diff --git "a/xresnet50_ep_20_full_suite.html" "b/xresnet50_ep_20_full_suite.html"
--- "a/xresnet50_ep_20_full_suite.html"
+++ "b/xresnet50_ep_20_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/xresnet50-data_new_bg_4x_simple-clust_spl-subs_4_v_3-run_1/">Current Report Home</a>
-        <a href="https://gitlab.com/mosquitoscan/experiments-reports/xresnet50-data_new_bg_4x_simple-clust_spl-subs_4_v_3-run_1/">Current Report Repository</a>
+        <a href="https://mosquitoscan.gitlab.io/experiments-reports/xresnet50-data_new_bg_4x_simple-clust_spl-subs_5_v_3-run_1/">Current Report Home</a>
+        <a href="https://gitlab.com/mosquitoscan/experiments-reports/xresnet50-data_new_bg_4x_simple-clust_spl-subs_5_v_3-run_1/">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
     
 
 <script type="application/vnd.jupyter.widget-state+json">
-{"version_major": 2, "version_minor": 0, "state": {"63cf4aaefd3645e08db6c70e5c354230": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_titles": {"0": "Full Suite"}, "children": ["IPY_MODEL_cc0d7ad83d0b4a129b293a8cf750bdc4"], "layout": "IPY_MODEL_deed9a7b1877441bb3b0a4eb01828c36"}}, "cc0d7ad83d0b4a129b293a8cf750bdc4": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_b69ae2ab18944cbc9be687c2549b3446", "IPY_MODEL_f754c2cec6404f9887ff7711c317108d", "IPY_MODEL_4fdf926132b74ac09feb1309e6e1bbea", "IPY_MODEL_b068cd938058406cb10ffdc54d1fbaaa", "IPY_MODEL_e8d66bbd42ae4a3ba69d2de13ecfe35d"], "layout": "IPY_MODEL_2a12e0f309c54a5b86b4380ff2349879"}}, "b69ae2ab18944cbc9be687c2549b3446": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7cc3687d075d4f64b69e6d1f39350709", "style": "IPY_MODEL_4f7889af882146dfbb624bedd6386d0e", "value": "\n<h1 id=\"summary_TAXP0WFOY4DD1VN8LFJDTVV2Y\">Full Suite</h1>\n<p>\n    The suite is composed of various checks such as: Class Performance, Image Property Outliers, Property Label Correlation Change, 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"}}, "7cc3687d075d4f64b69e6d1f39350709": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4f7889af882146dfbb624bedd6386d0e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f754c2cec6404f9887ff7711c317108d": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_b289079a38d947118cb9742899c10d21", "IPY_MODEL_6eb0180a94424383976554769a3f76bd"], "layout": "IPY_MODEL_9b9fb27f928f49fc95148da25585ef84"}}, "b289079a38d947118cb9742899c10d21": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_706ea9b241d6442b89d446a890656d4b", "style": "IPY_MODEL_e5fb208c31b5432db43bcc5953169a56", "value": "<span id=\"summary_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-4XKSS\"></span>"}}, "706ea9b241d6442b89d446a890656d4b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e5fb208c31b5432db43bcc5953169a56": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6eb0180a94424383976554769a3f76bd": {"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_469138dd66d44f6482d43ae5855eb696"], "layout": "IPY_MODEL_b2514de4c6244a09a9fd7a08b82aa907", "selected_index": null}}, "469138dd66d44f6482d43ae5855eb696": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_3c5047d62ecb4a53bcf0e4e987aadfc3", "IPY_MODEL_3ba839c0079b4a2bbeba68ee142d3012", "IPY_MODEL_19967f959e4b48ba961ccc3c51a368f3", "IPY_MODEL_53e5187a29a44960a35dd99a4864882c", "IPY_MODEL_2773d18a5dd2454383018b7de96eb669", "IPY_MODEL_53e5187a29a44960a35dd99a4864882c", "IPY_MODEL_321e36e56dad4c44b7abbc07e302f1cd", "IPY_MODEL_53e5187a29a44960a35dd99a4864882c", "IPY_MODEL_75a7f1eeb83f4d40a99d8e8743f95399"], "layout": "IPY_MODEL_c76386abec80431b980c7616d570f789"}}, "3c5047d62ecb4a53bcf0e4e987aadfc3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f228916acdc6406596d016ae903db240", "style": "IPY_MODEL_31df1d27675f481f8f9f8a370bcb3ba5", "value": "<style type=\"text/css\">\n#T_77e72 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_77e72 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_77e72 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_77e72 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_77e72 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_77e72\">\n  <thead>\n    <tr>\n      <th id=\"T_77e72_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_77e72_level0_col1\" class=\"col_heading level0 col1\" >Check</th>\n      <th id=\"T_77e72_level0_col2\" class=\"col_heading level0 col2\" >Condition</th>\n      <th id=\"T_77e72_level0_col3\" class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_77e72_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_77e72_row0_col1\" class=\"data row0 col1\" ><a href=\"#ClassPerformance_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-4XKSS\">Class Performance</a></td>\n      <td id=\"T_77e72_row0_col2\" class=\"data row0 col2\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_77e72_row0_col3\" class=\"data row0 col3\" >6 scores failed. Found max degradation of 64.94% for metric Recall and class aedes_albopictus.