9d21db4848", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place, check used 5000 images from the original dataset. Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "dd8aafcb12e4438e8e0a7a0fbdcf9444": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "254d793d482f44e483851e9d21db4848": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3613bfe0b24b4379bf81601706426658": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3033c15ab2b3471aa9fd9ae6cb73ce40", "style": "IPY_MODEL_24c41d7751b741c2a83ffe3f191dafd6", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "3033c15ab2b3471aa9fd9ae6cb73ce40": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "24c41d7751b741c2a83ffe3f191dafd6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c6105265610342ad955e901ff0f08fa2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5f32b23e407a407aa23cbb09494b7d79": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fe0dec00f550439e8a164c40f3c66238": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_22b29dbc323a4998b033aa643870c736", "IPY_MODEL_8fb6c1bf7bc242da8196196ca872d19a", "IPY_MODEL_d857604bea2d4d22a5e2edff840a2642"], "layout": "IPY_MODEL_b6df7b142dda4fbba68fe9a23b6732a5"}}, "22b29dbc323a4998b033aa643870c736": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_53b3fa48e9264f1fb6462adbb1a599b0", "style": "IPY_MODEL_a28058d7fd794d65b0f8501fe675e05d", "value": "<h4 id=\"PropertyLabelCorrelation_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Property Label Correlation</b></h4>"}}, "53b3fa48e9264f1fb6462adbb1a599b0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a28058d7fd794d65b0f8501fe675e05d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8fb6c1bf7bc242da8196196ca872d19a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e254e847a7cf45dfa975f1f0a8f88017", "style": "IPY_MODEL_3a60a37650434523be1b671db63a8935", "value": "<p>    Return the Predictive Power Score of image properties, in order to estimate their ability to predict the label. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_property_label_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "e254e847a7cf45dfa975f1f0a8f88017": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3a60a37650434523be1b671db63a8935": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d857604bea2d4d22a5e2edff840a2642": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_18cd7080623f49dca900fe9b76db6b2e", "IPY_MODEL_38a377101b30450995781ff26ce75006", "IPY_MODEL_d637c8cc212f41d690a5d7d6bb8e890e", "IPY_MODEL_0f6b4c484a1f47488917ceeaa2921d5f", "IPY_MODEL_8476487b7eaa4ffd8875e5166338d87f", "IPY_MODEL_922395b1e48f469891d69948b5782948", "IPY_MODEL_18a4fc0cc3434a748869e64af313db10"], "layout": "IPY_MODEL_255e9069eb2f483cbb98f4a9a25b8bb5"}}, "18cd7080623f49dca900fe9b76db6b2e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_019fae16608f4fc09b8e17a9315b4cb5", "style": "IPY_MODEL_89a3b8d0e6c74da1a5e5fff720e66001", "value": "<h5><b>Additional Outputs</b></h5>"}}, "019fae16608f4fc09b8e17a9315b4cb5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "89a3b8d0e6c74da1a5e5fff720e66001": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "38a377101b30450995781ff26ce75006": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "#818800"}, "name": "Test", "text": ["<b>0.13</b>", "<b>0.12</b>", "<b>0.11</b>"], "textposition": "outside", "x": ["Mean Red Relative Intensity", "Mean Green Relative Intensity", "Mean Blue Relative Intensity"], "y": {"dtype": "float64", "shape": [3]}, "type": "bar", "uid": "d5661996-95ce-4aad-aa3a-a8ec96d544cb"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"barmode": "group", "height": 500, "legend": {"x": 1.0, "y": 1.0}, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram": [{"marker": {"pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "histogram"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Predictive Power Score (PPS) - Can a feature predict the label by itself?"}, "xaxis": {"range": [-3, 5], "title": {"text": "Column"}, "type": "category"}, "yaxis": {"range": [0, 1.05], "title": {"text": "Predictive Power Score (PPS)"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "J6FCFOP2wD8YHKSl0DO/P6AgqIPEI7s/"}]}, "d637c8cc212f41d690a5d7d6bb8e890e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a62bf44c086346fdbc73ae44ee2119a0", "style": "IPY_MODEL_1cd4cf8f74994d1587f50addd570830e", "value": "<div>The Predictive Power Score (PPS) is used to estimate the ability of an image property (such as brightness)to predict the label by itself. (Read more about <a href=https://docs.deepchecks.com/stable/vision/auto_checks/data_integrity/plot_property_label_correlation.html target=\"_blank\">Predictive Power Score</a>)</div>"}}, "a62bf44c086346fdbc73ae44ee2119a0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1cd4cf8f74994d1587f50addd570830e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0f6b4c484a1f47488917ceeaa2921d5f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_13dc552180ec4968be2c4e01ede646e7", "style": "IPY_MODEL_e031aa0206f54921b2e4e7a78ad686c7", "value": "<div><u>In the graph above</u>, we should suspect we have problems in our data if:</div>"}}, "13dc552180ec4968be2c4e01ede646e7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e031aa0206f54921b2e4e7a78ad686c7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8476487b7eaa4ffd8875e5166338d87f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4348923573544d7482793aa8be76438d", "style": "IPY_MODEL_1189f9371ee94d8daefe0b7b47b6b848", "value": "<div><b>Train dataset PPS values are high</b>:</div>"}}, "4348923573544d7482793aa8be76438d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1189f9371ee94d8daefe0b7b47b6b848": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "922395b1e48f469891d69948b5782948": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fba645276c1b40f6a163f0ce1f788d66", "style": "IPY_MODEL_7a16537c3d2248aba40712441c2fbd3b", "value": "<div>   A high PPS (close to 1) can mean that there's a bias in the dataset, as a single property can predict the label successfully, using simple classic ML algorithms</div>"}}, "fba645276c1b40f6a163f0ce1f788d66": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7a16537c3d2248aba40712441c2fbd3b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "18a4fc0cc3434a748869e64af313db10": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d1f5955e692141838a5df46924675ce9", "style": "IPY_MODEL_4ab1e5e689c44189846a622395f08750", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "d1f5955e692141838a5df46924675ce9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4ab1e5e689c44189846a622395f08750": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "255e9069eb2f483cbb98f4a9a25b8bb5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b6df7b142dda4fbba68fe9a23b6732a5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1b3eb64144364256b1e4aa640c96293c": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_8c6b475db1f44276aff5481713e9c333", "IPY_MODEL_f8d24b2badc340c28ecb55b345676c94", "IPY_MODEL_bfb1fd06f12943ccb8019c185aa8af22"], "layout": "IPY_MODEL_7ca5b1f7f0b24fbaa44067f4542a2547"}}, "8c6b475db1f44276aff5481713e9c333": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_652bc99eee524508b5bb1a5681fb9e6a", "style": "IPY_MODEL_13d945b9376647c1b9a22cfed242a45c", "value": "<h4 id=\"PropertyLabelCorrelation_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Property Label Correlation</b></h4>"}}, "652bc99eee524508b5bb1a5681fb9e6a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "13d945b9376647c1b9a22cfed242a45c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f8d24b2badc340c28ecb55b345676c94": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_50808b5f4a92415ba681fb793d3c1e7a", "style": "IPY_MODEL_cd93514ecf2b40baa5edbf1e16011490", "value": "<p>    Return the Predictive Power Score of image properties, in order to estimate their ability to predict the label. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_property_label_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "50808b5f4a92415ba681fb793d3c1e7a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cd93514ecf2b40baa5edbf1e16011490": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bfb1fd06f12943ccb8019c185aa8af22": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_e01d01ec36a14a96b3d647fc012cfe29", "IPY_MODEL_9b12a99d32c34e23a07a9a507d84bdde", "IPY_MODEL_6c34156b5c4b4dc9a40b9db4e58f08d3", "IPY_MODEL_a97e030a966940eb82bd8390cdde5e45", "IPY_MODEL_815dd272ae5d4f91807cfb38382b4fca", "IPY_MODEL_9a612397484545b68af984201890c664", "IPY_MODEL_57a2bbc8378f4bfea83818107d6da05e", "IPY_MODEL_f8758c3bc16544f7830b94a7d81dcd1c"], "layout": "IPY_MODEL_4c6c22241d2f4d3ba9b5efca1e2fc1bf"}}, "e01d01ec36a14a96b3d647fc012cfe29": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_72222042134443e982f6ec30f4306c21", "style": "IPY_MODEL_cac65266ec55479ab1d9f27492c68492", "value": "<h5><b>Additional Outputs</b></h5>"}}, "72222042134443e982f6ec30f4306c21": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cac65266ec55479ab1d9f27492c68492": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9b12a99d32c34e23a07a9a507d84bdde": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "#003b47"}, "name": "Train", "text": ["<b>0.13</b>", "<b>0.12</b>", "<b>0.09</b>"], "textposition": "outside", "x": ["Mean Green Relative Intensity", "Mean Red Relative Intensity", "Mean Blue Relative Intensity"], "y": {"dtype": "float64", "shape": [3]}, "type": "bar", "uid": "84fdcff1-7d24-4013-8158-3eaf69a4cc79"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"barmode": "group", "height": 500, "legend": {"x": 1.0, "y": 1.0}, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram": [{"marker": {"pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "histogram"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Predictive Power Score (PPS) - Can a feature predict the label by itself?"}, "xaxis": {"range": [-3, 5], "title": {"text": "Column"}, "type": "category"}, "yaxis": {"range": [0, 1.05], "title": {"text": "Predictive Power Score (PPS)"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "gfpZmZxgwD+LvkFj116/PxlEj8JwV7Y/"}]}, "6c34156b5c4b4dc9a40b9db4e58f08d3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1b1ad348a7cb46da8f8a6ee96e0e7899", "style": "IPY_MODEL_addda552a1e54c129330ab73dcec6e05", "value": "<div>The Predictive Power Score (PPS) is used to estimate the ability of an image property (such as brightness)to predict the label by itself. (Read more about <a href=https://docs.deepchecks.com/stable/vision/auto_checks/data_integrity/plot_property_label_correlation.html target=\"_blank\">Predictive Power Score</a>)</div>"}}, "1b1ad348a7cb46da8f8a6ee96e0e7899": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "addda552a1e54c129330ab73dcec6e05": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a97e030a966940eb82bd8390cdde5e45": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e8995c04fc1e4fb2a95cf41d6408d89d", "style": "IPY_MODEL_b6e34f63fa354cd7b3e4ee0e29b65763", "value": "<div><u>In the graph above</u>, we should suspect we have problems in our data if:</div>"}}, "e8995c04fc1e4fb2a95cf41d6408d89d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b6e34f63fa354cd7b3e4ee0e29b65763": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "815dd272ae5d4f91807cfb38382b4fca": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5931d3cd50ba41b0a0b592428285199c", "style": "IPY_MODEL_e26b8f3b18dd47c199ba5d47891ce693", "value": "<div><b>Train dataset PPS values are high</b>:</div>"}}, "5931d3cd50ba41b0a0b592428285199c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e26b8f3b18dd47c199ba5d47891ce693": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9a612397484545b68af984201890c664": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f1231c86d44b4268a4192e11107385ca", "style": "IPY_MODEL_b1bf73399350482d8449974d56f506d0", "value": "<div>   A high PPS (close to 1) can mean that there's a bias in the dataset, as a single property can predict the label successfully, using simple classic ML algorithms</div>"}}, "f1231c86d44b4268a4192e11107385ca": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b1bf73399350482d8449974d56f506d0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "57a2bbc8378f4bfea83818107d6da05e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c3db1835adf44fb580d365d0f143c02e", "style": "IPY_MODEL_46aa424d18c4466d9af45e72766d21cd", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place, check used 5000 images from the original dataset. Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "c3db1835adf44fb580d365d0f143c02e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "46aa424d18c4466d9af45e72766d21cd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f8758c3bc16544f7830b94a7d81dcd1c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0b4a8d9c0f6447e6adf950a5bcd3c3a7", "style": "IPY_MODEL_0bb2d2258d69418fa7e6d1fabf16f5a9", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "0b4a8d9c0f6447e6adf950a5bcd3c3a7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0bb2d2258d69418fa7e6d1fabf16f5a9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4c6c22241d2f4d3ba9b5efca1e2fc1bf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7ca5b1f7f0b24fbaa44067f4542a2547": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4d0f64dff13447489834baef99b9c17f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_81578f84e76b48d5851d792fc1bb5589", "IPY_MODEL_d5dd4c5cb81341c5a76c4a52913c7e34", "IPY_MODEL_e1f364bea74146c9a1293894382dc254"], "layout": "IPY_MODEL_29ae4cd6dec04037b5b376ece393ebba"}}, "81578f84e76b48d5851d792fc1bb5589": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7e11d900853d446888b8a5330888d611", "style": "IPY_MODEL_2a59e678fbe048a5b8872fae739d2519", "value": "<h4 id=\"ConfusionMatrix_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Confusion Matrix</b></h4>"}}, "7e11d900853d446888b8a5330888d611": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2a59e678fbe048a5b8872fae739d2519": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d5dd4c5cb81341c5a76c4a52913c7e34": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_48772de89f854eb391c8219eabf42ad8", "style": "IPY_MODEL_1b6ac97a3d73470f94909e172d044f3c", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "48772de89f854eb391c8219eabf42ad8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1b6ac97a3d73470f94909e172d044f3c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e1f364bea74146c9a1293894382dc254": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_0af2704d393c478c892b72ee75a1cfb6", "IPY_MODEL_92827e4903de4455993d4b495fc6745c", "IPY_MODEL_82900f08e9254d12a000affbffef107b", "IPY_MODEL_0b86f08c914b4130a7e6f7da6b69e9be", "IPY_MODEL_787a47067e9b4ca8a48741636dadbc45"], "layout": "IPY_MODEL_cbe894ea610d4062bb3c85fc1b41bb61"}}, "0af2704d393c478c892b72ee75a1cfb6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cb73f05ad499498c8822279c4d159036", "style": "IPY_MODEL_354912fbbd12401da6fe5b270ed08236", "value": "<h5><b>Additional Outputs</b></h5>"}}, "cb73f05ad499498c8822279c4d159036": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "354912fbbd12401da6fe5b270ed08236": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "92827e4903de4455993d4b495fc6745c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b093c264485b4c58a569442fe96d86bf", "style": "IPY_MODEL_197d58ff16a645848a4813ce9322d52c", "value": "<div>Showing 10 of 12 classes:</div>"}}, "b093c264485b4c58a569442fe96d86bf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "197d58ff16a645848a4813ce9322d52c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "82900f08e9254d12a000affbffef107b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_afab83cbc9814f9ab2ae857a020ad92e", "style": "IPY_MODEL_56c1b2a6cc4648108095c7c6c8408ea7", "value": "<div>The overall accuracy of your model is: 73.87%.<br>Best accuracy achieved on samples with <b>culex_quinquefasciatus</b> label (97.22%).<br>Worst accuracy achieved on samples with <b>anopheles_arabiensis</b> label (4.0%).</div>"}}, "afab83cbc9814f9ab2ae857a020ad92e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", 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2}}}, "title": {"text": "Confusion Matrix (# Samples)", "x": 0.5}, "xaxis": {"constrain": "domain", "scaleanchor": "y", "title": {"text": "Predicted Value"}, "type": "category"}, "yaxis": {"autorange": "reversed", "constrain": "domain", "title": {"text": "True Value"}, "type": "category"}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "787a47067e9b4ca8a48741636dadbc45": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b2a948f89d2a4e718f05ce28c8ceb777", "style": "IPY_MODEL_7e7f887ed4754cf5a20960f3d9291f66", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "b2a948f89d2a4e718f05ce28c8ceb777": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7e7f887ed4754cf5a20960f3d9291f66": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cbe894ea610d4062bb3c85fc1b41bb61": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "29ae4cd6dec04037b5b376ece393ebba": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a8be309e1a044ddbbe98dce25ac97d38": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_a8803491b02b4f8f8425a3a7f96009d2", "IPY_MODEL_58047e2362d84bbbb1bb842a10f415f3", "IPY_MODEL_7983870418e24be685f043bae2c989e1"], "layout": "IPY_MODEL_944b4c1571f94ad5ae55c467a51b15d3"}}, "a8803491b02b4f8f8425a3a7f96009d2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_65a4997132b54ec3a597cc7c627814f4", "style": "IPY_MODEL_f8ff4995bc57478fa49f04bdcd60a834", "value": "<h4 id=\"ConfusionMatrix_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Confusion Matrix</b></h4>"}}, "65a4997132b54ec3a597cc7c627814f4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f8ff4995bc57478fa49f04bdcd60a834": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "58047e2362d84bbbb1bb842a10f415f3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_59acdd31fa6946598ffc242639856d44", "style": "IPY_MODEL_9fab1706155c4667bb79eb5705052ad6", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "59acdd31fa6946598ffc242639856d44": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9fab1706155c4667bb79eb5705052ad6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7983870418e24be685f043bae2c989e1": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_0c67d7d852a946dd8abe1c76f72452d8", "IPY_MODEL_6bcfbad31c8f400bab1666bd66886019", "IPY_MODEL_5c8b4ba8e11a4c91bbde5aae45c097b2", 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"state": {"_dom_classes": [], "layout": "IPY_MODEL_405ba62682d74734829a4c3ce2c3b76d", "style": "IPY_MODEL_f1355d965b1f424e9f62aceb3a8b380f", "value": "<div>Showing 10 of 12 classes:</div>"}}, "405ba62682d74734829a4c3ce2c3b76d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f1355d965b1f424e9f62aceb3a8b380f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5c8b4ba8e11a4c91bbde5aae45c097b2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_89c6d450e25f426c9cfbd5f514f68337", "style": "IPY_MODEL_f125119646be4e33854cfaa51055cd97", "value": "<div>The overall accuracy of your model is: 85.38%.<br>Best accuracy achieved on samples with <b>culex_inatomii</b> label (100.0%).<br>Worst accuracy achieved on samples with <b>anopheles_arabiensis</b> label (16.03%).</div>"}}, "89c6d450e25f426c9cfbd5f514f68337": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f125119646be4e33854cfaa51055cd97": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c25409a23e78424189d9df3cd7e5a344": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"customdata": [[[6.86, 72.18, 96.83], [0.27, 2.89, 3.87], [0.05, 0.52, 0.7], [0.0, 0.0, 0.0], [1.22, 12.86, 17.25], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [1.02, 10.76, 14.44], [0.07, 0.79, 1.06]], [[0.17, 1.22, 1.22], [14.08, 97.92, 98.09], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.12, 0.87, 0.87], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], 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"IPY_MODEL_a8a738ec819b40a5870fa0d1c8d645ce", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place, check used 5000 images from the original dataset. Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "aabcfb7dbcba45be88de52164967d32f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a8a738ec819b40a5870fa0d1c8d645ce": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cb78f0875d5549308dd1019181a7483e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_109d9ab6c0114e8e9d4bbd73defa0b00", "style": "IPY_MODEL_65d7c2f78ef143c497f25672a9b8ea7a", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "109d9ab6c0114e8e9d4bbd73defa0b00": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "65d7c2f78ef143c497f25672a9b8ea7a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7c11a81cc2484bf0b41196a56501a4a7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "944b4c1571f94ad5ae55c467a51b15d3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c46d10488d2d49b6b18c1d530943c036": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_2d58e92033cf412e8e1b44e367e98d11", "IPY_MODEL_87ed4ec210784d21ab5d6532b47ded69", "IPY_MODEL_47acb381a7ce44d6ae5142e5fcac53f7"], "layout": "IPY_MODEL_743bf935d3cb4d43986d3649a78a6272"}}, "2d58e92033cf412e8e1b44e367e98d11": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c97f03324c7f4a7c848127e157e2fdf9", "style": "IPY_MODEL_3605b7fed7f6449ba90815e29a00af6a", "value": "<h4 id=\"HeatmapComparison_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Heatmap Comparison</b></h4>"}}, "c97f03324c7f4a7c848127e157e2fdf9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3605b7fed7f6449ba90815e29a00af6a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "87ed4ec210784d21ab5d6532b47ded69": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f0070542819c4a29be353634a89701f7", "style": "IPY_MODEL_ca5615f3c1bc478ab80ae0494d76c9c7", "value": "<p>Check if the average image brightness (or bbox location if applicable) is similar between train and test set. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_heatmap_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "f0070542819c4a29be353634a89701f7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ca5615f3c1bc478ab80ae0494d76c9c7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "47acb381a7ce44d6ae5142e5fcac53f7": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_f4fb67adb0644763aef8c2207e05996f", "IPY_MODEL_7a52c0882b8d4dd2b038fabe6f21bc17", "IPY_MODEL_1961cafe523546dfabb869ba1314ec6d", "IPY_MODEL_1ac24e60dbb9447da773f07c6f1b2ffc"], "layout": "IPY_MODEL_fbac49c991014ca9acefe305ca2a6d9b"}}, "f4fb67adb0644763aef8c2207e05996f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d53ceed2b8e644b9b52d1511c691c77f", "style": "IPY_MODEL_05e57163e5f844c682f8bbf46429590f", "value": "<h5><b>Additional Outputs</b></h5>"}}, "d53ceed2b8e644b9b52d1511c691c77f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "05e57163e5f844c682f8bbf46429590f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7a52c0882b8d4dd2b038fabe6f21bc17": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"coloraxis": "coloraxis", "hoverinfo": "skip", "xaxis": "x", "yaxis": "y", "z": [[253, 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style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "ebc3e6f51677499eb85ebf14dc32fdb6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5cab8725d2cf41f4abb15c9fa2ab39f2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1ac24e60dbb9447da773f07c6f1b2ffc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2307d179ec764debab8aa7075a8456be", "style": "IPY_MODEL_987793a83c504807b22d1d8a1da85e12", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "2307d179ec764debab8aa7075a8456be": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "987793a83c504807b22d1d8a1da85e12": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fbac49c991014ca9acefe305ca2a6d9b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "743bf935d3cb4d43986d3649a78a6272": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d25456b2e4b24492bca86196fec5b05d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "18b69ee594e3458bbcda28b402f8ae6b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cc9c67984ffd4265a2ed4b8f1b008959": {"model_name": 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"model_module_version": "1.5.0", "state": {}}, "f6f5a0f8fa03472b8614099f11ee245c": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Didn't Run"}, "children": ["IPY_MODEL_332d78d4d4ea41e0bc9be4ccc1d4f21a"], "layout": "IPY_MODEL_028193da598f416287f4208921c2eccd", "selected_index": null}}, "332d78d4d4ea41e0bc9be4ccc1d4f21a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6654017c87204e2a8c1bc4ea8bb05584", "style": "IPY_MODEL_0e2b772c37dd4a7f80a64a0673178430", "value": "<style type=\"text/css\">\n#T_fdde8 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fdde8 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fdde8 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fdde8 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fdde8 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_fdde8\">\n  <thead>\n    <tr>\n      <th id=\"T_fdde8_level0_col0\" class=\"col_heading level0 col0\" >Check</th>\n      <th id=\"T_fdde8_level0_col1\" class=\"col_heading level0 col1\" >Reason</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_fdde8_row0_col0\" class=\"data row0 col0\" >Mean Average Precision Report</td>\n      <td id=\"T_fdde8_row0_col1\" class=\"data row0 col1\" >Check is irrelevant for task of type TaskType.CLASSIFICATION</td>\n    </tr>\n    <tr>\n      <td id=\"T_fdde8_row1_col0\" class=\"data row1 col0\" >Mean Average Precision Report</td>\n      <td id=\"T_fdde8_row1_col1\" class=\"data row1 col1\" >Check is irrelevant for task of type TaskType.CLASSIFICATION</td>\n    </tr>\n    <tr>\n      <td id=\"T_fdde8_row2_col0\" class=\"data row2 col0\" >Label Property Outliers</td>\n      <td id=\"T_fdde8_row2_col1\" class=\"data row2 col1\" >DeepchecksProcessError: task type classification does not have default label properties for label outliers.</td>\n    </tr>\n    <tr>\n      <td id=\"T_fdde8_row3_col0\" class=\"data row3 col0\" >Label Property Outliers</td>\n      <td id=\"T_fdde8_row3_col1\" class=\"data row3 col1\" >DeepchecksProcessError: task type classification does not have default label properties for label outliers.</td>\n    </tr>\n    <tr>\n      <td id=\"T_fdde8_row4_col0\" class=\"data row4 col0\" >Mean Average Recall Report</td>\n      <td id=\"T_fdde8_row4_col1\" class=\"data row4 col1\" >Check is irrelevant for task of type TaskType.CLASSIFICATION</td>\n    </tr>\n    <tr>\n      <td id=\"T_fdde8_row5_col0\" class=\"data row5 col0\" >Mean Average Recall Report</td>\n      <td id=\"T_fdde8_row5_col1\" class=\"data row5 col1\" >Check is irrelevant for task of type TaskType.CLASSIFICATION</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "6654017c87204e2a8c1bc4ea8bb05584": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0e2b772c37dd4a7f80a64a0673178430": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "028193da598f416287f4208921c2eccd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f9ac0df954f54a859695db71bd803fb0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f36b21f76d564af1b58afe7f77a5353c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3f92093837e04b3a95ebed29a349fb89": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}}}
+{"version_major": 2, "version_minor": 0, "state": {"c6e3d53cf0784f1cb241e1e64f3d2850": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_titles": {"0": "Full Suite"}, "children": ["IPY_MODEL_604419ace70543c6a02d626d4e207bd4"], "layout": "IPY_MODEL_20ace2c004f348da9f60387b696d8dd4"}}, "604419ace70543c6a02d626d4e207bd4": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_667d661a7d57413aa45695f577e52975", "IPY_MODEL_7c52219feb7540a2b4b12626efb5a2a9", "IPY_MODEL_6bdec01cc02d443fb988729a9608a5ee", "IPY_MODEL_4e2c7e270c57440f865f4f0224c26b46", "IPY_MODEL_ed442f91681045fcbe03e43bfc87459f"], "layout": "IPY_MODEL_3509f3f91c4d4c139c4c0e7b0a2b2797"}}, "667d661a7d57413aa45695f577e52975": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2c50a00ade5b4a13955281602097fd7b", "style": "IPY_MODEL_c963936d5c1f4e6c96af92ad91fe38c8", "value": "\n<h1 id=\"summary_VPGB8IY525FNSCQQAVAQDMFZT\">Full Suite</h1>\n<p>\n    The suite is composed of various checks such as: Label Property Outliers, New Labels, Property Label Correlation, etc...<br>\n    Each check may contain conditions (which will result in pass <span style=\"color: green;display:inline-block\">\u2713</span> /\n    fail <span style=\"color: red;display:inline-block\">\u2716</span> / warning <span style=\"color: orange;font-weight:bold;display:inline-block\">!</span> / error <span style=\"color: firebrick;font-weight:bold;display:inline-block\">\u2048</span>) as well as\n    other outputs such as plots or tables.<br>\n    Suites, checks and conditions can all be modified. Read more about\n    <a href=https://docs.deepchecks.com/stable/general/usage/customizations/auto_examples/plot_create_a_custom_suite.html?utm_source=display_output&utm_medium=referral&utm_campaign=suite_link target=\"_blank\">custom suites</a>.\n</p>\n\n"}}, "2c50a00ade5b4a13955281602097fd7b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c963936d5c1f4e6c96af92ad91fe38c8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7c52219feb7540a2b4b12626efb5a2a9": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_af730ee9f25441a992f4167dd076bbe0", "IPY_MODEL_eedb1eb7aecf4e4181accf63ebd06d69"], "layout": "IPY_MODEL_d08a9e4dca5d489b93e3a5a987579c8d"}}, "af730ee9f25441a992f4167dd076bbe0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_532ee9c9f6dc426995dc0797efb241ba", "style": "IPY_MODEL_c9bc16d274724315ad6b801613e144bb", "value": "<span id=\"summary_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\"></span>"}}, "532ee9c9f6dc426995dc0797efb241ba": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c9bc16d274724315ad6b801613e144bb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "eedb1eb7aecf4e4181accf63ebd06d69": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Didn't Pass"}, "children": ["IPY_MODEL_67e356a1810a4dd1a4ade0b06cf6ef1c"], "layout": "IPY_MODEL_f128846b61b8442e93612088ef38c914", "selected_index": null}}, "67e356a1810a4dd1a4ade0b06cf6ef1c": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_159573e76fa24ae1b2773b5f6fad6def", "IPY_MODEL_9f3f4ad058cf431f849f09251fe86b23", "IPY_MODEL_3a10774375a94bcaa3ccef788dbaf0cc", "IPY_MODEL_477aba66a59744b0aafe0fb3ac430545", "IPY_MODEL_8d84ed34a1c94fec92e6fe44fe89ef3d", "IPY_MODEL_477aba66a59744b0aafe0fb3ac430545", "IPY_MODEL_c05cc7f4035540fdbc3959b5c7b1553c", "IPY_MODEL_477aba66a59744b0aafe0fb3ac430545", "IPY_MODEL_e5b9cad08944458e9a11a57bb8c96a61", "IPY_MODEL_477aba66a59744b0aafe0fb3ac430545", "IPY_MODEL_039cb113801547999753d5612263aabf"], "layout": "IPY_MODEL_71c464c50a51433dae68019792707acd"}}, "159573e76fa24ae1b2773b5f6fad6def": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_784b0fb6843d4a7e88b4586f85f3000c", "style": "IPY_MODEL_34ae868ff51b4eb190cb08aed31a289a", "value": "<style type=\"text/css\">\n#T_2e02e table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2e02e thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2e02e tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2e02e th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_2e02e td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_2e02e\">\n  <thead>\n    <tr>\n      <th id=\"T_2e02e_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_2e02e_level0_col1\" class=\"col_heading level0 col1\" >Check</th>\n      <th id=\"T_2e02e_level0_col2\" class=\"col_heading level0 col2\" >Condition</th>\n      <th id=\"T_2e02e_level0_col3\" class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_2e02e_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_2e02e_row0_col1\" class=\"data row0 col1\" ><a href=\"#ClassPerformance_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\">Class Performance</a></td>\n      <td id=\"T_2e02e_row0_col2\" class=\"data row0 col2\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_2e02e_row0_col3\" class=\"data row0 col3\" >12 scores failed. Found max degradation of 62.06% for metric Recall and class aedes_vexans.</td>\n    </tr>\n    <tr>\n      <td id=\"T_2e02e_row1_col0\" class=\"data row1 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_2e02e_row1_col1\" class=\"data row1 col1\" ><a href=\"#PredictionDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\">Prediction Drift</a></td>\n      <td id=\"T_2e02e_row1_col2\" class=\"data row1 col2\" >categorical drift score < 0.15 and numerical drift score < 0.15 for prediction drift</td>\n      <td id=\"T_2e02e_row1_col3\" class=\"data row1 col3\" >Failed for 1 out of 1 prediction properties.\nFound 1 categorical prediction properties with Cramer's V above threshold: {'Samples Per Class': '0.17'}</td>\n    </tr>\n    <tr>\n      <td id=\"T_2e02e_row2_col0\" class=\"data row2 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_2e02e_row2_col1\" class=\"data row2 col1\" ><a href=\"#SimpleModelComparison_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\">Simple Model Comparison</a></td>\n      <td id=\"T_2e02e_row2_col2\" class=\"data row2 col2\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_2e02e_row2_col3\" class=\"data row2 col3\" >Found metrics with gain below threshold: {'F1': {'anopheles_arabiensis': '0%'}}</td>\n    </tr>\n    <tr>\n      <td id=\"T_2e02e_row3_col0\" class=\"data row3 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_2e02e_row3_col1\" class=\"data row3 col1\" ><a href=\"#WeakSegmentsPerformance_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\">Weak Segments Performance</a></td>\n      <td id=\"T_2e02e_row3_col2\" class=\"data row3 col2\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_2e02e_row3_col3\" class=\"data row3 col3\" >Found a segment with cross entropy of -7.009 in comparison to an average score of -3.164 in sampled data.</td>\n    </tr>\n    <tr>\n      <td id=\"T_2e02e_row4_col0\" class=\"data row4 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_2e02e_row4_col1\" class=\"data row4 col1\" ><a href=\"#WeakSegmentsPerformance_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\">Weak Segments Performance</a></td>\n      <td id=\"T_2e02e_row4_col2\" class=\"data row4 col2\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_2e02e_row4_col3\" class=\"data row4 col3\" >Found a segment with cross entropy of -5.98 in comparison to an average score of -2.147 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "784b0fb6843d4a7e88b4586f85f3000c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "34ae868ff51b4eb190cb08aed31a289a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9f3f4ad058cf431f849f09251fe86b23": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f1738c9cb5854022a4a2f570587ab747", "style": "IPY_MODEL_d48bcb3928644efdab806b2d69db897b", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "f1738c9cb5854022a4a2f570587ab747": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d48bcb3928644efdab806b2d69db897b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3a10774375a94bcaa3ccef788dbaf0cc": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_ab2f8b7314d848a29e2694f25379c3e9", "IPY_MODEL_2f94854e7acd4a1ea5d1e91abeac3b2c", "IPY_MODEL_66a3a5ca89e5498697c9caa064c0e9ce", "IPY_MODEL_f5395203707d44ae8b05cc1d603725c2"], "layout": "IPY_MODEL_2006072d082b48c49dade319bdf8c061"}}, "ab2f8b7314d848a29e2694f25379c3e9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4a250bb6aed944e6b135926a0686de74", "style": "IPY_MODEL_637df76165fb477fbcd3525c16343c2f", "value": "<h4 id=\"ClassPerformance_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\"><b>Class Performance</b></h4>"}}, "4a250bb6aed944e6b135926a0686de74": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "637df76165fb477fbcd3525c16343c2f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2f94854e7acd4a1ea5d1e91abeac3b2c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_00f278f2b9144d74bab20de2ee7e816a", "style": "IPY_MODEL_68ea020277ea4c3a99f9b076f566dfc0", "value": "<p>Summarize given metrics on a dataset and model. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_class_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "00f278f2b9144d74bab20de2ee7e816a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "68ea020277ea4c3a99f9b076f566dfc0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "66a3a5ca89e5498697c9caa064c0e9ce": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_555e70f5a1a04506a8d54ede40d09126", "style": "IPY_MODEL_c6df3f88204d4014b72deec27ff9e630", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_845f0 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_845f0 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_845f0 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_845f0 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_845f0 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_845f0\">\n  <thead>\n    <tr>\n      <th id=\"T_845f0_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_845f0_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_845f0_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_845f0_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_845f0_row0_col1\" class=\"data row0 col1\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_845f0_row0_col2\" class=\"data row0 col2\" >12 scores failed. Found max degradation of 62.06% for metric Recall and class aedes_vexans.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "555e70f5a1a04506a8d54ede40d09126": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c6df3f88204d4014b72deec27ff9e630": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f5395203707d44ae8b05cc1d603725c2": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_8dfa31653c2345d9912d866a2dc1e7bc", "IPY_MODEL_455d5c0cfeac408d8e46018ddd6fbf9c", "IPY_MODEL_a63312902b2c468a8546d49895d34c2a", "IPY_MODEL_83d13761279746eca46504df3f57e1ee"], "layout": "IPY_MODEL_07cbacddb6d74fcb88adab56e0d3ae9b"}}, "8dfa31653c2345d9912d866a2dc1e7bc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8518b22815f248b4a2bb2d027d8bd6a3", "style": "IPY_MODEL_8ccb00941c7e4329a12c28211060c694", "value": "<h5><b>Additional Outputs</b></h5>"}}, "8518b22815f248b4a2bb2d027d8bd6a3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8ccb00941c7e4329a12c28211060c694": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "455d5c0cfeac408d8e46018ddd6fbf9c": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Train<br>Metric=Precision<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Train", "marker": {"color": "#003b47", "pattern": {"shape": ""}}, "name": "Train", "offsetgroup": "Train", "orientation": "v", "showlegend": true, "x": ["aedes_albopictus", "anopheles_freeborni", "culex_pipiens", "culex_inatomii", "aedes_koreicus", "anopheles_sinensis", "aedes_vexans", "aedes_aegypti", "aedes_dorsalis", "culex_tritaeniorhynchus", "culex_quinquefasciatus", "anopheles_arabiensis"], "xaxis": "x", "y": {"dtype": "float64", "shape": [12]}, "yaxis": "y", "type": "histogram", "uid": "a1e35b86-cccc-4b88-8114-f8f41225d10b"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Train<br>Metric=Recall<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Train", "marker": {"color": "#003b47", "pattern": {"shape": ""}}, "name": "Train", "offsetgroup": "Train", "orientation": "v", "showlegend": false, "x": ["culex_quinquefasciatus", "culex_tritaeniorhynchus", "culex_inatomii", "aedes_dorsalis", "aedes_albopictus", "aedes_vexans", "aedes_koreicus", "anopheles_sinensis", "culex_pipiens", "aedes_aegypti", 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"@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "68f36867975a4140aa90beaf3bb30bce": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "83d13761279746eca46504df3f57e1ee": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_20c9ae03f0c441679bb0a95ffa943005", "style": "IPY_MODEL_e4ae394544934874b4386f7fa26177ce", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\" style=\"font-size: 14px;\">Go to top</a>"}}, "20c9ae03f0c441679bb0a95ffa943005": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e4ae394544934874b4386f7fa26177ce": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "07cbacddb6d74fcb88adab56e0d3ae9b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2006072d082b48c49dade319bdf8c061": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "477aba66a59744b0aafe0fb3ac430545": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0f79e270a8c44a5494c9dc83a47580b9", "style": "IPY_MODEL_dc08a598f2154ad29dfffa59cd2e92d8", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "0f79e270a8c44a5494c9dc83a47580b9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dc08a598f2154ad29dfffa59cd2e92d8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8d84ed34a1c94fec92e6fe44fe89ef3d": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_c1e314156dda4348ab8b0cf4809b7734", "IPY_MODEL_5df9ba7277554883ac7e11c9d137c2b4", "IPY_MODEL_85adb4a0deb84343a7180b44910bede7", "IPY_MODEL_ad805048ba024e77890d7636afbf017d"], "layout": "IPY_MODEL_676d4c3d7fcf42eaba3aa253a6c510aa"}}, "c1e314156dda4348ab8b0cf4809b7734": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a7472de9296f4bc18eff19ce0f2d4299", "style": "IPY_MODEL_54aa72707ff4468da5e6a553252aaea4", "value": "<h4 id=\"PredictionDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\"><b>Prediction Drift</b></h4>"}}, "a7472de9296f4bc18eff19ce0f2d4299": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "54aa72707ff4468da5e6a553252aaea4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5df9ba7277554883ac7e11c9d137c2b4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bc6c9653fe7e452186069647296f674f", "style": "IPY_MODEL_44b60306c4704bff8b219e80d8d2ed1f", "value": "<p>    Calculate prediction drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_prediction_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "bc6c9653fe7e452186069647296f674f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "44b60306c4704bff8b219e80d8d2ed1f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "85adb4a0deb84343a7180b44910bede7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dafe7047b47646afb2e9a6c20cca7881", "style": "IPY_MODEL_82d3e19fe7514bc8bdc1fde3291482e6", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_901d8 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_901d8 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_901d8 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_901d8 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_901d8 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_901d8\">\n  <thead>\n    <tr>\n      <th id=\"T_901d8_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_901d8_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_901d8_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_901d8_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_901d8_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.15 and numerical drift score < 0.15 for prediction drift</td>\n      <td id=\"T_901d8_row0_col2\" class=\"data row0 col2\" >Failed for 1 out of 1 prediction properties.