</td>\n    </tr>\n    <tr>\n      <td id=\"T_77e72_row1_col0\" class=\"data row1 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_77e72_row1_col1\" class=\"data row1 col1\" ><a href=\"#PredictionDrift_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-4XKSS\">Prediction Drift</a></td>\n      <td id=\"T_77e72_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_77e72_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': '0.21'}</td>\n    </tr>\n    <tr>\n      <td id=\"T_77e72_row2_col0\" class=\"data row2 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_77e72_row2_col1\" class=\"data row2 col1\" ><a href=\"#SimpleModelComparison_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-4XKSS\">Simple Model Comparison</a></td>\n      <td id=\"T_77e72_row2_col2\" class=\"data row2 col2\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_77e72_row2_col3\" class=\"data row2 col3\" >Found metrics with gain below threshold: {'F1': {'aedes_aegypti': '-4.6%'}}</td>\n    </tr>\n    <tr>\n      <td id=\"T_77e72_row3_col0\" class=\"data row3 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_77e72_row3_col1\" class=\"data row3 col1\" ><a href=\"#WeakSegmentsPerformance_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-4XKSS\">Weak Segments Performance</a></td>\n      <td id=\"T_77e72_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_77e72_row3_col3\" class=\"data row3 col3\" >Found a segment with cross entropy of -5.204 in comparison to an average score of -1.94 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "f228916acdc6406596d016ae903db240": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "31df1d27675f481f8f9f8a370bcb3ba5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3ba839c0079b4a2bbeba68ee142d3012": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_83a6c4aae894466b98b00ac4f5813841", "style": "IPY_MODEL_330c4e80914149208d5a62edd43e77ff", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "83a6c4aae894466b98b00ac4f5813841": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "330c4e80914149208d5a62edd43e77ff": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "19967f959e4b48ba961ccc3c51a368f3": {"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_8a3753c0f0e84027832c562dc43d5df7", "IPY_MODEL_cb3943d5743c4a6dbbeec09427739a02", "IPY_MODEL_e7e7627069c0439c8558dec96501b053", "IPY_MODEL_a6db39be05e04e018bd72af1f50751aa"], "layout": "IPY_MODEL_365dcbd742c849e4b8f51671999720b5"}}, "8a3753c0f0e84027832c562dc43d5df7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a4ff5a2e1afa4a3eb1fd89aa3da246a7", "style": "IPY_MODEL_80df5ea8ad2948d9b3788d59a1e37de3", "value": "<h4 id=\"ClassPerformance_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-4XKSS\"><b>Class Performance</b></h4>"}}, "a4ff5a2e1afa4a3eb1fd89aa3da246a7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "80df5ea8ad2948d9b3788d59a1e37de3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cb3943d5743c4a6dbbeec09427739a02": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_aaafee6301e745bdafc1e619ec380492", "style": "IPY_MODEL_5c6b3b35fde947ebaf47e47f6b4b2abf", "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>"}}, "aaafee6301e745bdafc1e619ec380492": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5c6b3b35fde947ebaf47e47f6b4b2abf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e7e7627069c0439c8558dec96501b053": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_80d721993ade43f5a9ccc2e39bb76316", "style": "IPY_MODEL_116aacf78af04049b862a47098610e27", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_f0bf6 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f0bf6 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f0bf6 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f0bf6 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_f0bf6 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_f0bf6\">\n  <thead>\n    <tr>\n      <th id=\"T_f0bf6_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_f0bf6_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_f0bf6_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_f0bf6_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_f0bf6_row0_col1\" class=\"data row0 col1\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_f0bf6_row0_col2\" class=\"data row0 col2\" >6 scores failed. Found max degradation of 64.94% for metric Recall and class aedes_albopictus.