\nFound 1 categorical prediction properties with Cramer's V above threshold: {'Samples Per Class': '0.17'}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "dafe7047b47646afb2e9a6c20cca7881": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "82d3e19fe7514bc8bdc1fde3291482e6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ad805048ba024e77890d7636afbf017d": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_d6032b7710bd4b9295bfc7f42653c963", "IPY_MODEL_7f9f973180f84aaf92a891a35c77b60d", "IPY_MODEL_b4c18652b4614cf29a229bbf92912a32", "IPY_MODEL_db15b0bfe691404b95ef1c5a4892f2eb", "IPY_MODEL_b43ad423fdd047a399e17cfb13a7e6e4", "IPY_MODEL_6b831eaa3a214c9281070661e92e68f7"], "layout": "IPY_MODEL_76a4506993684c58a6a53c0a6befe29d"}}, "d6032b7710bd4b9295bfc7f42653c963": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cbae9ed5625a4985828c66a9433b8900", "style": "IPY_MODEL_2f17b10e73ea4102b3e1f6e1cc508df7", "value": "<h5><b>Additional Outputs</b></h5>"}}, "cbae9ed5625a4985828c66a9433b8900": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2f17b10e73ea4102b3e1f6e1cc508df7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7f9f973180f84aaf92a891a35c77b60d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8aec7409ea7749e5b99a0c6081ff2145", "style": "IPY_MODEL_bd288ba1352a42f0a4c30468b1d36d14", "value": "<div><span>The Drift score is a measure for the difference between two distributions. In this check, drift is measured for the distribution of the following prediction properties: ['Samples Per Class']. </span></div>"}}, "8aec7409ea7749e5b99a0c6081ff2145": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bd288ba1352a42f0a4c30468b1d36d14": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b4c18652b4614cf29a229bbf92912a32": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3e132b3250c64ded81547001808914b9", "style": "IPY_MODEL_2908b3bb8af949a4828b9e7542ca10e9", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "3e132b3250c64ded81547001808914b9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", 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{"text": "Samples Per Class", "x": 0.5, "xanchor": "center"}, "xaxis": {"anchor": "y", "domain": [0.0, 1.0], "dtick": 0.05, "fixedrange": true, "gridcolor": "black", "linecolor": "black", "range": [0, 0.4], "showgrid": false}, "yaxis": {"anchor": "x", "autorange": true, "color": "black", "domain": [0.9200000000000002, 1.0], "fixedrange": true, "rangemode": "normal", "showgrid": false, "showline": false, "showticklabels": false, "zeroline": false}, "xaxis2": {"anchor": "y2", "domain": [0.0, 1.0], "range": [-3, 12], "type": "category"}, "yaxis2": {"anchor": "x2", "autorange": true, "domain": [0.0, 0.7200000000000001], "fixedrange": true, "rangemode": "normal", "title": {"text": "Frequency"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 2, "y", "buffer"], "data": "B6DmU+L6sj8mTQ11bLq7PwLdmKePVqM/ibasvOJksT8replGbY5oP7JTrVvAnKY/qqGOTXezsT+u+p3UGyikP2Npn0d2qtU/D4ekdAJ8xj8="}, {"encoding": "base64", "path": ["_data", 3, "y", "buffer"], "data": "987rM+iqsT8bKU2nCHy8PwZdNVKaTqE/pwh8nG14oz8JfJxteBN3PyiYL6rcWqY/SLTC5kCitT/pXwkxFMynPxspTacIfMw/K2wOJFph0z8="}]}, "b43ad423fdd047a399e17cfb13a7e6e4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_119dd924940847238ae2efbca011ec1d", "style": "IPY_MODEL_504d396a59774858b15f7327ec79cd6a", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "119dd924940847238ae2efbca011ec1d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "504d396a59774858b15f7327ec79cd6a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6b831eaa3a214c9281070661e92e68f7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e4ac38eb2b2f426aa7a261a8a0c979a3", "style": "IPY_MODEL_05dac9ab95324d3d9960653578ce586e", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\" style=\"font-size: 14px;\">Go to top</a>"}}, "e4ac38eb2b2f426aa7a261a8a0c979a3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "05dac9ab95324d3d9960653578ce586e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "76a4506993684c58a6a53c0a6befe29d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "676d4c3d7fcf42eaba3aa253a6c510aa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c05cc7f4035540fdbc3959b5c7b1553c": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_6b476c3d8dae4304b904de6fe362131f", "IPY_MODEL_793e7b95fb5b4609a5407de4f3d09a72", "IPY_MODEL_e4ff4805bb3d4eb6b1fb7caf6ea73fa2", "IPY_MODEL_0180a85afc1c4168b5b74d9fda6190a9"], "layout": "IPY_MODEL_b3e1dbe962ce4b738b99b1ec05ceb49a"}}, "6b476c3d8dae4304b904de6fe362131f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_29bcf0d02e77419c82d044f6916df437", "style": "IPY_MODEL_05ae5bf70dbf4b8588afd35acaa68f4f", "value": "<h4 id=\"SimpleModelComparison_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\"><b>Simple Model Comparison</b></h4>"}}, "29bcf0d02e77419c82d044f6916df437": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "05ae5bf70dbf4b8588afd35acaa68f4f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "793e7b95fb5b4609a5407de4f3d09a72": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_51fb7f53a46342e98eed88e15e6ed73f", "style": "IPY_MODEL_967316571f5a49e6a4c6fbacdd7d0e04", "value": "<p>Compare given model score to simple model score (according to given model type). <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_simple_model_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "51fb7f53a46342e98eed88e15e6ed73f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "967316571f5a49e6a4c6fbacdd7d0e04": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e4ff4805bb3d4eb6b1fb7caf6ea73fa2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_47e43ec8c2e84dd1ad1e0e8b1dbde417", "style": "IPY_MODEL_ae4d64908fb041829227287df4221983", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_dc1b4 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_dc1b4 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_dc1b4 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_dc1b4 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_dc1b4 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_dc1b4\">\n  <thead>\n    <tr>\n      <th id=\"T_dc1b4_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_dc1b4_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_dc1b4_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_dc1b4_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_dc1b4_row0_col1\" class=\"data row0 col1\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_dc1b4_row0_col2\" class=\"data row0 col2\" >Found metrics with gain below threshold: {'F1': {'anopheles_arabiensis': '0%'}}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "47e43ec8c2e84dd1ad1e0e8b1dbde417": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ae4d64908fb041829227287df4221983": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0180a85afc1c4168b5b74d9fda6190a9": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_708789c0a9924f02a3d5bf1b3c0cabc6", "IPY_MODEL_a785f70012ae4179a059fb29a587a0f6", "IPY_MODEL_f879e72e3f1b4c7bbe2fb2035e0a0703", "IPY_MODEL_e5a7e4826e364eb4a1188bbc8acdef0d"], "layout": "IPY_MODEL_882ea90dbbe94ca29d6dea117afaa023"}}, "708789c0a9924f02a3d5bf1b3c0cabc6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e30999e633974a37822db9fa8c5c39c9", "style": "IPY_MODEL_6770dc383b1e45e4ada91b6717836bc9", "value": "<h5><b>Additional Outputs</b></h5>"}}, "e30999e633974a37822db9fa8c5c39c9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6770dc383b1e45e4ada91b6717836bc9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a785f70012ae4179a059fb29a587a0f6": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Model=Simple Model<br>Metric=F1<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Simple Model", "marker": {"color": "#637a0a", "pattern": {"shape": ""}}, "name": "Simple Model", "offsetgroup": "Simple Model", "orientation": "v", "showlegend": true, "x": ["aedes_albopictus", "aedes_aegypti", "aedes_dorsalis", "aedes_koreicus", "aedes_vexans", "anopheles_arabiensis", "anopheles_freeborni", "anopheles_sinensis", "culex_inatomii", "culex_pipiens", "culex_quinquefasciatus", "culex_tritaeniorhynchus"], "xaxis": "x", "y": {"dtype": "float64", "shape": [12]}, "yaxis": "y", "type": "histogram", "uid": "c3df443b-c556-4a06-9a9d-07d5d23f0416"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Model=Given Model<br>Metric=F1<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Given Model", "marker": {"color": "#e5cb75", "pattern": {"shape": ""}}, "name": "Given Model", "offsetgroup": "Given Model", "orientation": "v", "showlegend": true, "x": ["culex_inatomii", "aedes_dorsalis", "culex_tritaeniorhynchus", "culex_pipiens", "aedes_albopictus", "anopheles_sinensis", "aedes_koreicus", "culex_quinquefasciatus", "aedes_vexans", "aedes_aegypti", 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{"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Simple Model (Strategy: most_frequent) vs. Given Model"}, "xaxis": {"anchor": "y", "domain": [0.0, 1.0], "type": "category"}, "yaxis": {"anchor": "x", "domain": [0.0, 1.0], "showticklabels": true}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "uSEGpGMp0z8AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "11zN1VzN5T9yTyMs9zTiPw0ndV8eW+E/Lgc3+4VY4T8DmRQvkEnhP1FQUFBQUOA/mpmZmZmZ2T8YVz/oqMDXP1HrVwrU+tU/AqnkvCzi0z8SERERERHRPwAAAAAAAAAA"}]}, "f879e72e3f1b4c7bbe2fb2035e0a0703": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_337e9079bb7944d7a897cf10d74ed30b", "style": "IPY_MODEL_fac230879ee648a79abbcd7976001a11", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "337e9079bb7944d7a897cf10d74ed30b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fac230879ee648a79abbcd7976001a11": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e5a7e4826e364eb4a1188bbc8acdef0d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7e8b8b93dae04a42aed3dce576ad2f04", "style": "IPY_MODEL_dc1a659fec6849babc48ddf7d8321848", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\" style=\"font-size: 14px;\">Go to top</a>"}}, "7e8b8b93dae04a42aed3dce576ad2f04": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "dc1a659fec6849babc48ddf7d8321848": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "882ea90dbbe94ca29d6dea117afaa023": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b3e1dbe962ce4b738b99b1ec05ceb49a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e5b9cad08944458e9a11a57bb8c96a61": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_94e3668920534b14ab1cb5ea476967cb", "IPY_MODEL_0c5cdbbe8e334f8fb7b1607a93c85922", "IPY_MODEL_e9c38f19dd824ac3ac053db7cca01eb6", "IPY_MODEL_8597720f7a384eee9fecfb6889f082d0"], "layout": "IPY_MODEL_59482d35b951453ab1864542eb0407c0"}}, "94e3668920534b14ab1cb5ea476967cb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8d3ce070b7e844cb89cc96d5891808db", "style": "IPY_MODEL_2de4d5b7f0b741ef86f969ffdcd7d4f2", "value": "<h4 id=\"WeakSegmentsPerformance_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\"><b>Weak Segments Performance</b></h4>"}}, "8d3ce070b7e844cb89cc96d5891808db": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2de4d5b7f0b741ef86f969ffdcd7d4f2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0c5cdbbe8e334f8fb7b1607a93c85922": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c37b9000422e4e7d864798c7d3d87373", "style": "IPY_MODEL_b89c5254da704c6885f31925352c3ca4", "value": "<p>Search for segments with low performance scores. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_weak_segments_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "c37b9000422e4e7d864798c7d3d87373": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b89c5254da704c6885f31925352c3ca4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e9c38f19dd824ac3ac053db7cca01eb6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4b1936666b3a48158dd96fd99a67a15c", "style": "IPY_MODEL_bfd8df3de36349c2b38ffdc28a598c05", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_aaee3 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_aaee3 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_aaee3 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_aaee3 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_aaee3 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_aaee3\">\n  <thead>\n    <tr>\n      <th id=\"T_aaee3_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_aaee3_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_aaee3_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_aaee3_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_aaee3_row0_col1\" class=\"data row0 col1\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_aaee3_row0_col2\" class=\"data row0 col2\" >Found a segment with cross entropy of -7.009 in comparison to an average score of -3.164 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "4b1936666b3a48158dd96fd99a67a15c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bfd8df3de36349c2b38ffdc28a598c05": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8597720f7a384eee9fecfb6889f082d0": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_f40eeeebf450491e967435588776747c", "IPY_MODEL_7c4a7ce979b04fa783396fa6b1e2f037", "IPY_MODEL_a4eb76a791ac4b4699d7371b2a168dbe", "IPY_MODEL_55ff0a39ca5f432b85e1d835e1c4c17b"], "layout": "IPY_MODEL_faccc52b16bb411084665e7a3d10972d"}}, "f40eeeebf450491e967435588776747c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a902dc61b3fe4c67b373f1e7e2dfe934", "style": "IPY_MODEL_add2ff89375a4569a09024d9d2e6e12e", "value": "<h5><b>Additional Outputs</b></h5>"}}, "a902dc61b3fe4c67b373f1e7e2dfe934": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "add2ff89375a4569a09024d9d2e6e12e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7c4a7ce979b04fa783396fa6b1e2f037": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8b91d5385918486aada5647292a5352d", "style": "IPY_MODEL_7b5dd85b2c254f8fbb60727a1bc41936", "value": "<div>Showcasing intersections of properties with weakest detected segments.<br> The full list of weak segments can be observed in the check result value. </div>"}}, "8b91d5385918486aada5647292a5352d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7b5dd85b2c254f8fbb60727a1bc41936": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, 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true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Cross Entropy (percent of data)"}, "xaxis": {"anchor": "y", "constrain": "domain", "domain": [0.0, 1.0], "scaleanchor": "y", "showgrid": false, "title": {"text": "Brightness"}}, "yaxis": {"anchor": "x", "autorange": "reversed", "constrain": "domain", "domain": [0.0, 1.0], "showgrid": false, "title": {"text": "RMS Contrast"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "9ed2f9ab07954fa79c554be5db0b2cb3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_aa105579d60741cb9e15b095488993b6", "style": "IPY_MODEL_b5f0d5e64ecf43889ca6f0ebf4b73202", "value": "<div>Check ran on 710 data samples. Cross Entropy on the full data set is -3.16.</div>"}}, "aa105579d60741cb9e15b095488993b6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b5f0d5e64ecf43889ca6f0ebf4b73202": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b806600eb9f24dcc95f7ee1069f4c939": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8938167ce7cf47c1b679c2890afbc3cb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "994dea149c2a4ea4a59f11ad228d351c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "55ff0a39ca5f432b85e1d835e1c4c17b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_eab85c588e184d6a971941750a8c4166", "style": "IPY_MODEL_fa8fc5353dec4b239be4ae026bd9bf07", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\" style=\"font-size: 14px;\">Go to top</a>"}}, "eab85c588e184d6a971941750a8c4166": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fa8fc5353dec4b239be4ae026bd9bf07": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "faccc52b16bb411084665e7a3d10972d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "59482d35b951453ab1864542eb0407c0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "039cb113801547999753d5612263aabf": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_2deeeaab5c134ab79daa8ecc8b0e46b9", "IPY_MODEL_e58bef4524764951bdcb9fe436ab511f", "IPY_MODEL_ebc93c5d891c40528e3bf3b67427cb63", "IPY_MODEL_09d123529fd6435ba6f5a83369ed7ef9"], "layout": "IPY_MODEL_63f8bff7cd164cedae065432cb07c3e1"}}, "2deeeaab5c134ab79daa8ecc8b0e46b9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5723a5e0cba343ce92bc04efdc94cc63", "style": "IPY_MODEL_d0c84cef5d4a4fa386b4d305dbed8894", "value": "<h4 id=\"WeakSegmentsPerformance_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\"><b>Weak Segments Performance</b></h4>"}}, "5723a5e0cba343ce92bc04efdc94cc63": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d0c84cef5d4a4fa386b4d305dbed8894": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e58bef4524764951bdcb9fe436ab511f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7abf6c86a9334db3a608d90f736cf11a", "style": "IPY_MODEL_de51be3893674f659807b6728ebe0afa", "value": "<p>Search for segments with low performance scores. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_weak_segments_performance.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "7abf6c86a9334db3a608d90f736cf11a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "de51be3893674f659807b6728ebe0afa": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ebc93c5d891c40528e3bf3b67427cb63": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_633b204112754487adac0a5b7b367c3b", "style": "IPY_MODEL_d61a3dd1e84c4e23ab9c547c5259a901", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_3aa27 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3aa27 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3aa27 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3aa27 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3aa27 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_3aa27\">\n  <thead>\n    <tr>\n      <th id=\"T_3aa27_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_3aa27_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_3aa27_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_3aa27_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_3aa27_row0_col1\" class=\"data row0 col1\" >The relative performance of weakest segment is greater than 80% of average model performance.</td>\n      <td id=\"T_3aa27_row0_col2\" class=\"data row0 col2\" >Found a segment with cross entropy of -5.98 in comparison to an average score of -2.147 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "633b204112754487adac0a5b7b367c3b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d61a3dd1e84c4e23ab9c547c5259a901": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "09d123529fd6435ba6f5a83369ed7ef9": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_ee4a41900e604199bc3d033074dcb6e2", "IPY_MODEL_1bc61b6ec9264cf485975bd850d48547", "IPY_MODEL_7dbd78d94b004c1d95cbc3c49de6b2cb", "IPY_MODEL_e9c5c237864148a3be3d042166c263af", "IPY_MODEL_28a823d87ea94145a28f3e2ae508cf6f"], "layout": "IPY_MODEL_161d89dd1eed41108509cfb513da22e7"}}, "ee4a41900e604199bc3d033074dcb6e2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9fa879b4216b492293787fc1cd6353bf", "style": "IPY_MODEL_2c3c886533294a8ea486e3974106a0b2", "value": "<h5><b>Additional Outputs</b></h5>"}}, "9fa879b4216b492293787fc1cd6353bf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2c3c886533294a8ea486e3974106a0b2": {"model_name": "DescriptionStyleModel", 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"ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Cross Entropy (percent of data)"}, "xaxis": {"anchor": "y", "constrain": "domain", "domain": [0.0, 1.0], "scaleanchor": "y", "showgrid": false, "title": {"text": "RMS Contrast"}}, "yaxis": {"anchor": "x", "autorange": "reversed", "constrain": "domain", "domain": [0.0, 1.0], "showgrid": false}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "6770b65c81b04f1ab851ec6ffd003c9f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1fb49ce942b242019450e251f43e362b", "style": "IPY_MODEL_d9fd4633f73a4af8b53fc24758db52e8", "value": "<div>Check ran on 5004 data samples. Cross Entropy on the full data set is -2.15.</div>"}}, "1fb49ce942b242019450e251f43e362b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d9fd4633f73a4af8b53fc24758db52e8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "29d847b9b5b14608a8054ffecd154915": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a781847337e544e888a5e8069f21177c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f562560179604a28bfa8c5e571e17dc1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e9c5c237864148a3be3d042166c263af": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_74084812f4884242ad60864d6c0d13ec", "style": "IPY_MODEL_8abe4f64eae94022af4b1378d4c6bcad", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place, check used 5000 images from the original dataset. Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "74084812f4884242ad60864d6c0d13ec": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8abe4f64eae94022af4b1378d4c6bcad": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "28a823d87ea94145a28f3e2ae508cf6f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_39c26efcb91342bca593af59e517896a", "style": "IPY_MODEL_5fa921692bf64bcb869818ee24129e4b", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-GP8D2\" style=\"font-size: 14px;\">Go to top</a>"}}, "39c26efcb91342bca593af59e517896a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5fa921692bf64bcb869818ee24129e4b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "161d89dd1eed41108509cfb513da22e7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "63f8bff7cd164cedae065432cb07c3e1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "71c464c50a51433dae68019792707acd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f128846b61b8442e93612088ef38c914": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d08a9e4dca5d489b93e3a5a987579c8d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6bdec01cc02d443fb988729a9608a5ee": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_5a6c96a80be347f3a4a5e644fff06f76", "IPY_MODEL_a8125958d27045609d4c822387f9d139"], "layout": "IPY_MODEL_3917bde37b76466082d2d6c34e0ffbbf"}}, "5a6c96a80be347f3a4a5e644fff06f76": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_279446cd3c8c4ac3814f13aae40dc46b", "style": "IPY_MODEL_c1a72e5ea65242e78c5babb16d4f2fe0", "value": "<span id=\"summary_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\"></span>"}}, "279446cd3c8c4ac3814f13aae40dc46b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c1a72e5ea65242e78c5babb16d4f2fe0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a8125958d27045609d4c822387f9d139": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Passed"}, "children": ["IPY_MODEL_49dad7d0f2f248f480b893237020c4e2"], "layout": "IPY_MODEL_1532eb319f9f4cd7a1b0c26898464fcd", "selected_index": null}}, "49dad7d0f2f248f480b893237020c4e2": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_5ed015b31ac24db0a1e7a52512328d23", "IPY_MODEL_ffe2d858563d4906838ff67699de3e36", "IPY_MODEL_c24c6549f7bf403c883a452c9c816053", "IPY_MODEL_0a791e4c2665464e97df6631bc8f54ec", "IPY_MODEL_1c38134fccf3410a82affa234174aa1f", "IPY_MODEL_0a791e4c2665464e97df6631bc8f54ec", "IPY_MODEL_ce00752d288440dc89d07a2ed9032e76", "IPY_MODEL_0a791e4c2665464e97df6631bc8f54ec", "IPY_MODEL_4ae237021d2e4add97b1387c3608d8d5"], "layout": "IPY_MODEL_e25fc57847c446cfa0bbc03d73f52021"}}, "5ed015b31ac24db0a1e7a52512328d23": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3f08ff3d51764ea8bfa12120ec000522", "style": "IPY_MODEL_a20a6280cbfd4bcfb0760dada6ba94bd", "value": "<style type=\"text/css\">\n#T_5e680 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5e680 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5e680 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5e680 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5e680 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_5e680\">\n  <thead>\n    <tr>\n      <th id=\"T_5e680_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_5e680_level0_col1\" class=\"col_heading level0 col1\" >Check</th>\n      <th id=\"T_5e680_level0_col2\" class=\"col_heading level0 col2\" >Condition</th>\n      <th id=\"T_5e680_level0_col3\" class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_5e680_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_5e680_row0_col1\" class=\"data row0 col1\" ><a href=\"#ImagePropertyDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\">Image Property Drift</a></td>\n      <td id=\"T_5e680_row0_col2\" class=\"data row0 col2\" >drift score < 0.2 for image properties drift</td>\n      <td id=\"T_5e680_row0_col3\" class=\"data row0 col3\" >Passed for 7 properties out of 7 properties.\nFound property \"Mean Blue Relative Intensity\" has the highest numerical drift score: 0.13</td>\n    </tr>\n    <tr>\n      <td id=\"T_5e680_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_5e680_row1_col1\" class=\"data row1 col1\" ><a href=\"#PropertyLabelCorrelationChange_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\">Property Label Correlation Change</a></td>\n      <td id=\"T_5e680_row1_col2\" class=\"data row1 col2\" >Train-Test properties' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_5e680_row1_col3\" class=\"data row1 col3\" >0 PPS found for all properties</td>\n    </tr>\n    <tr>\n      <td id=\"T_5e680_row2_col0\" class=\"data row2 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_5e680_row2_col1\" class=\"data row2 col1\" ><a href=\"#NewLabels_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\">New Labels</a></td>\n      <td id=\"T_5e680_row2_col2\" class=\"data row2 col2\" >Percentage of new labels in the test set is less or equal to 0.5%</td>\n      <td id=\"T_5e680_row2_col3\" class=\"data row2 col3\" >No new labels were found in test set.</td>\n    </tr>\n    <tr>\n      <td id=\"T_5e680_row3_col0\" class=\"data row3 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_5e680_row3_col1\" class=\"data row3 col1\" ><a href=\"#LabelDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\">Label Drift</a></td>\n      <td id=\"T_5e680_row3_col2\" class=\"data row3 col2\" >categorical drift score < 0.15 and numerical drift score < 0.15</td>\n      <td id=\"T_5e680_row3_col3\" class=\"data row3 col3\" >Passed for 1 label properties out of 1 label properties.\nFound label property \"Samples Per Class\" has the highest categorical drift score: 0.13</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "3f08ff3d51764ea8bfa12120ec000522": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a20a6280cbfd4bcfb0760dada6ba94bd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ffe2d858563d4906838ff67699de3e36": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3082030501424d698387b870819fb6ac", "style": "IPY_MODEL_4d7c079d280343f4894966dece2cfb35", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "3082030501424d698387b870819fb6ac": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4d7c079d280343f4894966dece2cfb35": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c24c6549f7bf403c883a452c9c816053": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_af58d21ef779473d9a3c93c98bec124a", "IPY_MODEL_762b14da92fe496aacf17e86ad53fcb9", "IPY_MODEL_379b7a688f7d4094b24724909ad99dfc", "IPY_MODEL_78c46502c5994e70bffb7f1a4ebfd09e"], "layout": "IPY_MODEL_9f0c533d82014b53833f1af683ae7b15"}}, "af58d21ef779473d9a3c93c98bec124a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fec7f5b9dd2a49b4986ef87b6487263a", "style": "IPY_MODEL_6cbb5ddf4eb344b4a564a232e04be532", "value": "<h4 id=\"ImagePropertyDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\"><b>Image Property Drift</b></h4>"}}, "fec7f5b9dd2a49b4986ef87b6487263a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6cbb5ddf4eb344b4a564a232e04be532": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "762b14da92fe496aacf17e86ad53fcb9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_caef575b950244b480840e5c038f4017", "style": "IPY_MODEL_731ef2898bb34c3eb3d05d027d4b12e2", "value": "<p>    Calculate drift between train dataset and test dataset per image property, using statistical measures. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_image_property_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "caef575b950244b480840e5c038f4017": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "731ef2898bb34c3eb3d05d027d4b12e2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "379b7a688f7d4094b24724909ad99dfc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cf67defc011a4c88b36c1b3224409a7b", "style": "IPY_MODEL_c708199df96341dfbad12e3cbdf69a31", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_17b10 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_17b10 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_17b10 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_17b10 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_17b10 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_17b10\">\n  <thead>\n    <tr>\n      <th id=\"T_17b10_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_17b10_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_17b10_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_17b10_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_17b10_row0_col1\" class=\"data row0 col1\" >drift score < 0.2 for image properties drift</td>\n      <td id=\"T_17b10_row0_col2\" class=\"data row0 col2\" >Passed for 7 properties out of 7 properties.\nFound property \"Mean Blue Relative Intensity\" has the highest numerical drift score: 0.13</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "cf67defc011a4c88b36c1b3224409a7b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c708199df96341dfbad12e3cbdf69a31": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "78c46502c5994e70bffb7f1a4ebfd09e": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_dd208b3f6f3544f5a75ca1503a67e343", "IPY_MODEL_2685643f43684c43824e7760b5e01d11", "IPY_MODEL_f819cf98e95442598e3f83ffb53fad5d", "IPY_MODEL_483851cf211048c4af9bb007ebdf8cac", "IPY_MODEL_d06f141d7e25451f81d621dd47bec7e3", "IPY_MODEL_d25fcc487f3842e1959b957796ea665b", "IPY_MODEL_1701afcd52f24c7087778dfb5a2e8826", "IPY_MODEL_3c4d4418a192454cbefbc184b4596370", "IPY_MODEL_e80fa9d241c54ee6a2ff5a9f1c44cace", "IPY_MODEL_eb008adf667a4d23ba1844f6a419787e", "IPY_MODEL_ee708176120742308d4aed2913ee9d16", "IPY_MODEL_2059b515944b4b988f65ce77501efb38"], "layout": "IPY_MODEL_4b3197fc369d4c978c5a36b3eb8d0f41"}}, "dd208b3f6f3544f5a75ca1503a67e343": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_992f62b7499644ada7b28cacda563409", "style": "IPY_MODEL_8e87d952a45a42e88ad860edb8b37f53", "value": "<h5><b>Additional Outputs</b></h5>"}}, "992f62b7499644ada7b28cacda563409": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8e87d952a45a42e88ad860edb8b37f53": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2685643f43684c43824e7760b5e01d11": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_07c1579da88942aa92fbfc641da3f783", "style": "IPY_MODEL_ee487249aade4844b076180058d073af", "value": "<div><span> The Drift score is a measure for the difference between two distributions. In this check, drift is measured for the distribution of the following image properties: ['Area', 'Aspect Ratio', 'Brightness', 'Mean Blue Relative Intensity', 'Mean Green Relative Intensity', 'Mean Red Relative Intensity', 'RMS Contrast'].</span></div>"}}, "07c1579da88942aa92fbfc641da3f783": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ee487249aade4844b076180058d073af": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f819cf98e95442598e3f83ffb53fad5d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e18f4977f4a54369bf2291e84efb5eaa", "style": "IPY_MODEL_9902294301c94311883fcdbc3a5184a1", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, 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[0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram": [{"marker": {"pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "histogram"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], 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"header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Aspect Ratio", "x": 0.5, "xanchor": "center"}, "xaxis": {"anchor": "y", "domain": [0.0, 1.0], "dtick": 0.05, "fixedrange": true, "gridcolor": "black", "linecolor": "black", "range": [0, 0.4], "showgrid": false}, "yaxis": {"anchor": "x", "autorange": true, "color": "black", "domain": [0.9200000000000002, 1.0], "fixedrange": true, "rangemode": "normal", "showgrid": false, "showline": false, "showticklabels": false, "zeroline": false}, "xaxis2": {"anchor": "y2", "domain": [0.0, 1.0], "range": [1.0, 1.0], "tickmode": "array", "ticks": "outside", "tickvals": {"dtype": "float64", "shape": [1]}}, "yaxis2": {"anchor": "x2", "autorange": true, "domain": [0.0, 0.7200000000000001], "fixedrange": true, "rangemode": "normal", "title": {"text": "Frequency"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 1, "x", "buffer"], "data": "AAAAAAAA8D8="}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "AAAAAAAA8D8="}, {"encoding": "base64", "path": ["_data", 2, "x", "buffer"], "data": "AAAAAAAA8D8="}, {"encoding": "base64", "path": ["_data", 2, "y", "buffer"], "data": "AAAAAAAA8D8="}, {"encoding": "base64", "path": ["_layout", "xaxis2", "tickvals", "buffer"], "data": "AAAAAAAA8D8="}]}, "ee708176120742308d4aed2913ee9d16": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5f18d69f01f649fba9ae299441e54a37", "style": "IPY_MODEL_acd741974ae04192b67505b5c62359b0", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "5f18d69f01f649fba9ae299441e54a37": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "acd741974ae04192b67505b5c62359b0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2059b515944b4b988f65ce77501efb38": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1b720673d04442568ab9654b9b8e4a2c", "style": "IPY_MODEL_f7e616054c784d129a7e9cddbec170fa", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\" style=\"font-size: 14px;\">Go to top</a>"}}, "1b720673d04442568ab9654b9b8e4a2c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f7e616054c784d129a7e9cddbec170fa": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4b3197fc369d4c978c5a36b3eb8d0f41": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9f0c533d82014b53833f1af683ae7b15": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0a791e4c2665464e97df6631bc8f54ec": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a5ae6349994e4f9790b0b294d9203483", "style": "IPY_MODEL_01c76622a3c047f88d3fb2de04a976fd", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "a5ae6349994e4f9790b0b294d9203483": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "01c76622a3c047f88d3fb2de04a976fd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1c38134fccf3410a82affa234174aa1f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_6b11f0574ded450190cb5abe1cde3926", "IPY_MODEL_1183d3fd387149a3a604caff606aba58", "IPY_MODEL_c33ad504bc0f4f708f5979587b79f34c", "IPY_MODEL_5c53573d5b59413ea549c1cf3e0746d0"], "layout": "IPY_MODEL_f02eb6fa7ba14fd2a40f313a7e44da88"}}, "6b11f0574ded450190cb5abe1cde3926": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d6af541054a94c23abcd5c0bb4eed23a", "style": "IPY_MODEL_375d151925b74dff86b2a295f0219ccb", "value": "<h4 id=\"PropertyLabelCorrelationChange_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\"><b>Property Label Correlation Change</b></h4>"}}, "d6af541054a94c23abcd5c0bb4eed23a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "375d151925b74dff86b2a295f0219ccb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1183d3fd387149a3a604caff606aba58": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1b0ad48d79b4422e8672c26fa7fa4514", "style": "IPY_MODEL_0adb92f42f9841ac814f45de13880efe", "value": "<p>    Return the Predictive Power Score of image properties, in order to estimate their ability to predict the label. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_property_label_correlation_change.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "1b0ad48d79b4422e8672c26fa7fa4514": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0adb92f42f9841ac814f45de13880efe": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c33ad504bc0f4f708f5979587b79f34c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2038191c71fa4fc0a9b2c2a30ffe2ca8", "style": "IPY_MODEL_37117f56c85e4529bc38f84900d5e3c8", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_fb95f table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fb95f thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fb95f tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fb95f th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fb95f td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_fb95f\">\n  <thead>\n    <tr>\n      <th id=\"T_fb95f_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_fb95f_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_fb95f_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_fb95f_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_fb95f_row0_col1\" class=\"data row0 col1\" >Train-Test properties' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_fb95f_row0_col2\" class=\"data row0 col2\" >0 PPS found for all properties</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "2038191c71fa4fc0a9b2c2a30ffe2ca8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "37117f56c85e4529bc38f84900d5e3c8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5c53573d5b59413ea549c1cf3e0746d0": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_57d6e56557e7468fa28d36ddf56fa688", "IPY_MODEL_93f6f3cbeb9842368e65f82686292dde", "IPY_MODEL_87b4bbc1cf5d426db808068a7b427c6f", "IPY_MODEL_89425a526e8f48cab6034b27154316a9", "IPY_MODEL_18d6b68ef9104ce1b1dc369a7d1a9eb6", "IPY_MODEL_a143818153c7463fa37a8ff64c1960ef", "IPY_MODEL_3f1112a597494f0dacdb5faabad84c87", "IPY_MODEL_f21b96d7394b42b3aa5e469492e2358a", "IPY_MODEL_45e28d6321ac4972bd4bbb758ab633ba", "IPY_MODEL_a025444cb8354fdfb9c8f5ce372f6256", "IPY_MODEL_4998ee4aefee4b6da17199fffb0555f8", "IPY_MODEL_e0910c0ad17a47c2a34c605b8f14b0cb", "IPY_MODEL_2534b7da17734e6d8c6966051a589174"], "layout": "IPY_MODEL_2b8c7da97e744486afb485872f7bc05d"}}, "57d6e56557e7468fa28d36ddf56fa688": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1e71c43b14e040c1a85237277fcc3b7f", "style": "IPY_MODEL_4b263093a82f4463bb62d2503e99221a", "value": "<h5><b>Additional Outputs</b></h5>"}}, "1e71c43b14e040c1a85237277fcc3b7f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4b263093a82f4463bb62d2503e99221a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "93f6f3cbeb9842368e65f82686292dde": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "#003b47"}, "name": "Train", "text": ["<b>0.08</b>", "<b>0.0</b>", "<b>0.0</b>"], "textposition": "outside", "x": ["aedes_albopictus", "anopheles_freeborni", "culex_tritaeniorhynchus"], "y": {"dtype": "float64", "shape": [3]}, "type": "bar", "uid": "155c1973-f4c6-4d64-8a0b-800693e6e718"}, {"marker": {"color": "#818800"}, "name": "Test", "text": ["<b>0.22</b>", "<b>0.0</b>", "<b>0.0</b>"], "textposition": "outside", "x": ["aedes_albopictus", "anopheles_freeborni", "culex_tritaeniorhynchus"], "y": {"dtype": "float64", "shape": [3]}, "type": "bar", "uid": "4145f392-1301-4037-840e-aff2ca79cd7d"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"barmode": "group", "height": 500, "legend": {"x": 1.0, "y": 1.0}, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, 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[0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Mean Red Relative Intensity: Predictive Power Score (PPS) Per Class"}, "xaxis": {"range": [-3, 5], "title": {"text": "Class"}, "type": "category"}, "yaxis": {"range": [0, 1.05], "title": {"text": "Predictive Power Score (PPS)"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "yJfbTrQ5tT8AAAAAAAAAAAAAAAAAAAAA"}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "rIl2LC+lzD8AAAAAAAAAAAAAAAAAAAAA"}]}, "87b4bbc1cf5d426db808068a7b427c6f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0f8be5b737c341518b3e94ab8b6b8d89", "style": "IPY_MODEL_a426841176154b189fe1c8bf95217975", "value": "<div>The Predictive Power Score (PPS) is used to estimate the ability of an image property (such as brightness)to predict the label by itself. (Read more about <a href=https://docs.deepchecks.com/stable/vision/auto_checks/train_test_validation/plot_feature_label_correlation_change.html target=\"_blank\">Predictive Power Score</a>)</div>"}}, "0f8be5b737c341518b3e94ab8b6b8d89": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a426841176154b189fe1c8bf95217975": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "89425a526e8f48cab6034b27154316a9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_607b9c77718a4cbeb7e7922bd90def0d", "style": "IPY_MODEL_4bd1ef943274462ea8a7e1f67b428268", "value": "<div><u>In the graph above</u>, we should suspect we have problems in our data if:</div>"}}, "607b9c77718a4cbeb7e7922bd90def0d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4bd1ef943274462ea8a7e1f67b428268": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "18d6b68ef9104ce1b1dc369a7d1a9eb6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c3b3d19a936346c7905e8fe9bfd01c9b", "style": "IPY_MODEL_3a1d4b17330f41aa8fd7c6dfe7e5e7bd", "value": "<div>1. <b>Train dataset PPS values are high</b>:</div>"}}, "c3b3d19a936346c7905e8fe9bfd01c9b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3a1d4b17330f41aa8fd7c6dfe7e5e7bd": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a143818153c7463fa37a8ff64c1960ef": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9a67f76098fd4516bc1225b816633cc9", "style": "IPY_MODEL_4986ed2d0f4f4fb5bc2c69e101ef7139", "value": "<div>   A high PPS (close to 1) can mean that there's a bias in the dataset, as a single property can predict   the label successfully, using simple classic ML algorithms</div>"}}, "9a67f76098fd4516bc1225b816633cc9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4986ed2d0f4f4fb5bc2c69e101ef7139": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3f1112a597494f0dacdb5faabad84c87": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_84160c9620624eed9949bc67828db411", "style": "IPY_MODEL_63f85820a7804ccea97e7f81eaf36f7a", "value": "<div>2. <b>Large difference between train and test PPS</b> (train PPS is larger):</div>"}}, "84160c9620624eed9949bc67828db411": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "63f85820a7804ccea97e7f81eaf36f7a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f21b96d7394b42b3aa5e469492e2358a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_46f1c0904f6349d59696ab828321302b", "style": "IPY_MODEL_98571258bfc849e88037bfa59f3e5de2", "value": "<div>   An even more powerful indication of dataset bias, as an image property that was powerful in train</div>"}}, "46f1c0904f6349d59696ab828321302b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "98571258bfc849e88037bfa59f3e5de2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "45e28d6321ac4972bd4bbb758ab633ba": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dbeb4ae16ff84f18b270ad5e75eda033", "style": "IPY_MODEL_cc5173267e014c91b3a13a8cfe845b2f", "value": "<div>   but not in test can be explained by bias in train that is not relevant to a new dataset.</div>"}}, "dbeb4ae16ff84f18b270ad5e75eda033": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cc5173267e014c91b3a13a8cfe845b2f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a025444cb8354fdfb9c8f5ce372f6256": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c227e4ee45a945768f4d13127127abbe", "style": "IPY_MODEL_41705cdc689b45de81e8a3c6428db0b5", "value": "<div>3. <b>Large difference between test and train PPS</b> (test PPS is larger):</div>"}}, "c227e4ee45a945768f4d13127127abbe": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "41705cdc689b45de81e8a3c6428db0b5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4998ee4aefee4b6da17199fffb0555f8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f42973f0f7074e2284d2a1436186c6ac", "style": "IPY_MODEL_8ef98770106749b9acf6bb5caab1b5d9", "value": "<div>   An anomalous value, could indicate drift in test dataset that caused a coincidental correlation to the target label.</div>"}}, "f42973f0f7074e2284d2a1436186c6ac": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8ef98770106749b9acf6bb5caab1b5d9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e0910c0ad17a47c2a34c605b8f14b0cb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_03ce78b9752e42f2a9564711ab1f706f", "style": "IPY_MODEL_6a747d5439df469cb45fd9277ae5a1c0", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "03ce78b9752e42f2a9564711ab1f706f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6a747d5439df469cb45fd9277ae5a1c0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2534b7da17734e6d8c6966051a589174": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0021a703e0fb4cd19b201c72aefc1511", "style": "IPY_MODEL_73d35ecd93de48c39e09d44311a07d0d", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\" style=\"font-size: 14px;\">Go to top</a>"}}, "0021a703e0fb4cd19b201c72aefc1511": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "73d35ecd93de48c39e09d44311a07d0d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2b8c7da97e744486afb485872f7bc05d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f02eb6fa7ba14fd2a40f313a7e44da88": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ce00752d288440dc89d07a2ed9032e76": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_8a9fff5d86994b1f852feba0d6b45e67", "IPY_MODEL_a00e79635c2e43dfa99a4602d122f067", "IPY_MODEL_116df95359a24cabac76579b5d822be9", "IPY_MODEL_02fc2fddb114420e90f28da333adb859"], "layout": "IPY_MODEL_8202e477f37443d89a5970083963ba49"}}, "8a9fff5d86994b1f852feba0d6b45e67": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_eb3aab25277342c7b4c1fee3ea5faeba", "style": "IPY_MODEL_6d8c2d391d9a4afea20a27710de98bff", "value": "<h4 id=\"NewLabels_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\"><b>New Labels</b></h4>"}}, "eb3aab25277342c7b4c1fee3ea5faeba": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6d8c2d391d9a4afea20a27710de98bff": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a00e79635c2e43dfa99a4602d122f067": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_216734083f2c470ea814d4c877dd1259", "style": "IPY_MODEL_55081e5791b64baf9ef49d1331039a46", "value": "<p>Detects labels that appear only in the test set. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_new_labels.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "216734083f2c470ea814d4c877dd1259": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "55081e5791b64baf9ef49d1331039a46": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "116df95359a24cabac76579b5d822be9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c58e2658f3ef4d0ba6b55ae55a84791f", "style": "IPY_MODEL_a7355e8bb4e0450294a9a3242ce73442", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_312ea table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_312ea thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_312ea tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_312ea th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_312ea td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_312ea\">\n  <thead>\n    <tr>\n      <th id=\"T_312ea_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_312ea_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_312ea_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_312ea_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_312ea_row0_col1\" class=\"data row0 col1\" >Percentage of new labels in the test set is less or equal to 0.5%</td>\n      <td id=\"T_312ea_row0_col2\" class=\"data row0 col2\" >No new labels were found in test set.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "c58e2658f3ef4d0ba6b55ae55a84791f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a7355e8bb4e0450294a9a3242ce73442": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "02fc2fddb114420e90f28da333adb859": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_4769981614314bee807e4150191b66c5", "IPY_MODEL_9bcc55793a0e4a3080898cb503289fa8", "IPY_MODEL_9462bfe7129645ff88ed8fcf55c3076b"], "layout": "IPY_MODEL_eb5cd20f3c064d989f5fab11c9fb8d94"}}, "4769981614314bee807e4150191b66c5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_874d0615e1e0441cae882cff571e61fc", "style": "IPY_MODEL_45a78a093bcd41eeb9680a77a933060d", "value": "<h5><b>Additional Outputs</b></h5>"}}, "874d0615e1e0441cae882cff571e61fc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "45a78a093bcd41eeb9680a77a933060d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9bcc55793a0e4a3080898cb503289fa8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fb8285d9c65345949e318c1b359762e8", "style": "IPY_MODEL_db4720aedca5487990568b6ca1739275", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "fb8285d9c65345949e318c1b359762e8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "db4720aedca5487990568b6ca1739275": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9462bfe7129645ff88ed8fcf55c3076b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ceeac383875545e7b77320fac7a93dfc", "style": "IPY_MODEL_108255a852f14d91ac11abe5399f5302", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\" style=\"font-size: 14px;\">Go to top</a>"}}, "ceeac383875545e7b77320fac7a93dfc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "108255a852f14d91ac11abe5399f5302": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "eb5cd20f3c064d989f5fab11c9fb8d94": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8202e477f37443d89a5970083963ba49": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4ae237021d2e4add97b1387c3608d8d5": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_5fba89a026ac4c68a37ba39fd1fdda29", "IPY_MODEL_a9a7b81712de4ea3b3cd3e0416fa46df", "IPY_MODEL_8c72022afb6b45bbabd395463f5e3b76", "IPY_MODEL_132513dec4bc4f1aaa4eae1a2e8c0927"], "layout": "IPY_MODEL_67d054667bfe4da69a54212dfa53450f"}}, "5fba89a026ac4c68a37ba39fd1fdda29": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7b1a78db2c594be68640c5ec183dfb41", "style": "IPY_MODEL_f74c533075f0434fb4b092bcaa499307", "value": "<h4 id=\"LabelDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-0GM62\"><b>Label Drift</b></h4>"}}, "7b1a78db2c594be68640c5ec183dfb41": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f74c533075f0434fb4b092bcaa499307": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a9a7b81712de4ea3b3cd3e0416fa46df": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b58b2105edfd4bf8941756e868b25aea", "style": "IPY_MODEL_7a071f1425804a1e8c2ca70bda531c9a", "value": "<p>    Calculate label drift between train dataset and test dataset, using statistical measures. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_label_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "b58b2105edfd4bf8941756e868b25aea": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7a071f1425804a1e8c2ca70bda531c9a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8c72022afb6b45bbabd395463f5e3b76": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c99e23fb46ef4d929878c7e7da0fec61", "style": "IPY_MODEL_b460e5d7e24342d99b92ca4b671e12eb", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_1a84f table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1a84f thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1a84f tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1a84f th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_1a84f td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_1a84f\">\n  <thead>\n    <tr>\n      <th id=\"T_1a84f_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_1a84f_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_1a84f_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_1a84f_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_1a84f_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.15 and numerical drift score < 0.15</td>\n      <td id=\"T_1a84f_row0_col2\" class=\"data row0 col2\" >Passed for 1 label properties out of 1 label properties.\nFound label property \"Samples Per Class\" has the highest categorical drift score: 0.13</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "c99e23fb46ef4d929878c7e7da0fec61": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b460e5d7e24342d99b92ca4b671e12eb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "132513dec4bc4f1aaa4eae1a2e8c0927": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_a4c909639a3f479fac50b9ce1d345d40", "IPY_MODEL_27b0530df10c4b648aaa70a0f762cda0", "IPY_MODEL_745ea7dd851342b6b7fd7eab8b30a035", "IPY_MODEL_7f70edeea0134a99af7c51d9019102a3", "IPY_MODEL_c2aca65588834b95a9536222b8d6794b", "IPY_MODEL_2faf8db0fc2c423b99f7e5dcd7b9128f"], "layout": "IPY_MODEL_ba526459cb8d44eaaf28dc6e11b64583"}}, "a4c909639a3f479fac50b9ce1d345d40": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_902af0e219e24d62be459d8a1181f6fa", "style": "IPY_MODEL_5232497f69f64d6987444eef4f911a18", "value": "<h5><b>Additional Outputs</b></h5>"}}, "902af0e219e24d62be459d8a1181f6fa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5232497f69f64d6987444eef4f911a18": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "27b0530df10c4b648aaa70a0f762cda0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3e87e5381aa94e31bb9040496f3c1d3e", "style": "IPY_MODEL_91680be51e7c4f8d87b25a2d782f3f08", "value": "<div><span> The Drift score is a measure for the difference between two distributions. In this check, drift is measured for the distribution of the following label properties: ['Samples Per Class'].</span></div>"}}, "3e87e5381aa94e31bb9040496f3c1d3e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "91680be51e7c4f8d87b25a2d782f3f08": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "745ea7dd851342b6b7fd7eab8b30a035": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_eed9e0b095974c8e98106f483b620531", "style": "IPY_MODEL_f49cd637135d4cfd813ef6436b77362e", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "eed9e0b095974c8e98106f483b620531": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f49cd637135d4cfd813ef6436b77362e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7f70edeea0134a99af7c51d9019102a3": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#6DB36D"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.1], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "0e654c5e-f77b-424b-a352-0ae4418d45bc"}, {"base": 0.1, "marker": {"color": "#F9F871"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.029912295142730194], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "491a9d1a-db02-48e5-98cd-39f2ee7b8cb5"}, {"marker": {"color": "#003b47"}, "name": "Train Dataset", "x": ["aedes_aegypti", "aedes_albopictus", "aedes_dorsalis", "aedes_vexans", "anopheles_arabiensis", "anopheles_freeborni", "culex_inatomii", "culex_pipiens", "culex_quinquefasciatus", "culex_tritaeniorhynchus"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [10]}, "yaxis": "y2", "type": "bar", "uid": "3ae6fe4b-cfe6-429b-b555-5e0818a5fd5c"}, {"marker": {"color": "#818800"}, "name": "Test Dataset", "x": ["aedes_aegypti", "aedes_albopictus", "aedes_dorsalis", "aedes_vexans", "anopheles_arabiensis", "anopheles_freeborni", "culex_inatomii", "culex_pipiens", "culex_quinquefasciatus", "culex_tritaeniorhynchus"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [10]}, "yaxis": "y2", "type": "bar", "uid": "495ce846-d786-4d97-89ac-008686b25484"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"annotations": [{"font": {"size": 16}, "showarrow": false, "text": "Drift Score (Cramer's V)", "x": 0.5, "xanchor": "center", "xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}, {"font": {"size": 16}, "showarrow": false, "text": "Distribution Plot", "x": 0.5, "xanchor": "center", "xref": "paper", "y": 0.7200000000000001, "yanchor": "bottom", "yref": "paper"}], "bargroupgap": 0, "height": 400, "legend": {"title": {"text": "Legend"}, "y": 0.6, "yanchor": "top"}, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, 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"#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram": [{"marker": {"pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "histogram"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, 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"#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Samples Per Class", "x": 0.5, "xanchor": "center"}, "xaxis": {"anchor": "y", "domain": [0.0, 1.0], "dtick": 0.05, "fixedrange": true, "gridcolor": "black", "linecolor": "black", "range": [0, 0.4], "showgrid": false}, "yaxis": {"anchor": "x", "autorange": true, "color": "black", "domain": [0.9200000000000002, 1.0], "fixedrange": true, "rangemode": "normal", "showgrid": false, "showline": false, "showticklabels": false, "zeroline": false}, "xaxis2": {"anchor": "y2", "domain": [0.0, 1.0], "range": [-3, 12], "type": "category"}, "yaxis2": {"anchor": "x2", "autorange": true, "domain": [0.0, 0.7200000000000001], "fixedrange": true, "rangemode": "normal", "title": {"text": "Frequency"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 2, "y", "buffer"], "data": "SHaqdQuYsz+6m41EW1a+P5xYpUh/xrI/NAA1nxLXtz8/Wk1CFH+xP7jqd1JvoLA/gDARZD0csj8PUgViK+S3P676ndQbKLQ/UCjJg1SBuD8="}, {"encoding": "base64", "path": ["_data", 3, "y", "buffer"], "data": "en0GXTVSuj8g0QqbA4nGP2rvvD6Drqo/igtQe+f1uT9o0FUjpemkP8ckD9nRv6I/KJgvqtxapj9q77w+g666P4oLUHvn9ak/C5sDiVbYvD8="}]}, "c2aca65588834b95a9536222b8d6794b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_81ef5e563c1f48409a08b838207e6724", "style": "IPY_MODEL_6aad75f05cc149d885dd08e696930778", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "81ef5e563c1f48409a08b838207e6724": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", 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"IPY_MODEL_10481fb900e44aab964cac9446f87033"}}, "ee876b4d866f4c31809833fd9f098980": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_05ad79c752734e4c98e064bc34de6a77", "style": "IPY_MODEL_07d8fd5d672545af8042b2df83111714", "value": "<span id=\"summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"></span>"}}, "05ad79c752734e4c98e064bc34de6a77": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "07d8fd5d672545af8042b2df83111714": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9560933e2db248c7860609ad8035789b": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Other"}, "children": ["IPY_MODEL_1ca31f8c56a94984bb061693d7cd6249"], "layout": "IPY_MODEL_2552f3b0caa743efab6790610e89f0b4", "selected_index": null}}, "1ca31f8c56a94984bb061693d7cd6249": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_2fea48d4cd604c23b3bf5680743a302b", "IPY_MODEL_8d3259c73ad74094bde69a2ad3717856", "IPY_MODEL_c3812999dd2b476fb896a9d5c26461a2", "IPY_MODEL_648185893eee462f9957d12be473592a", "IPY_MODEL_de9122c148e04957b171f6dfafd31844", "IPY_MODEL_648185893eee462f9957d12be473592a", "IPY_MODEL_a51502f6dfbf4009affbe7e5637dfacc", "IPY_MODEL_648185893eee462f9957d12be473592a", "IPY_MODEL_3ad0afb16a2748be901bddc3b01b1705", "IPY_MODEL_648185893eee462f9957d12be473592a", "IPY_MODEL_6d8314d3093c4ba2b6ec0a3284a227e6", "IPY_MODEL_648185893eee462f9957d12be473592a", "IPY_MODEL_e68afd611cfe4508aad7e662a167cc6d", "IPY_MODEL_648185893eee462f9957d12be473592a", "IPY_MODEL_a7e585b9aca4430e9a0960932fb272f4", "IPY_MODEL_648185893eee462f9957d12be473592a", "IPY_MODEL_d80b1aab24674910ae2abf891338d9f0"], "layout": "IPY_MODEL_8ceeae0b27214e9fbeb68c1cda4730d1"}}, "2fea48d4cd604c23b3bf5680743a302b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4ded09ecbeea4ccdad6bd775b34bea67", "style": "IPY_MODEL_db9eab35e4ed40efb02700923e4415fa", "value": "<style type=\"text/css\">\n#T_31590 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_31590 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_31590 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_31590 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_31590 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_31590\">\n  <thead>\n    <tr>\n      <th id=\"T_31590_level0_col0\" class=\"col_heading level0 col0\" >Check</th>\n      <th id=\"T_31590_level0_col1\" class=\"col_heading level0 col1\" >Summary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_31590_row0_col0\" class=\"data row0 col0\" ><a href=\"#ImageDatasetDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Image Dataset Drift</a></td>\n      <td id=\"T_31590_row0_col1\" class=\"data row0 col1\" >Calculate drift between the entire train and test datasets (based on image properties) using a trained model. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_image_dataset_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_31590_row1_col0\" class=\"data row1 col0\" ><a href=\"#ImagePropertyOutliers_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Image Property Outliers</a></td>\n      <td id=\"T_31590_row1_col1\" class=\"data row1 col1\" >Find outliers images with respect to the given properties. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_image_property_outliers.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_31590_row2_col0\" class=\"data row2 col0\" ><a href=\"#ImagePropertyOutliers_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Image Property Outliers</a></td>\n      <td id=\"T_31590_row2_col1\" class=\"data row2 col1\" >Find outliers images with respect to the given properties. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_image_property_outliers.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_31590_row3_col0\" class=\"data row3 col0\" ><a href=\"#PropertyLabelCorrelation_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Property Label Correlation</a></td>\n      <td id=\"T_31590_row3_col1\" class=\"data row3 col1\" >    Return the Predictive Power Score of image properties, in order to estimate their ability to predict the label. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_property_label_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_31590_row4_col0\" class=\"data row4 col0\" ><a href=\"#PropertyLabelCorrelation_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Property Label Correlation</a></td>\n      <td id=\"T_31590_row4_col1\" class=\"data row4 col1\" >    Return the Predictive Power Score of image properties, in order to estimate their ability to predict the label. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_property_label_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_31590_row5_col0\" class=\"data row5 col0\" ><a href=\"#ConfusionMatrix_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Confusion Matrix</a></td>\n      <td id=\"T_31590_row5_col1\" class=\"data row5 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_31590_row6_col0\" class=\"data row6 col0\" ><a href=\"#ConfusionMatrix_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Confusion Matrix</a></td>\n      <td id=\"T_31590_row6_col1\" class=\"data row6 col1\" >Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n    <tr>\n      <td id=\"T_31590_row7_col0\" class=\"data row7 col0\" ><a href=\"#HeatmapComparison_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\">Heatmap Comparison</a></td>\n      <td id=\"T_31590_row7_col1\" class=\"data row7 col1\" >Check if the average image brightness (or bbox location if applicable) is similar between train and test set. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_heatmap_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></td>\n    </tr>\n  </tbody>\n</table>\n"}}, "4ded09ecbeea4ccdad6bd775b34bea67": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "db9eab35e4ed40efb02700923e4415fa": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8d3259c73ad74094bde69a2ad3717856": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_303e3f8a50b74157ab665905ae119110", "style": "IPY_MODEL_f599106b60b84f3885230641afd4b883", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "303e3f8a50b74157ab665905ae119110": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f599106b60b84f3885230641afd4b883": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c3812999dd2b476fb896a9d5c26461a2": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_e5016deb2a1a4575b4d4b0551522f50a", "IPY_MODEL_9281cbd836204ae6bfe07b0880a8a141", "IPY_MODEL_891344d71638426dae49059bed1c52a9"], "layout": "IPY_MODEL_64b766b971a141b5b4814f6464c2da4f"}}, "e5016deb2a1a4575b4d4b0551522f50a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_89f07cc4f92247e69c8a5de5b62cf6eb", "style": "IPY_MODEL_a9b917ba813d4fc69af728602307eb86", "value": "<h4 id=\"ImageDatasetDrift_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Image Dataset Drift</b></h4>"}}, "89f07cc4f92247e69c8a5de5b62cf6eb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a9b917ba813d4fc69af728602307eb86": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9281cbd836204ae6bfe07b0880a8a141": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a67eed69f01d45caa020b2f9a7fb3c29", "style": "IPY_MODEL_c3091d1c6bb949d3af58ba9af8a2bb24", "value": "<p>Calculate drift between the entire train and test datasets (based on image properties) using a trained model. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_image_dataset_drift.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "a67eed69f01d45caa020b2f9a7fb3c29": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c3091d1c6bb949d3af58ba9af8a2bb24": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "891344d71638426dae49059bed1c52a9": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_27419ef70df54e8282784b95107a30a3", "IPY_MODEL_189b4c6ed5a64f97b2c38d56a703cb62", "IPY_MODEL_0ec06a7a8c034e299b6f79aad4e7a6d4", "IPY_MODEL_1175699370814282a05862d0a348a68a", "IPY_MODEL_ee675239a1c140b1876c3079eddd3ba6", "IPY_MODEL_2824c891e64c4b5aba9ab571b1051c06", "IPY_MODEL_a822c4ef2e6f455e9d42b4276c9ae67f", "IPY_MODEL_ee44beaad0764d19bc2ce9b7f3421524", "IPY_MODEL_b3778177f29e4779908348906a3eaba6", "IPY_MODEL_73914ea71aeb466eb4b09ce593da51df", "IPY_MODEL_f16c83129cd74eab919c008bd4c1939a", "IPY_MODEL_27c8f94358fa4c56a67d6bd7206d19a4"], "layout": "IPY_MODEL_7bd37a9fbd234e2d9790dc0338bb755b"}}, "27419ef70df54e8282784b95107a30a3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_20b94a55bd5145338c1095465a3665e6", "style": "IPY_MODEL_b4d56cf7d74840529321fd6a0b14a2de", "value": "<h5><b>Additional Outputs</b></h5>"}}, "20b94a55bd5145338c1095465a3665e6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b4d56cf7d74840529321fd6a0b14a2de": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "189b4c6ed5a64f97b2c38d56a703cb62": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_430b2165e2f545b4a30f232829e27bbc", "style": "IPY_MODEL_c50c68b9c3be4b60958be5d0e9321182", "value": "<div>\n        <span>\n        The shown features are the image properties (brightness, aspect ratio, etc.) that are most important for the\n        domain classifier - the domain_classifier trained to distinguish between the train and test datasets.<br>\n        </span>\n        </div>"}}, "430b2165e2f545b4a30f232829e27bbc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c50c68b9c3be4b60958be5d0e9321182": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0ec06a7a8c034e299b6f79aad4e7a6d4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1283a642629c4628aa0edfd6ca395c92", "style": "IPY_MODEL_b0cf10b48c22427d82ebe763717c0de4", "value": "<div>\n    <span>\n    The percents of explained dataset difference are the importance values for the feature calculated\n    using `permutation_importance`.\n    </span><br><br>\n    </div>"}}, "1283a642629c4628aa0edfd6ca395c92": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b0cf10b48c22427d82ebe763717c0de4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1175699370814282a05862d0a348a68a": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#6DB36D"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.1], "y": ["Drift Score"], "type": "bar", "uid": "661d0f30-c507-43fd-84e8-fc595181fec3"}, {"base": 0.1, "marker": {"color": "#F9F871"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.08393616786792743], "y": ["Drift Score"], "type": "bar", "uid": "c9f4e773-70fc-40b5-ada6-45894d548815"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"height": 200, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram": [{"marker": {"pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "histogram"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Drift Score - Multivariable"}, "xaxis": {"dtick": 0.05, "fixedrange": true, "gridcolor": "black", "linecolor": "black", "range": [0, 0.4], "showgrid": false, "title": {"text": "Drift score"}}, "yaxis": {"autorange": true, "color": "black", "fixedrange": true, "rangemode": "normal", "showgrid": false, "showline": false, "showticklabels": false, "zeroline": false}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "ee675239a1c140b1876c3079eddd3ba6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3184736ff14645879fadcc19b22fd5ef", "style": "IPY_MODEL_060ee31460284c1eaa22713d26a814eb", "value": "<div><h3>Main features contributing to drift</h3></div>"}}, "3184736ff14645879fadcc19b22fd5ef": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "060ee31460284c1eaa22713d26a814eb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2824c891e64c4b5aba9ab571b1051c06": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e7fdbf12a2be4045a7a202aedec02ec0", "style": "IPY_MODEL_025b3ce612bf4e5fae8a74477b01bc07", "value": "<div>Showing only the top 3 columns, you can change it using n_top_columns param</div>"}}, "e7fdbf12a2be4045a7a202aedec02ec0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "025b3ce612bf4e5fae8a74477b01bc07": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a822c4ef2e6f455e9d42b4276c9ae67f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_937fa97b6b8344b5a92b20a6358a09c4", "style": "IPY_MODEL_c8f61761edab4370b819016a5c8ef7df", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "937fa97b6b8344b5a92b20a6358a09c4": 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"f16c83129cd74eab919c008bd4c1939a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_623bb40ec123423fa9908cea360f2f10", "style": "IPY_MODEL_40b13fad8b4547c28c23ff84c358b865", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "623bb40ec123423fa9908cea360f2f10": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "40b13fad8b4547c28c23ff84c358b865": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "27c8f94358fa4c56a67d6bd7206d19a4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_41e82884af134b12a0bdec167cba9f16", "style": "IPY_MODEL_da0d3010755848668d5f20ce161d9f14", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "41e82884af134b12a0bdec167cba9f16": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "da0d3010755848668d5f20ce161d9f14": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7bd37a9fbd234e2d9790dc0338bb755b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "64b766b971a141b5b4814f6464c2da4f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "648185893eee462f9957d12be473592a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_188586df902e4b779ace2613a785b812", "style": "IPY_MODEL_73c236df32c74e5eacafcc6f6a96d149", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "188586df902e4b779ace2613a785b812": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "73c236df32c74e5eacafcc6f6a96d149": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "de9122c148e04957b171f6dfafd31844": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_028fd9878e6f49cb952a0839073a3d23", "IPY_MODEL_6492ab9dd8834b86a1bc4152cfa551f5", "IPY_MODEL_6616cd0b488549879c004c601b8d8c7f"], "layout": "IPY_MODEL_65aecbe9162141cebd51cd71103f6ae9"}}, "028fd9878e6f49cb952a0839073a3d23": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_38a1fd59c8084f08bebafe2f4ba80e38", "style": "IPY_MODEL_d9b3faea905741f8b3483a80140be890", "value": "<h4 id=\"ImagePropertyOutliers_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Image Property Outliers</b></h4>"}}, "38a1fd59c8084f08bebafe2f4ba80e38": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d9b3faea905741f8b3483a80140be890": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6492ab9dd8834b86a1bc4152cfa551f5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_95c0e28f8a444d3d8b57b6d652bf206a", "style": "IPY_MODEL_12ed6a6b7f3742fb9dcb4a8cfbcf57f2", "value": "<p>Find outliers images with respect to the given properties. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_image_property_outliers.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "95c0e28f8a444d3d8b57b6d652bf206a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "12ed6a6b7f3742fb9dcb4a8cfbcf57f2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6616cd0b488549879c004c601b8d8c7f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_3655923d83434cd5bb2dde027e1e8bbd", "IPY_MODEL_2910cf882c9946c087d2380e670edb37", "IPY_MODEL_1eb8b606dcc44acaa7a61b314f1744e7", "IPY_MODEL_b3b4437a03074a6ba27069b62c8a66ad", "IPY_MODEL_f9a0b85191b2492cbbb569b76039e630"], "layout": "IPY_MODEL_04e09e0c8d084232acdabb75c78c890c"}}, "3655923d83434cd5bb2dde027e1e8bbd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3956567a5ed64035b1093511879467ee", "style": "IPY_MODEL_04d516e1816d474cad95b36e085d1877", "value": "<h5><b>Additional Outputs</b></h5>"}}, "3956567a5ed64035b1093511879467ee": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "04d516e1816d474cad95b36e085d1877": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2910cf882c9946c087d2380e670edb37": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6dbc337a35b94312895e8b3c27713122", "style": "IPY_MODEL_7d6e3aa951a34ec7bfaa136e7f2c1370", "value": "<div>\n<style>\n    .MMLSIY-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .MMLSIY-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .MMLSIY-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .MMLSIY-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Brightness\"</b></h5>\n<div>\nTotal number of outliers: 22\n</div>\n<div>\nNon-outliers range: 196.73 to 250.3\n</div>\n<div class=\"MMLSIY-container\">\n    <div class=\"MMLSIY-row\">\n        <h5 class=\"MMLSIY-item\">Brightness</h5>\n        <div class=\"MMLSIY-item\">162.08</div><div class=\"MMLSIY-item\">165.15</div><div class=\"MMLSIY-item\">175.72</div>\n    </div>\n    <div class=\"MMLSIY-row\">\n        <h5 class=\"MMLSIY-item\">Image</h5>\n        <div class=\"MMLSIY-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"MMLSIY-item\"><img 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\"/></div><div 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\"/></div>\n    </div>\n</div>\n\n<style>\n    .BKWMNT-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .BKWMNT-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .BKWMNT-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .BKWMNT-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"RMS Contrast\"</b></h5>\n<div>\nTotal number of outliers: 2\n</div>\n<div>\nNon-outliers range: 21.65 to 99.35\n</div>\n<div class=\"BKWMNT-container\">\n    <div class=\"BKWMNT-row\">\n        <h5 class=\"BKWMNT-item\">RMS Contrast</h5>\n        <div class=\"BKWMNT-item\">99.41</div><div class=\"BKWMNT-item\">100.78</div>\n    </div>\n    <div class=\"BKWMNT-row\">\n        <h5 class=\"BKWMNT-item\">Image</h5>\n        <div class=\"BKWMNT-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"BKWMNT-item\"><img src=\"data:image/png;base64,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\"/></div>\n    </div>\n</div>\n\n<style>\n    .ODGEII-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .ODGEII-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .ODGEII-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .ODGEII-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Mean Red Relative Intensity\"</b></h5>\n<div>\nTotal number of outliers: 56\n</div>\n<div>\nNon-outliers range: 0.33 to 0.34\n</div>\n<div class=\"ODGEII-container\">\n    <div class=\"ODGEII-row\">\n        <h5 class=\"ODGEII-item\">Mean Red Relative Intensity</h5>\n        <div class=\"ODGEII-item\">0.33</div><div class=\"ODGEII-item\">0.33</div><div class=\"ODGEII-item\">0.33</div><div class=\"ODGEII-item\">0.36</div><div class=\"ODGEII-item\">0.37</div><div class=\"ODGEII-item\">0.38</div>\n    </div>\n    <div class=\"ODGEII-row\">\n        <h5 class=\"ODGEII-item\">Image</h5>\n        <div class=\"ODGEII-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"ODGEII-item\"><img src=\"data:image/png;base64,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class=\"ODGEII-item\"><img src=\"data:image/png;base64,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\"/></div>\n    </div>\n</div>\n\n<style>\n    .GUJYBA-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .GUJYBA-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .GUJYBA-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .GUJYBA-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Mean Green Relative Intensity\"</b></h5>\n<div>\nTotal number of outliers: 64\n</div>\n<div>\nNon-outliers range: 0.33 to 0.34\n</div>\n<div class=\"GUJYBA-container\">\n    <div class=\"GUJYBA-row\">\n        <h5 class=\"GUJYBA-item\">Mean Green Relative Intensity</h5>\n        <div class=\"GUJYBA-item\">0.33</div><div class=\"GUJYBA-item\">0.33</div><div class=\"GUJYBA-item\">0.33</div><div class=\"GUJYBA-item\">0.34</div><div class=\"GUJYBA-item\">0.34</div><div class=\"GUJYBA-item\">0.34</div>\n    </div>\n    <div class=\"GUJYBA-row\">\n        <h5 class=\"GUJYBA-item\">Image</h5>\n        <div class=\"GUJYBA-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"GUJYBA-item\"><img src=\"data:image/png;base64,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\"/></div>\n    </div>\n</div>\n\n<style>\n    .CGQNHP-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .CGQNHP-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .CGQNHP-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .CGQNHP-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Mean Blue Relative Intensity\"</b></h5>\n<div>\nTotal number of outliers: 36\n</div>\n<div>\nNon-outliers range: 0.32 to 0.34\n</div>\n<div class=\"CGQNHP-container\">\n    <div class=\"CGQNHP-row\">\n        <h5 class=\"CGQNHP-item\">Mean Blue Relative Intensity</h5>\n        <div class=\"CGQNHP-item\">0.28</div><div class=\"CGQNHP-item\">0.3</div><div class=\"CGQNHP-item\">0.31</div><div class=\"CGQNHP-item\">0.34</div>\n    </div>\n    <div class=\"CGQNHP-row\">\n        <h5 class=\"CGQNHP-item\">Image</h5>\n        <div class=\"CGQNHP-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"CGQNHP-item\"><img 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src=\"data:image/png;base64,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\"/></div>\n    </div>\n</div>\n</div>"}}, "6dbc337a35b94312895e8b3c27713122": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7d6e3aa951a34ec7bfaa136e7f2c1370": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1eb8b606dcc44acaa7a61b314f1744e7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8ceb62202e734b5888d1a2ba41654bc2", "style": "IPY_MODEL_27e761af664d45cb99cf86a5b633d055", "value": "<div><h5><b>Properties With No Outliers Found</h5></b></div>"}}, "8ceb62202e734b5888d1a2ba41654bc2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "27e761af664d45cb99cf86a5b633d055": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b3b4437a03074a6ba27069b62c8a66ad": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_872bdcee5a1a4fabb63d380bd0b00030", "style": "IPY_MODEL_1b15b83747f841a38dd88854cd3dc301", "value": "<style type=\"text/css\">\n#T_cfe18 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_cfe18 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_cfe18 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_cfe18 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_cfe18 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_cfe18\">\n  <thead>\n    <tr>\n      <th id=\"T_cfe18_level0_col0\" class=\"col_heading level0 col0\" >More Info</th>\n      <th id=\"T_cfe18_level0_col1\" class=\"col_heading level0 col1\" >Properties</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_cfe18_row0_col0\" class=\"data row0 col0\" >No outliers found.</td>\n      <td id=\"T_cfe18_row0_col1\" class=\"data row0 col1\" >Aspect Ratio, Area</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "872bdcee5a1a4fabb63d380bd0b00030": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1b15b83747f841a38dd88854cd3dc301": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f9a0b85191b2492cbbb569b76039e630": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9286a892066245bda68970091c1344b7", "style": "IPY_MODEL_869f7c2221e64fc192dc42d24e1782cb", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "9286a892066245bda68970091c1344b7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "869f7c2221e64fc192dc42d24e1782cb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "04e09e0c8d084232acdabb75c78c890c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "65aecbe9162141cebd51cd71103f6ae9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a51502f6dfbf4009affbe7e5637dfacc": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_a82469632a6b4a8386dcdc4371baec7a", "IPY_MODEL_3ee3b377780343f6a7a8df8301449d9c", "IPY_MODEL_f3bed9835d6c43ad8f73347e3e684c31"], "layout": "IPY_MODEL_76d6910d17a24ef4b4d78d6f0f55c943"}}, "a82469632a6b4a8386dcdc4371baec7a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c555cd5d945e4687a47c30f3fd2ee742", "style": "IPY_MODEL_b1e421867250484a96feb6b19ebfb246", "value": "<h4 id=\"ImagePropertyOutliers_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Image Property Outliers</b></h4>"}}, "c555cd5d945e4687a47c30f3fd2ee742": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b1e421867250484a96feb6b19ebfb246": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3ee3b377780343f6a7a8df8301449d9c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_662acf09000f4b539084ce26cd8ebba2", "style": "IPY_MODEL_8009029170cd45418c8ce4211dc5a761", "value": "<p>Find outliers images with respect to the given properties. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_image_property_outliers.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "662acf09000f4b539084ce26cd8ebba2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8009029170cd45418c8ce4211dc5a761": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f3bed9835d6c43ad8f73347e3e684c31": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_a1b8c7bf2ca248de9989ee16c75ab9fc", "IPY_MODEL_b452b193025d4621b7fe545d0e0ea5b5", "IPY_MODEL_26cc7d0626c541edbecc10e881fcdde5", "IPY_MODEL_050e76828e554b04a64ca30eac130159", "IPY_MODEL_55d456e03e954c02b7fb706d94844755", "IPY_MODEL_2cf7a34e5380405c84b98dec30f03052"], "layout": "IPY_MODEL_4bc6c5c5139c475c8497a0a1a3aef17b"}}, "a1b8c7bf2ca248de9989ee16c75ab9fc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6b789aa883b04099aca18715e1699bbc", "style": "IPY_MODEL_8486d29dac2340639482c5ecfb2d87b3", "value": "<h5><b>Additional Outputs</b></h5>"}}, "6b789aa883b04099aca18715e1699bbc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8486d29dac2340639482c5ecfb2d87b3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b452b193025d4621b7fe545d0e0ea5b5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b4ebaf3bdd9947039af9633ff5f75fb1", "style": "IPY_MODEL_bfc8291bd32842449a166dd90f3654fa", "value": "<div>\n<style>\n    .FDUDVB-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .FDUDVB-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .FDUDVB-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .FDUDVB-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Brightness\"</b></h5>\n<div>\nTotal number of outliers: 156\n</div>\n<div>\nNon-outliers range: 198.64 to 250.88\n</div>\n<div class=\"FDUDVB-container\">\n    <div class=\"FDUDVB-row\">\n        <h5 class=\"FDUDVB-item\">Brightness</h5>\n        <div class=\"FDUDVB-item\">161.29</div><div class=\"FDUDVB-item\">165.13</div><div class=\"FDUDVB-item\">165.12</div>\n    </div>\n    <div class=\"FDUDVB-row\">\n        <h5 class=\"FDUDVB-item\">Image</h5>\n        <div class=\"FDUDVB-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"FDUDVB-item\"><img 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src=\"data:image/png;base64,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\"/></div>\n    </div>\n</div>\n\n<style>\n    .KEVCMQ-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .KEVCMQ-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .KEVCMQ-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .KEVCMQ-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"RMS Contrast\"</b></h5>\n<div>\nTotal number of outliers: 8\n</div>\n<div>\nNon-outliers range: 17.67 to 99.85\n</div>\n<div class=\"KEVCMQ-container\">\n    <div class=\"KEVCMQ-row\">\n        <h5 class=\"KEVCMQ-item\">RMS Contrast</h5>\n        <div class=\"KEVCMQ-item\">101.57</div><div class=\"KEVCMQ-item\">101.91</div><div class=\"KEVCMQ-item\">101.91</div>\n    </div>\n    <div class=\"KEVCMQ-row\">\n        <h5 class=\"KEVCMQ-item\">Image</h5>\n        <div class=\"KEVCMQ-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"KEVCMQ-item\"><img 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NLGA3xJHOw0kgZOe+/8AvZarcH1yqO72SyW+zU2ihj3cPNKcFz/TJC2S59031zSUdkipri2aSaDyN0DJc0DYnJA9vosyLqy/Vseu32LUAd3EOIx29PZUTRzmsaXOcGgcknCKJU9nvt7pyzqGs8GmcARBC1ocSNxnbARBL1Eet+no62jkuNNE41cQGoD+tg529d8/RS5eII30Vev4lbBTTE/maUBjiT+tvY/4WP1z03Jd4GVdEzVVxDSWjHnbz+2/3WpvtFUdK3aO52x/8mZ5yzGGt76DvuOfjHqpvbLjTXSiZVUj9THcg8tPcEeqDhOHMLmOHysd3Jxwug9f9NNpy24UETtMjj4zRk6XHcH2Hb7KCxsBDmnnGyyMM5zsr0ZOlJYizCowcBUZDvM3V3HKtFescAV67Y57HhBSqhuMj6hUkZOFXjSNjzypRQdiVW1+SB3VDsYyDuqQ4h2UGa5gazPdHnMTdv0ndeCQPj5wQPurOo5x/pUwCHHzAbZVOcODjwsluTEcDzN5AWO4bZCDzYnI+iymzOi0uA4CwgTlX5T5APZUYcshe8uJ3JXsbsbFWzsdwgWr0Z1NWGlqo54w0vjcHDUMjIOeF1vo3qn+PslhmphFNEM/ywdGnt8FcXAydl1P8Lbc6KhqK95ePEd4bRnYgckj5KCfIiICIiDEudDHcbfNSS/plbjPoexXOqKtrejL7JS1QEkDsGQM4eMbOaT3G4+4XUFpuoun6e+Uul2mKobjRNoyRvx8INoDDV0wILZIZmZBByHNI/sQuX9X9Iy2uV1Zb2ufRnc43MXsfb3/ANO36Ku9RRXOaxXOUjRlkQe4eRzSctzn7cqdyxRzxOilY18bxhzXDIITBwCQgx4k/V2WNup91X0TPDLLWWxjZKcnPhN2czPYDuOMY3UH0+G9zJWYOcfBWRYHr3VxpyMHb0Xj4iDkbhViNwaCU0GtxzyvCcfC9z6qgnVuUFJ3XgHbhekZ4Xnv3CoqY7DsZ2V0AE7lWc5GVUCTyoM1rhCBJHzjcFY5OW59VQ7IAOdj29F7GdjlBQ3aT2Vyc8eypaMyAquo2duM7J6MYjVvpQNaechenJ4VQGOSgu0zGCZo05yRyu9WukhordDBTxeFG1uQ0kkjO+5O/wB1w+005rLpS0odp8aVserGcZOMrvETDHExjnueWtALncu9yrBWiIqCIiAiIggP4i2UYZd6dnmJDJ/fs137Y+3ot70bfP4zacTH/wBVTYjl/wCrbZ313W8qII6mnkgmaHRyNLXA9wVy250lR0f1NHNTFxgc7XHgnD2Z3YfUj69ig6uon1X0pbq6mkrGmOkkiYXOcGgNdxz9v3W3Z1BbHWltxNS1sJHBPmzj9OPVc+6i6qqLtLLFTyvioeBHsC/jnHuOFKIx4Qj2zlUSuAH9gpZ0x0rU3l8dXVMEdADg5JDpB/0/XutzdPw7hnlL7fUiIZ2ZICcDA7/Of2WcVzZrW6ck5PoqHMx2W3uFguVqkLKqnew9iBkH4PB5Wv8ADcD5uFUYwblUkbrJMXcLzRvugsAd1UBhXNG+COV4RjlB6WtMeQd1THyQqSSPhIv+ZygqwQ8AeqqqCQ/C9IHiD5XlQcSZRVsOA4GF5jKai4+yqYx0kga0EuJwAERKOgLS+4X1k5DhFSlspcCBgg7D3zj/AHv19aTpSxssdpZCc+PIA+c5yNWO3xwt2tAiIgIiICIiAtferTT3igkpp2tDnNwyTTksPqFsEQchufSd8ppXQsppZog4hj426tQHfAzj4K3/AE10K+KSKsuxALHZFPgHj/7Hj6f+FP0QUsY2NjWMaGtaMAAYACqREHhAIIIyDyFpLn0parlI2SSIxOaA3+ThoIz6Y9+VvEQQWX8O4y0+FcCDnbVHnbf0Px+6j126SudtYZZIBLEOZITqAG/I5HHx7rra8UwcIMIBwRj5VuSEEeXkLp9+6KguNRJUUcwpnvGSzTlpdnn27/X6rBZ+H7m0TR+eb+YJy7+XlnwO6mDmj4y3nhUM8rx8rcXq2VVrq3wVkRaRwQDpcPUEjce6025OfQorKx5gcd1aqR5lkMIcAqvyc9XVNip4nPc4gBrRkkqT6MGNjnkNaCSdgAF0/ofpB9A9tyukRbUD/lROwdOw8x9DyszpDpCG1wx1VwgY6vB1DJz4f0zjPv8ACly1IgiIqCIiAiIgIiICIiAiIgIiICIiAiIgIiIMK6Wuiu1P4FdA2Ro3aeC045BWkb0JY2vefClIc3ABf+k5zkH9lKETByfq/pqS2XAy0UDvybw0Nx/S45GP2JUq6AtE9voZaitgdFPKcAPZhwb85zv6YClyKYCIioIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIg/9k=\"/></div><div 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src=\"data:image/png;base64,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\"/></div>\n    </div>\n</div>\n\n<style>\n    .IFDCRJ-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .IFDCRJ-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .IFDCRJ-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .IFDCRJ-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Mean Red Relative Intensity\"</b></h5>\n<div>\nTotal number of outliers: 414\n</div>\n<div>\nNon-outliers range: 0.33 to 0.34\n</div>\n<div class=\"IFDCRJ-container\">\n    <div class=\"IFDCRJ-row\">\n        <h5 class=\"IFDCRJ-item\">Mean Red Relative Intensity</h5>\n        <div class=\"IFDCRJ-item\">0.32</div><div class=\"IFDCRJ-item\">0.33</div><div class=\"IFDCRJ-item\">0.32</div><div class=\"IFDCRJ-item\">0.4</div><div class=\"IFDCRJ-item\">0.4</div><div class=\"IFDCRJ-item\">0.4</div>\n    </div>\n    <div class=\"IFDCRJ-row\">\n        <h5 class=\"IFDCRJ-item\">Image</h5>\n        <div class=\"IFDCRJ-item\"><img src=\"data:image/png;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA0JCgsKCA0LCgsODg0PEyAVExISEyccHhcgLikxMC4pLSwzOko+MzZGNywtQFdBRkxOUlNSMj5aYVpQYEpRUk//2wBDAQ4ODhMREyYVFSZPNS01T09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT0//wAARCADIAMgDASIAAhEBAxEB/8QAHAABAAIDAQEBAAAAAAAAAAAAAAYHAwQFAgEI/8QAPhAAAQMDAgQEAwYDBQkAAAAAAQACAwQFEQYhEjFBURMiYYEHcZEUMkKhsdEVwdIXJFJy8SMzQ2JzgrLh8P/EABgBAQEBAQEAAAAAAAAAAAAAAAABAgME/8QAHREBAQEBAQEAAwEAAAAAAAAAAAERAgMSBCFBMf/aAAwDAQACEQMRAD8As5ERAREQEREBERAREQEREBERAREQEREBERAREQEREBERBp1F0oKWbwZ6qJkmCeEu3GBnfslJdKCsdw01VE92cYzgn5A81Ab/ACMqb3VSwuyxz8A98AD+SwUM8tHVx1MQaZIzlvEMjlj+a7Ty2a530yrRRQZmp6g1cU1bIWwwgu8OEY8Q4wAcnl+y51T8QLu2Uvp6CF8Acd+Ek4+qxeLGp1KspFF7Lri0XQ+G+X7NKMeWXbJPZSdYafUREBERAREQEREBERAREQEREBERAREQEREFYyQOikdHMxzHsOC0jcI1gypPqe0/euEAcTt4refpxKMA+q93j1Oo8/fOMU8HE0g8is1urIbdHKx1upqpz8cLphng27YX3n1Xl0bccl178pWZbHIq6KmqoA4gwzxgkPbvxnPLYbe+V2NL6xqrfMygu/E+JxwyTc4AHT8tlqzQgbt5rD4FNJTzQ1LHNldgxyjfGAdiOx7jcLx98Y6c9LbY5r2Ne0gtcMgjqF6VV2a93PS1X9kr2OkppQHBr3d+RB5cvYqa1mp6eO3QVdE1s4leW8DncLm89yNz0XL5rr9R30XKtV+pLk8xtDopukb+ZHcYXVWbMXRERARRPUGtGWW8CgNGZRgFz+PGMjPLHZSmJ4kibIBgOAI90HtERAREQEREBERAREQEREHxRy96cZM2SpoARMSXOjzs/qcdj+SkiK89Xm7Es1WM8FTRu4amGSM5xhzcfmvgnZ1KsuaGKeMxzRskYebXDIK58un7TM/idRMBxjDCWD6Ar0z8m5lc75oCXtcSVifuN/8ARTOr0hSSS8VLO+nb1ZjjHtk5/VfaPSNFEXmrkfU8XIbsDfoVOvXmk4RujozqG1z26SJrnUjOOCTfIyclm22/Tso+wT0NS+nqY3Mew4c1wwQrigp4aaIRU8TIoxyawYC0LtYaC7AuqYsTBha2VpwW/v7rlO8rV5/Su46oABzHEOG4IOCFIaHWzogyOvg8QDYyMOHY9RyP/wBsuXXaHvEVQRRyRTRcw4v4D7grVm0vfKYAyUhkBBOYnB2Md0vU6JLFgW/UVquDiyCqaHj8EnkJ+WfmuoqMqGlhyTwkHZazq6pbymefm4qfC/SQfEisbU6lYIuHEEQYHNdni6n6Zx7Lp23X1dBb2RyULat0ezpPE4NugwG9BsoJI50gy7cHn6LJSSGOOSOVzhE7q3mpiatnTmsqW81LqWeIUtQf920v4g/0BwN/RSdfn+LijIkikxwuy12cHKl1Fr28wQRwvipphGAC94cXO9SeLmpjUq00Vfn4kuHO1DP/AFz/AErQf8SbjxO4aOmAzsC1x/mpi6s9FVo+I95O5o6MA9eF39S+/wBot5B81NQgf5Xf1K4mrRRVpF8Qbq/70FEP+x39S8O1leC4uFW1oJ5CJuB9QmGrORQGk1zWBwE8EErQ3G2WEnvncfkpDatT0dxnbTlj4JXDy8ZGHHsCmU13UXxFFfUREBERAREQEREBfCuVfL9TWaMeKx80rhkRMI4sb779MjCra+a1utxaYQ4UsXIsjyOL5nmia0r3Oay41Ez2hpMrieEbDdcWVp4vIchH1cm/E47814kPE0FmcreoykFrBxbE8keP7vlpw4Hks7eGSOLiHmbyXiXBgft5uLmoMLeEgGQZHotmOIfgad14hpw7Azs79VtUTnMkfFJ99hwVYmvscUsZDscu4Wfx6gnhYyFre5YFvwPic0B2HA/j7LFUtY8lrMYHNh6+q6zy1n6c11C97i7xWOPMhpWJ1Ng+UOPfK6MNIHnijzE4dCea6kTYnQmORoZJjY91ueNZveIzHF03W0xmGrpOozH5nNDh6Lx4DHNywYPZOvGxee9a0THDdbEbng9dllijIGCFk4WhZnBqRaY1FNFUR0Na58sUjgyNxOSwnYD5foi5VhpJau90whaSI5GyOONmgHO/0x7ouHUyukqzkRFloXD1Xff4DbBO2MukldwMPRpxzK7ijHxBoftmmJ5AcOpv9qPXoUETtvxHroJ3/wARjFSwjDQMM4T32G6nNm1RaryAKacMlP8AwpNne3Q+yjFq0bar7pukq3OfDUyRniew5Gc9R8vkuFdNC3i1ubJRn7U3BcfCBy3CIt1RzV2qGWCJkUbBJUzNJbk7MHIEjrv+hUFtWsbvZ4n0kx42jIAmaSWH9fYrm1d5krauSa4NFQ6T7xd0Hp2VkNaVVda2pqHzVFQ9z3HOSeaxurI5mcNS3zdHhZKuCllidNQSHEY88ch8w+XdaMAa6piEuOEuGVUGBpnDX8lttkg8JwGMha1X4YnJhJ5rCAeL5qLjLE9/itOdsrI6YOMjR1K12+UnHRGnzZUl/a42m+Jw5BK++NIC14GXs5n/ABBZYN2jKk+m9GS3qF9XUTGnp/M2MhuXPd0PYtz+nurrOOJRmWRwNI18hfzY1pdn6LeY17ZjFUwmORhwWuGCPZZbFUT6V1WaepBjjc4sfxs3LCeeOe+AQrWfT0ldADLBHKyRoPnZzHMc1rn0sPmKnlcw+VhII6oHl7AzII7qy6jTlmmjw+ghYAc5jHAfqFBaW20921NLR21zm0bXE+KAXBrcbc+5/VdufesdcPAfiMDOQsTpIwdgpjFoukZI/wASsnfGfuNAALfmev0C3RpWzhoBp3OwNyZXb/mnX5GpPOxXEk/CTjAXQtlluV0c0xQlkTgT4sgIb/7Vi09rt9MXGnooIy7nwxjdbYAAAAwAuN7rpOXMslmgtNPhmXTPA8R5OcnsPTOUXURYaEREBcjVQ4tNV4PWIj8wuusVTC2pp5IJPuyNLT7oIx8Nn8elIz1ErgfYAKVkgDJ2A6qFfDJzmUFwpi4lkFSQ0HpnJ/ZdfWd1farDLJC0OllPhtBGefM/RBC9fVP8TuQjpGReDS8TS8c3uOM5+WMf6qFytfF5XtLfmvTpXYPnIBOSF9fVlzPCl8w6Dst4wwwtEtTHGNmlwz8l5mY2Kd/huDgHENwvPC6N4Od1lp4HSuPDz6LNrcjzHGXHB5lZ3U5jbnqpjo/Rs1XUsqrnBJHSNHEA7YynbAxzxvz9lILzodtfcjNSzxUtP4LWiMMzhw2AAGMDGN8k5UVVDmkNOQvkTM810aqmMc74njBY4tPzBwp/8P8ATtI2iF1nayaSXLY2uZtHgkE+udumylRx9EaWFzcau4MeKRmOBpaQJs5GzgehA+atGNjIo2xxtDWNGGtAwAOyNa1jQ1jQ1oGAAMAL0rBEfiFY3XK0faoMiaky/hZHkvzjr6AE9V6+Ht2NwsxhlfmWA4wc5A/1z+SlbgHNLTyIwquqWO0TrFkkJlFuqMZPD06t358v0QWBqC4G12Wpq2ta9zAA1rjgEkgfzz7LifD2iZFaZazJMlQ8tPoG7D9SuHra6i519LRUpcWMALmkfjdjtz2IU+tlGygt0FKwACNgBwc5PU/VE/rbRERRERAREQEREBfF9RBAdCRvpdT3qkkcOKMgEA7Eh2Mhe/iVPH4dPTvqRGAxzyzO53ABx9fzXimkipPivVxhmBOwABo/EWgk/qorr2rZXanqTG48MLvD3PLAAPtkFXn/AFK5c1ueIeOJ7JQRnyncLm9eWT6r1xEfcJHut+qgiFBFPxAzPOMt5Y/da7rMaHC5++5d1Uu0npevr6imq2NEdKyQF8jjyIw7AHM9N+S1dH2g3e4tpZI3eC08csjeje2e5VyU8MdNTxwQMDIo2hrGjkAOQWG3tF9RBWnxAtbKO6x1kWA2rBLm9njGT75H5rrfDapc6krKUkcMb2yN338wIPt5R9V0dc24VtidO1hdNSnxGkc+H8Q+m/sFDdE1oo9SQte8NZO0xOJGc53A9NwFP6LWRfF9VBRvXNmZdrBKcPMtODIzhPyzt12GfZSRRbXtfPS2mOCAPb47jxSNyOENwcZHLJI+nqggGjHtqdR0LaiYRPilBJdydw7ge+Me6udVBYPs2XMrYhwu2bIBuw/sp5pq/wAdW51vqZR9pjJDHchI3pj1/VXE1JERFFEREBERAREQEREFcXiWKg+J0VXK8Ojw1z+Dct8uNx36/IhQKtc011QCMtLznHZSnXc8kesZ3A/ca0N25DhH7lRTaSVxfzcc5ViV6mpJGxMeW8MbvuuPJSDR2mpL/M4SyFlHAcvcOZJ5ALDpy2z3uvitc8hbTDLy4cwAOiuC30FNbaOOko4xHEwbDqT3PcpbpIW+30ttpWU1HE2ONgxsNz6k9StpEUUREQeJWCSJ7CSA4EEj1VKVcD7Xc3xysJNPLjhzuQDt+SuxzmtGXEAepVa/EiniiucVXHKwmaMB7Q/JBG2cdBj9CpRZEEniwRy4xxtDsdsjKyLnaenfU2ChnkIL3wNJIGOi6KoLDVU0FZTvp6mNskT/ALzHcjvlZkQV1qLTE1qY6roXmSlaMv4yOJm+N+435/VRtshEolZlkrdwAcZ+RVzuaHtLXAFpGCCNiFDdQ6NbKJqu2uw8kvMGNuXJuBzz09UTHqxayDw2G5jAAwZ2g5H+YfzCl8cjJWB8b2vY4ZDmnIKph4mp5y14cyRhw4kbg9iFI9Lag+wT+DIMQvOXsH4eXnHpjOyirHRYqeeKpgZPA8PjeMtcOoWVUEREBERARF85IKd+ITiNW1eP+T/watLTlhqr9WCKHDGM3kkO/CO+MjKk+v7TFLeoZqdwfPUhoMefxbNHyB2+hU9tdvp7ZQxU1PG1vC0Bzg0AuPc+qDVsmn6CyR/3SMmVzQ18rzlzv2Gd8LrIiAiIgIiIOLqawNv1JHH9odBJE7iaccTTnnkdfn+6gj9AXt1SafihMROfG4/L9Of5furVRTBq2yjbb7dT0bXl4gjDA4jGcLaRFQREQF8X1EHEvunKa7jxAfBqAMB4GQfmOqgVRZprdW+DXxlmRljmnY/Iq2Fr1lHT10DoamMPaR15j1B6IliFWXVItbBR1bHSQM+65mOJg7Y6qY2650lzg8ajlDwNnNOxaexCgGobA+1VIeHB8EpPAeo9D7dVy4H1VDO2oo5XRvH4moLfRcjTt5ju1CC5zRUsGJWDb3HoiK66IiAo/rO5fw+yPYzi8WpzEwjG225PspAoVrG0V9deaWSGKR8D2ti4mguEZ4tyQOQwQc+iDnaMtb7lWisqmOdTU+OEuzhzxjA9tvyVirRstsitNtjpIty3d7v8TupW+gIiICIiAiIgIiICIiAiIgIiICIiDBWUkFbTPp6lgfG7mO3qPVVvcrfUWqqdT1Ay07teOTh3Cs9cjUtBFW2iZz9nwNMjHdsDf6hBXVJVS224RVNM4hzHZxnGR1B9Ciy2ah/il3ipSXtjdkucwZLQAd/rj6ogtdERAREQEREBERAREQEREBERAREQEREBERAREQF5c1r2lrwHNcMEEZBC9IgwU9HTUznup6eKIv8AvFjQMos6ICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiD//Z\"/></div><div class=\"IFDCRJ-item\"><img 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\"/></div><div class=\"IFDCRJ-item\"><img src=\"data:image/png;base64,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\"/></div>\n    </div>\n</div>\n\n<style>\n    .TWKAUD-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .TWKAUD-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .TWKAUD-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .TWKAUD-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Mean Green Relative Intensity\"</b></h5>\n<div>\nTotal number of outliers: 225\n</div>\n<div>\nNon-outliers range: 0.33 to 0.34\n</div>\n<div class=\"TWKAUD-container\">\n    <div class=\"TWKAUD-row\">\n        <h5 class=\"TWKAUD-item\">Mean Green Relative Intensity</h5>\n        <div class=\"TWKAUD-item\">0.32</div><div class=\"TWKAUD-item\">0.32</div><div class=\"TWKAUD-item\">0.32</div><div class=\"TWKAUD-item\">0.34</div><div class=\"TWKAUD-item\">0.34</div><div class=\"TWKAUD-item\">0.34</div>\n    </div>\n    <div class=\"TWKAUD-row\">\n        <h5 class=\"TWKAUD-item\">Image</h5>\n        <div class=\"TWKAUD-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"TWKAUD-item\"><img 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\"/></div><div 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src=\"data:image/png;base64,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\"/></div>\n    </div>\n</div>\n\n<style>\n    .GONYZI-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .GONYZI-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .GONYZI-item {\n      display: flex;\n      min-width: 200px;\n      position: relative;\n      word-wrap: break-word;\n      align-items: center;\n      justify-content: center;\n    }\n    .GONYZI-title {\n        font-family: \"Open Sans\", verdana, arial, sans-serif;\n        color: #2a3f5f\n    }\n    /* A fix for jupyter widget which doesn't have width defined on HTML widget */\n    .widget-html-content {\n        width: -moz-available;          /* WebKit-based browsers will ignore this. */\n        width: -webkit-fill-available;  /* Mozilla-based browsers will ignore this. */\n        width: fill-available;\n    }\n</style>\n<h5><b>Property \"Mean Blue Relative Intensity\"</b></h5>\n<div>\nTotal number of outliers: 268\n</div>\n<div>\nNon-outliers range: 0.32 to 0.34\n</div>\n<div class=\"GONYZI-container\">\n    <div class=\"GONYZI-row\">\n        <h5 class=\"GONYZI-item\">Mean Blue Relative Intensity</h5>\n        <div class=\"GONYZI-item\">0.28</div><div class=\"GONYZI-item\">0.28</div><div class=\"GONYZI-item\">0.28</div><div class=\"GONYZI-item\">0.34</div><div class=\"GONYZI-item\">0.34</div><div class=\"GONYZI-item\">0.34</div>\n    </div>\n    <div class=\"GONYZI-row\">\n        <h5 class=\"GONYZI-item\">Image</h5>\n        <div class=\"GONYZI-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"GONYZI-item\"><img 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src=\"data:image/png;base64,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\"/></div>\n    </div>\n</div>\n</div>"}}, "b4ebaf3bdd9947039af9633ff5f75fb1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bfc8291bd32842449a166dd90f3654fa": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "26cc7d0626c541edbecc10e881fcdde5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c0991c5651944a99b52f0a42b8ddef9b", "style": "IPY_MODEL_59526f2b549e4e588f21096a4b7c4528", "value": "<div><h5><b>Properties With No Outliers Found</h5></b></div>"}}, "c0991c5651944a99b52f0a42b8ddef9b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "59526f2b549e4e588f21096a4b7c4528": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "050e76828e554b04a64ca30eac130159": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a5b3f0bf1408451882660081a5590cac", "style": "IPY_MODEL_f7d3f9ab3bbd447aba1ba0a6ca4e1693", "value": "<style type=\"text/css\">\n#T_60ac4 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_60ac4 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_60ac4 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_60ac4 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_60ac4 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_60ac4\">\n  <thead>\n    <tr>\n      <th id=\"T_60ac4_level0_col0\" class=\"col_heading level0 col0\" >More Info</th>\n      <th id=\"T_60ac4_level0_col1\" class=\"col_heading level0 col1\" >Properties</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_60ac4_row0_col0\" class=\"data row0 col0\" >No outliers found.</td>\n      <td id=\"T_60ac4_row0_col1\" class=\"data row0 col1\" >Aspect Ratio, Area</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "a5b3f0bf1408451882660081a5590cac": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f7d3f9ab3bbd447aba1ba0a6ca4e1693": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "55d456e03e954c02b7fb706d94844755": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e10d95ef35ad425bbab58e094421a698", "style": "IPY_MODEL_5d1c845bbf3d4f30a724514dbcf9106c", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place, check used 5000 images from the original dataset. Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "e10d95ef35ad425bbab58e094421a698": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5d1c845bbf3d4f30a724514dbcf9106c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2cf7a34e5380405c84b98dec30f03052": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c89d0d4439224c02a8f0a8fdf40a8b2b", "style": "IPY_MODEL_70c7edb0333645229d4757b44370c7a6", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "c89d0d4439224c02a8f0a8fdf40a8b2b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "70c7edb0333645229d4757b44370c7a6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4bc6c5c5139c475c8497a0a1a3aef17b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "76d6910d17a24ef4b4d78d6f0f55c943": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3ad0afb16a2748be901bddc3b01b1705": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_aa648c562cbc43cdbe2ed43c45ab34f6", "IPY_MODEL_0fa3a3a865d84560b8c397697fdd2be5", "IPY_MODEL_11d8a3320e304dd78c7aabb862b4fe2b"], "layout": "IPY_MODEL_0a0f7196167e40fc8f40b31098a9b973"}}, "aa648c562cbc43cdbe2ed43c45ab34f6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ea745bf52a0c4032864896dac3945a2b", "style": "IPY_MODEL_aef6fd3a50f142d781194c82741bc19e", "value": "<h4 id=\"PropertyLabelCorrelation_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Property Label Correlation</b></h4>"}}, "ea745bf52a0c4032864896dac3945a2b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "aef6fd3a50f142d781194c82741bc19e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0fa3a3a865d84560b8c397697fdd2be5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d1ddfbcf5b8f496e933e69c4742023c1", "style": "IPY_MODEL_c568fcfd62584f5cabd9789b1467418a", "value": "<p>    Return the Predictive Power Score of image properties, in order to estimate their ability to predict the label. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_property_label_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "d1ddfbcf5b8f496e933e69c4742023c1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c568fcfd62584f5cabd9789b1467418a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "11d8a3320e304dd78c7aabb862b4fe2b": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_06666464f62a48dba355d24163b73a64", "IPY_MODEL_5b1f9e933aab49fdb32aa146aec7985e", "IPY_MODEL_f33095a308564f689b4508c2baf0930e", "IPY_MODEL_ca78e32226ab41aa94d01f3e08f0e5b4", "IPY_MODEL_c99de27487cf4d998dacbbfc00a7ab54", "IPY_MODEL_3a2e2cb3dbb34f7990ce9ab9b7501b5e", "IPY_MODEL_fad0c2e71cae47d891091fe461a05fd0"], "layout": "IPY_MODEL_ef91047e895841629fe0371374541207"}}, "06666464f62a48dba355d24163b73a64": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ce3024d063be421ea662b7035e8b9b0b", "style": "IPY_MODEL_9f770e5786844cdc9a1dec62aeee35d9", "value": "<h5><b>Additional Outputs</b></h5>"}}, "ce3024d063be421ea662b7035e8b9b0b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9f770e5786844cdc9a1dec62aeee35d9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5b1f9e933aab49fdb32aa146aec7985e": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "#818800"}, "name": "Test", "text": ["<b>0.12</b>", "<b>0.12</b>", "<b>0.08</b>"], "textposition": "outside", "x": ["Mean Green Relative Intensity", "Mean Red Relative Intensity", "Mean Blue Relative Intensity"], "y": {"dtype": "float64", "shape": [3]}, "type": "bar", "uid": "8fbf5ddd-7b31-420f-8127-4520436129de"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"barmode": "group", "height": 500, "legend": {"x": 1.0, "y": 1.0}, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram": [{"marker": {"pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "histogram"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Predictive Power Score (PPS) - Can a feature predict the label by itself?"}, "xaxis": {"range": [-3, 5], "title": {"text": "Column"}, "type": "category"}, "yaxis": {"range": [0, 1.05], "title": {"text": "Predictive Power Score (PPS)"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "Z23UoLBvvj9c3EUq7tC9PwJKQjWBG7Q/"}]}, "f33095a308564f689b4508c2baf0930e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c4267e6bb28d4d9c9e091e8afe717cac", "style": "IPY_MODEL_3c3ddc541dfd43ac9888802f4fd481c4", "value": "<div>The Predictive Power Score (PPS) is used to estimate the ability of an image property (such as brightness)to predict the label by itself. (Read more about <a href=https://docs.deepchecks.com/stable/vision/auto_checks/data_integrity/plot_property_label_correlation.html target=\"_blank\">Predictive Power Score</a>)</div>"}}, "c4267e6bb28d4d9c9e091e8afe717cac": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3c3ddc541dfd43ac9888802f4fd481c4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ca78e32226ab41aa94d01f3e08f0e5b4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7a621147caaf47e3a7e0359f61624803", "style": "IPY_MODEL_16499d3084eb4b48bd83451c507a5f0b", "value": "<div><u>In the graph above</u>, we should suspect we have problems in our data if:</div>"}}, "7a621147caaf47e3a7e0359f61624803": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "16499d3084eb4b48bd83451c507a5f0b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c99de27487cf4d998dacbbfc00a7ab54": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c296efb5375d4a00ad530c2a9a4cc91e", "style": "IPY_MODEL_fb0f62d356ee4c1da1114731309d4449", "value": "<div><b>Train dataset PPS values are high</b>:</div>"}}, "c296efb5375d4a00ad530c2a9a4cc91e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fb0f62d356ee4c1da1114731309d4449": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3a2e2cb3dbb34f7990ce9ab9b7501b5e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2fae068fbbfa4f998ceaf6712f98516c", "style": "IPY_MODEL_2a5e6bd941d04cdb9356b19205131131", "value": "<div>   A high PPS (close to 1) can mean that there's a bias in the dataset, as a single property can predict the label successfully, using simple classic ML algorithms</div>"}}, "2fae068fbbfa4f998ceaf6712f98516c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2a5e6bd941d04cdb9356b19205131131": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fad0c2e71cae47d891091fe461a05fd0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_08e445d7f837406592194c1ebed1179e", "style": "IPY_MODEL_64636a60ff8145d1940a9a04b9a75c0a", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "08e445d7f837406592194c1ebed1179e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "64636a60ff8145d1940a9a04b9a75c0a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ef91047e895841629fe0371374541207": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0a0f7196167e40fc8f40b31098a9b973": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6d8314d3093c4ba2b6ec0a3284a227e6": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_4dcb9d0fac304611b5a8e9de389fb8ef", "IPY_MODEL_6f33fd36a6fc4a00a007d8c0636a403b", "IPY_MODEL_0a4ef1138993409eab00d65b6f8a4483"], "layout": "IPY_MODEL_0996284b46c84798bd17e26971b3112c"}}, "4dcb9d0fac304611b5a8e9de389fb8ef": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5d19fc0cbac6438980bfcae7f976ad9a", "style": "IPY_MODEL_033d54c7326f4976a7c1dfe0fc069e05", "value": "<h4 id=\"PropertyLabelCorrelation_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Property Label Correlation</b></h4>"}}, "5d19fc0cbac6438980bfcae7f976ad9a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "033d54c7326f4976a7c1dfe0fc069e05": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6f33fd36a6fc4a00a007d8c0636a403b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4ecc97dca14b4615aa235604a7cd4957", "style": "IPY_MODEL_feb96cf6281d40c28dea7dd38dcf902c", "value": "<p>    Return the Predictive Power Score of image properties, in order to estimate their ability to predict the label. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/data_integrity/plot_property_label_correlation.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "4ecc97dca14b4615aa235604a7cd4957": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "feb96cf6281d40c28dea7dd38dcf902c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0a4ef1138993409eab00d65b6f8a4483": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_220bf5a724da44dfadbd9703582c4ee3", "IPY_MODEL_43a67538dcab44ce80cf62c6ddc7df0f", "IPY_MODEL_c6864c2e911f41ff9f50ad9cad26914b", "IPY_MODEL_f2ff305710e547378169d8ac7396e471", "IPY_MODEL_fb07a14413e14d2786cebcfeb74874ab", "IPY_MODEL_800ccd36f29c4d70b4ba1136f9e06f32", "IPY_MODEL_268b22df556c4ab49ba1b2c74677a1d1", "IPY_MODEL_244c884bf7e6411895eb44a3a166e002"], "layout": "IPY_MODEL_9815194c89d9402c9b853ebfd23a5f1b"}}, "220bf5a724da44dfadbd9703582c4ee3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d1560c1575254e96bd58a39d197ea4f4", "style": "IPY_MODEL_1b126411f35344c894b450d07d4d6c10", "value": "<h5><b>Additional Outputs</b></h5>"}}, "d1560c1575254e96bd58a39d197ea4f4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1b126411f35344c894b450d07d4d6c10": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "43a67538dcab44ce80cf62c6ddc7df0f": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "#003b47"}, "name": "Train", "text": ["<b>0.14</b>", "<b>0.13</b>", "<b>0.1</b>"], "textposition": "outside", "x": ["Mean Green Relative Intensity", "Mean Red Relative Intensity", "Mean Blue Relative Intensity"], "y": {"dtype": "float64", "shape": [3]}, "type": "bar", "uid": "3380d763-cd34-494e-8d79-641be20f978d"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"barmode": "group", "height": 500, "legend": {"x": 1.0, "y": 1.0}, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "barpolar"}], "bar": [{"error_x": {"color": "#2a3f5f"}, "error_y": {"color": "#2a3f5f"}, "marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "bar"}], "carpet": [{"aaxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "baxis": {"endlinecolor": "#2a3f5f", "gridcolor": "white", "linecolor": "white", "minorgridcolor": "white", "startlinecolor": "#2a3f5f"}, "type": "carpet"}], "choropleth": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "choropleth"}], "contourcarpet": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "contourcarpet"}], "contour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "contour"}], "heatmapgl": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmapgl"}], "heatmap": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "heatmap"}], "histogram2dcontour": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2dcontour"}], "histogram2d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "histogram2d"}], "histogram": [{"marker": {"pattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}}, "type": "histogram"}], "mesh3d": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "type": "mesh3d"}], "parcoords": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "parcoords"}], "pie": [{"automargin": true, "type": "pie"}], "scatter3d": [{"line": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatter3d"}], "scattercarpet": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattercarpet"}], "scattergeo": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergeo"}], "scattergl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattergl"}], "scattermapbox": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scattermapbox"}], "scatterpolargl": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolargl"}], "scatterpolar": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterpolar"}], "scatter": [{"fillpattern": {"fillmode": "overlay", "size": 10, "solidity": 0.2}, "type": "scatter"}], "scatterternary": [{"marker": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "type": "scatterternary"}], "surface": [{"colorbar": {"outlinewidth": 0, "ticks": ""}, "colorscale": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "type": "surface"}], "table": [{"cells": {"fill": {"color": "#EBF0F8"}, "line": {"color": "white"}}, "header": {"fill": {"color": "#C8D4E3"}, "line": {"color": "white"}}, "type": "table"}]}, "layout": {"annotationdefaults": {"arrowcolor": "#2a3f5f", "arrowhead": 0, "arrowwidth": 1}, "autotypenumbers": "strict", "coloraxis": {"colorbar": {"outlinewidth": 0, "ticks": ""}}, "colorscale": {"diverging": [[0, "#8e0152"], [0.1, "#c51b7d"], [0.2, "#de77ae"], [0.3, "#f1b6da"], [0.4, "#fde0ef"], [0.5, "#f7f7f7"], [0.6, "#e6f5d0"], [0.7, "#b8e186"], [0.8, "#7fbc41"], [0.9, "#4d9221"], [1, "#276419"]], "sequential": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]], "sequentialminus": [[0.0, "#0d0887"], [0.1111111111111111, "#46039f"], [0.2222222222222222, "#7201a8"], [0.3333333333333333, "#9c179e"], [0.4444444444444444, "#bd3786"], [0.5555555555555556, "#d8576b"], [0.6666666666666666, "#ed7953"], [0.7777777777777778, "#fb9f3a"], [0.8888888888888888, "#fdca26"], [1.0, "#f0f921"]]}, "colorway": ["#636efa", "#EF553B", "#00cc96", "#ab63fa", "#FFA15A", "#19d3f3", "#FF6692", "#B6E880", "#FF97FF", "#FECB52"], "font": {"color": "#2a3f5f"}, "geo": {"bgcolor": "white", "lakecolor": "white", "landcolor": "#E5ECF6", "showlakes": true, "showland": true, "subunitcolor": "white"}, "hoverlabel": {"align": "left"}, "hovermode": "closest", "mapbox": {"style": "light"}, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": {"angularaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "radialaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "scene": {"xaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "yaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Predictive Power Score (PPS) - Can a feature predict the label by itself?"}, "xaxis": {"range": [-3, 5], "title": {"text": "Column"}, "type": "category"}, "yaxis": {"range": [0, 1.05], "title": {"text": "Predictive Power Score (PPS)"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}, "buffers": [{"encoding": "base64", "path": ["_data", 0, "y", "buffer"], "data": "OW9fF7jIwT/ozOegxAHAP4jJiJxu4bg/"}]}, "c6864c2e911f41ff9f50ad9cad26914b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_13ca61311a9a464f8a2a9f38db5da226", "style": "IPY_MODEL_9ae575d62bd047baa331f50512b9ab98", "value": "<div>The Predictive Power Score (PPS) is used to estimate the ability of an image property (such as brightness)to predict the label by itself. (Read more about <a href=https://docs.deepchecks.com/stable/vision/auto_checks/data_integrity/plot_property_label_correlation.html target=\"_blank\">Predictive Power Score</a>)</div>"}}, "13ca61311a9a464f8a2a9f38db5da226": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9ae575d62bd047baa331f50512b9ab98": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f2ff305710e547378169d8ac7396e471": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_41ac359e02f14f21b2ff1c47b7c7dbed", "style": "IPY_MODEL_e7888e9be60a4cae9b9e60a8d8d0d794", "value": "<div><u>In the graph above</u>, we should suspect we have problems in our data if:</div>"}}, "41ac359e02f14f21b2ff1c47b7c7dbed": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e7888e9be60a4cae9b9e60a8d8d0d794": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fb07a14413e14d2786cebcfeb74874ab": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_480eb952ae554249834f4a9cc72e74d1", "style": "IPY_MODEL_a06ff8fdfdd6426cb92725c530e33749", "value": "<div><b>Train dataset PPS values are high</b>:</div>"}}, "480eb952ae554249834f4a9cc72e74d1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a06ff8fdfdd6426cb92725c530e33749": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "800ccd36f29c4d70b4ba1136f9e06f32": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e6366ee3743d454cbb74ebed79812a5f", "style": "IPY_MODEL_52a7697f8cd0464f8099a5dc24b69481", "value": "<div>   A high PPS (close to 1) can mean that there's a bias in the dataset, as a single property can predict the label successfully, using simple classic ML algorithms</div>"}}, "e6366ee3743d454cbb74ebed79812a5f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "52a7697f8cd0464f8099a5dc24b69481": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "268b22df556c4ab49ba1b2c74677a1d1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bc57e3cb07a446099c0f2f35d56285d2", "style": "IPY_MODEL_51d7c24c226b469eb928b47da46435c8", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place, check used 5000 images from the original dataset. Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "bc57e3cb07a446099c0f2f35d56285d2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "51d7c24c226b469eb928b47da46435c8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "244c884bf7e6411895eb44a3a166e002": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f41f0067ee2944308355ca6eb0efdd53", "style": "IPY_MODEL_52e0a4baa11c4583ac60301c7be45ade", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "f41f0067ee2944308355ca6eb0efdd53": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "52e0a4baa11c4583ac60301c7be45ade": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9815194c89d9402c9b853ebfd23a5f1b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0996284b46c84798bd17e26971b3112c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e68afd611cfe4508aad7e662a167cc6d": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_47de3df152804e8faf4674a3f2e760a0", "IPY_MODEL_52be8c8c20ad4ff2bd4222029592e999", "IPY_MODEL_528b9904304446098c0897e7d4f5c36e"], "layout": "IPY_MODEL_61a1b5aebd5d45b1ac3a4b5ac383b810"}}, "47de3df152804e8faf4674a3f2e760a0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c35325f66e214b0e884d75201f3bc74e", "style": "IPY_MODEL_c8f73bf173284ff8bb4b6af8b526dd6f", "value": "<h4 id=\"ConfusionMatrix_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Confusion Matrix</b></h4>"}}, "c35325f66e214b0e884d75201f3bc74e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c8f73bf173284ff8bb4b6af8b526dd6f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "52be8c8c20ad4ff2bd4222029592e999": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_87d27cb6197a46dc8b9ff2df538be029", "style": "IPY_MODEL_7679e563c99942099a65490c612e66e6", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "87d27cb6197a46dc8b9ff2df538be029": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7679e563c99942099a65490c612e66e6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "528b9904304446098c0897e7d4f5c36e": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_f487d73fbe30441b9382622a6073677c", "IPY_MODEL_b30263231950431196635489dd9c80f3", "IPY_MODEL_b3562fc8bfe14d3381da7d21c3de0943", "IPY_MODEL_268c9e9c44e243168019b8e67b1936dd", "IPY_MODEL_617b7a5d49a14c44be6670f55e6ed075"], "layout": "IPY_MODEL_05972fff7d584e86888c76a20b432dbb"}}, "f487d73fbe30441b9382622a6073677c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_58285f07f4fe4e6b9cc73038455cc67f", "style": "IPY_MODEL_00e09736ae72441c86071403e87b35e9", "value": "<h5><b>Additional Outputs</b></h5>"}}, "58285f07f4fe4e6b9cc73038455cc67f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "00e09736ae72441c86071403e87b35e9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b30263231950431196635489dd9c80f3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8cbc46ebadcf4b6c8049f287dcfa7315", "style": "IPY_MODEL_4205a68ccca143b29c3089bfb7860021", "value": "<div>Showing 10 of 12 classes:</div>"}}, "8cbc46ebadcf4b6c8049f287dcfa7315": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4205a68ccca143b29c3089bfb7860021": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b3562fc8bfe14d3381da7d21c3de0943": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c6fcf046dfde4541b3e9734e2fe0cfae", "style": "IPY_MODEL_e5d6589ae6274048b9ecac9400877ba3", "value": "<div>The overall accuracy of your model is: 58.2%.<br>Best accuracy achieved on samples with <b>aedes_dorsalis</b> label (100.0%).<br>Worst accuracy achieved on samples with <b>anopheles_arabiensis</b> label (0.0%).</div>"}}, "c6fcf046dfde4541b3e9734e2fe0cfae": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", 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"gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}, "zaxis": {"backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white"}}, "shapedefaults": {"line": {"color": "#2a3f5f"}}, "ternary": {"aaxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "baxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}, "bgcolor": "#E5ECF6", "caxis": {"gridcolor": "white", "linecolor": "white", "ticks": ""}}, "title": {"x": 0.05}, "xaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Confusion Matrix (# Samples)", "x": 0.5}, "xaxis": {"constrain": "domain", "scaleanchor": "y", "title": {"text": "Predicted Value"}, "type": "category"}, "yaxis": {"autorange": "reversed", "constrain": "domain", "title": {"text": "True Value"}, "type": "category"}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "617b7a5d49a14c44be6670f55e6ed075": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1340b962442d42c49bc29c300bfed47d", "style": "IPY_MODEL_61b0949f0a88459f90ce5007dedb78eb", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "1340b962442d42c49bc29c300bfed47d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "61b0949f0a88459f90ce5007dedb78eb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "05972fff7d584e86888c76a20b432dbb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "61a1b5aebd5d45b1ac3a4b5ac383b810": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a7e585b9aca4430e9a0960932fb272f4": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_bfdd890c31e74287815af85c5aa01423", "IPY_MODEL_f20a15f831374681acd19a12757afee2", "IPY_MODEL_0873a8f5a2bc432c8140f16a50063743"], "layout": "IPY_MODEL_aa5334c559e94dac959b674a84e5ee75"}}, "bfdd890c31e74287815af85c5aa01423": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ff8d3aee59e04cb2a0fe91bc342acbb2", "style": "IPY_MODEL_1cd984b47b784021a9824095e7a4629a", "value": "<h4 id=\"ConfusionMatrix_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Confusion Matrix</b></h4>"}}, "ff8d3aee59e04cb2a0fe91bc342acbb2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1cd984b47b784021a9824095e7a4629a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f20a15f831374681acd19a12757afee2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a12860249f904c6d9f01c582d25363e3", "style": "IPY_MODEL_8de0fdd5393d4ec1928b9e4cfc2f99bf", "value": "<p>Calculate the confusion matrix of the model on the given dataset. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/model_evaluation/plot_confusion_matrix.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "a12860249f904c6d9f01c582d25363e3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8de0fdd5393d4ec1928b9e4cfc2f99bf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0873a8f5a2bc432c8140f16a50063743": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_ebd52625ac8d491297c688d713e2fcdf", "IPY_MODEL_60c5eadab6904abeb566a6c424bc5553", "IPY_MODEL_f148aaace6e748f3b404e124bca35913", "IPY_MODEL_ff0497a99e0545989a28f42833ff5786", "IPY_MODEL_7d253e04a2c04b51be6c2939e16fc420", "IPY_MODEL_2772e86b49f94b4e97218bde83f834c2"], "layout": "IPY_MODEL_0c15ba5f211b4428a45f940c62b77577"}}, "ebd52625ac8d491297c688d713e2fcdf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_df6249aa56a9487981d1d720e1bfe6f8", "style": "IPY_MODEL_cd50b1953d334871ade21e6e959cd22f", "value": "<h5><b>Additional Outputs</b></h5>"}}, "df6249aa56a9487981d1d720e1bfe6f8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cd50b1953d334871ade21e6e959cd22f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "60c5eadab6904abeb566a6c424bc5553": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2284736ab6124943a0cbc59a74ba8d3b", "style": 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"zerolinecolor": "white", "zerolinewidth": 2}, "yaxis": {"automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": {"standoff": 15}, "zerolinecolor": "white", "zerolinewidth": 2}}}, "title": {"text": "Confusion Matrix (# Samples)", "x": 0.5}, "xaxis": {"constrain": "domain", "scaleanchor": "y", "title": {"text": "Predicted Value"}, "type": "category"}, "yaxis": {"autorange": "reversed", "constrain": "domain", "title": {"text": "True Value"}, "type": "category"}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "7d253e04a2c04b51be6c2939e16fc420": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c623178370404d93be6c08ceefb22b3f", "style": "IPY_MODEL_fb202dd7f8134ae191b41dcda4891f12", "value": "<div><p style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place, check used 5000 images from the original dataset. Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "c623178370404d93be6c08ceefb22b3f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fb202dd7f8134ae191b41dcda4891f12": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2772e86b49f94b4e97218bde83f834c2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_630b19ae875d4a948d6693d0550985c1", "style": "IPY_MODEL_f7cdc3293d8b4eb88aeba4611ea8b1fc", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, "630b19ae875d4a948d6693d0550985c1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f7cdc3293d8b4eb88aeba4611ea8b1fc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0c15ba5f211b4428a45f940c62b77577": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "aa5334c559e94dac959b674a84e5ee75": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d80b1aab24674910ae2abf891338d9f0": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_1d0a6b898a984cf3a49945edbd72f8c9", "IPY_MODEL_605e69d81069404ebb5691e53dbb5589", "IPY_MODEL_bf9ac0824afa424a8f94e9b2d5ea744c"], "layout": "IPY_MODEL_6303fc15783e4b36a529f315b4d01737"}}, "1d0a6b898a984cf3a49945edbd72f8c9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_422e97d9c8184053b8809ba13962cf75", "style": "IPY_MODEL_87ba159d99d84564802c9e90a591ff62", "value": "<h4 id=\"HeatmapComparison_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\"><b>Heatmap Comparison</b></h4>"}}, "422e97d9c8184053b8809ba13962cf75": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "87ba159d99d84564802c9e90a591ff62": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "605e69d81069404ebb5691e53dbb5589": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_92efbb291c1349e99417be93f34a584f", "style": "IPY_MODEL_022208558cf246fd87c9f96e604014b4", "value": "<p>Check if the average image brightness (or bbox location if applicable) is similar between train and test set. <a href=\"https://docs.deepchecks.com/0.18/vision/auto_checks/train_test_validation/plot_heatmap_comparison.html?utm_source=display_output&utm_medium=referral&utm_campaign=check_link\" target=\"_blank\">Read More...</a></p>"}}, "92efbb291c1349e99417be93f34a584f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "022208558cf246fd87c9f96e604014b4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bf9ac0824afa424a8f94e9b2d5ea744c": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_cadb20958db047a287f52ec14c12390b", "IPY_MODEL_a7d32e8e0f2b43c097a1838f601c2827", "IPY_MODEL_efa578d2d7524d65afe88be1864a5c17", "IPY_MODEL_12aa65a151114fbfac03e6b686305a88"], "layout": "IPY_MODEL_886e0dce3843430b90ce1065abb85358"}}, "cadb20958db047a287f52ec14c12390b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b2a29fca1a59469dabd144f5efbc7bcf", "style": "IPY_MODEL_b2e0a3e8541c4729be299038e905fa5c", "value": "<h5><b>Additional Outputs</b></h5>"}}, "b2a29fca1a59469dabd144f5efbc7bcf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b2e0a3e8541c4729be299038e905fa5c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a7d32e8e0f2b43c097a1838f601c2827": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"coloraxis": "coloraxis", "hoverinfo": "skip", "xaxis": "x", "yaxis": "y", "z": [[253, 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style=\"font-size:0.9em;line-height:1;\"><i>Note - data sampling: Sampling procedure took place in <b>Train</b> dataset, check used 5000 images from the original dataset.</br> Sample size can be controlled with the \"n_samples\" parameter.</i></p></div>"}}, "1fba4d564c71429eafeab1e581c029d4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2053386e77df4df59d8524b3bf22e503": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "12aa65a151114fbfac03e6b686305a88": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b1c5c232f84443bcbc59bb0e630960ea", "style": "IPY_MODEL_134bd6fa114041e69796991d29fd9f98", "value": "<br><a href=\"#summary_VPGB8IY525FNSCQQAVAQDMFZT-section-S60YU\" style=\"font-size: 14px;\">Go to top</a>"}}, 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"LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ed442f91681045fcbe03e43bfc87459f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_6d34c512167b40419387adfc94bc5e69", "IPY_MODEL_7ab4c9067adf49e1b35101ca893b7056"], "layout": "IPY_MODEL_1f05fba26022419fbce8a492fe5a5850"}}, "6d34c512167b40419387adfc94bc5e69": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f60027bf70494ec9a5c3ac0148f90dc0", "style": "IPY_MODEL_5e99854ab3f24715b70fd00dacf39dcc"}}, "f60027bf70494ec9a5c3ac0148f90dc0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5e99854ab3f24715b70fd00dacf39dcc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7ab4c9067adf49e1b35101ca893b7056": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "_titles": {"0": "Didn't Run"}, "children": ["IPY_MODEL_182e8dda222e4962ad5ad1b412d81884"], "layout": "IPY_MODEL_4991577f75514a358d56f6f8c7baf62f", "selected_index": null}}, "182e8dda222e4962ad5ad1b412d81884": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f3553117c5b04dd5a9d6c5368af9dd7a", "style": "IPY_MODEL_e203fb0ea7ed49b2b37fbe1120cf191d", "value": "<style type=\"text/css\">\n#T_01713 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_01713 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_01713 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_01713 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_01713 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_01713\">\n  <thead>\n    <tr>\n      <th id=\"T_01713_level0_col0\" class=\"col_heading level0 col0\" >Check</th>\n      <th id=\"T_01713_level0_col1\" class=\"col_heading level0 col1\" >Reason</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_01713_row0_col0\" class=\"data row0 col0\" >Mean Average Precision Report</td>\n      <td id=\"T_01713_row0_col1\" class=\"data row0 col1\" >Check is irrelevant for task of type TaskType.CLASSIFICATION</td>\n    </tr>\n    <tr>\n      <td id=\"T_01713_row1_col0\" class=\"data row1 col0\" >Mean Average Precision Report</td>\n      <td id=\"T_01713_row1_col1\" class=\"data row1 col1\" >Check is irrelevant for task of type TaskType.CLASSIFICATION</td>\n    </tr>\n    <tr>\n      <td id=\"T_01713_row2_col0\" class=\"data row2 col0\" >Label Property Outliers</td>\n      <td id=\"T_01713_row2_col1\" class=\"data row2 col1\" >DeepchecksProcessError: task type classification does not have default label properties for label outliers.</td>\n    </tr>\n    <tr>\n      <td id=\"T_01713_row3_col0\" class=\"data row3 col0\" >Label Property Outliers</td>\n      <td id=\"T_01713_row3_col1\" class=\"data row3 col1\" >DeepchecksProcessError: task type classification does not have default label properties for label outliers.</td>\n    </tr>\n    <tr>\n      <td id=\"T_01713_row4_col0\" class=\"data row4 col0\" >Mean Average Recall Report</td>\n      <td id=\"T_01713_row4_col1\" class=\"data row4 col1\" >Check is irrelevant for task of type TaskType.CLASSIFICATION</td>\n    </tr>\n    <tr>\n      <td id=\"T_01713_row5_col0\" class=\"data row5 col0\" >Mean Average Recall Report</td>\n      <td id=\"T_01713_row5_col1\" class=\"data row5 col1\" >Check is irrelevant for task of type TaskType.CLASSIFICATION</td>\n    </tr>\n  </tbody>\n</table>\n"}}, 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