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "80d721993ade43f5a9ccc2e39bb76316": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "116aacf78af04049b862a47098610e27": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a6db39be05e04e018bd72af1f50751aa": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_91c8db07aa6d4ea6be99fbe5e8a2232e", "IPY_MODEL_a7071dcf95af43c1a724ae264bc057fe", "IPY_MODEL_276b7a09ad134c1d8747ed08b83cf518"], "layout": "IPY_MODEL_ba3639e8ce12449f92f19f5290084802"}}, "91c8db07aa6d4ea6be99fbe5e8a2232e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_591d01784c5745f9b049566f7e896274", "style": "IPY_MODEL_147a3fbb94f9499ea4a9181efc7b1823", "value": "<h5><b>Additional Outputs</b></h5>"}}, "591d01784c5745f9b049566f7e896274": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "147a3fbb94f9499ea4a9181efc7b1823": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a7071dcf95af43c1a724ae264bc057fe": {"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": "#00008b", "pattern": {"shape": ""}}, "name": "Train", "offsetgroup": "Train", "orientation": "v", "showlegend": true, "x": ["aedes_aegypti", "aedes_albopictus", "culex_quinquefasciatus"], "xaxis": "x", "y": {"dtype": "float64", "shape": [3]}, "yaxis": "y", "type": "histogram", "uid": "dc81c589-4d51-4929-8989-25081595247f"}, {"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": "#00008b", "pattern": {"shape": ""}}, "name": "Train", "offsetgroup": "Train", "orientation": "v", "showlegend": false, "x": ["aedes_aegypti", "aedes_albopictus", "culex_quinquefasciatus"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [3]}, "yaxis": "y2", "type": "histogram", "uid": "1fc36945-c1cc-4b26-a50f-7d7bc440ec85"}, {"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": "#69b3a2", "pattern": {"shape": ""}}, "name": "Test", "offsetgroup": "Test", "orientation": "v", "showlegend": true, "x": ["aedes_albopictus", "culex_quinquefasciatus", "aedes_aegypti"], "xaxis": "x", "y": {"dtype": "float64", "shape": [3]}, "yaxis": "y", "type": "histogram", "uid": "b189418e-b4cd-4274-bc95-79d098add843"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Test<br>Metric=Recall<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Test", "marker": {"color": "#69b3a2", "pattern": {"shape": ""}}, "name": "Test", "offsetgroup": "Test", "orientation": "v", "showlegend": false, "x": ["culex_quinquefasciatus", "aedes_aegypti", "aedes_albopictus"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [3]}, "yaxis": "y2", "type": "histogram", "uid": "ecd4b6eb-ad09-4ccc-9419-dea14634001e"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, 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"data": "JUmSJEmS5D95PB6Px+PhPxM/o8TPKOE/"}, {"encoding": "base64", "path": ["_data", 3, "y", "buffer"], "data": "hfldLW7C7D/hfAzOx+DcPyjEWfkJcdY/"}]}, "276b7a09ad134c1d8747ed08b83cf518": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3e607bcc2d4f4e1d966b68ecca7e99bd", "style": "IPY_MODEL_02c8763b7f4b447b9f14379dd6b2029d", "value": "<br><a href=\"#summary_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-4XKSS\" style=\"font-size: 14px;\">Go to top</a>"}}, "3e607bcc2d4f4e1d966b68ecca7e99bd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "02c8763b7f4b447b9f14379dd6b2029d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ba3639e8ce12449f92f19f5290084802": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "365dcbd742c849e4b8f51671999720b5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "53e5187a29a44960a35dd99a4864882c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2e4be611e9bd4525a7afacc5677ed7af", "style": "IPY_MODEL_f119d51e511a41f894da3b2b84ace754", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "2e4be611e9bd4525a7afacc5677ed7af": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f119d51e511a41f894da3b2b84ace754": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2773d18a5dd2454383018b7de96eb669": {"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_da08cff7d0f24d6eba2973e5746d3d00", "IPY_MODEL_7f1e2a9630b349a78d89117583cece6d", "IPY_MODEL_f2ad5dee93c043dba501bddacf6f0a16", "IPY_MODEL_9454c4b3250b46bb9a2b34a40c39bb1d"], "layout": "IPY_MODEL_ab7d87fb8d3a45c3a19102cd3cf7fd44"}}, "da08cff7d0f24d6eba2973e5746d3d00": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_66dcaa2b4518487595cf9d0c67fec2a1", "style": "IPY_MODEL_ceef7a9771dc4df48c84d8f8775cad93", "value": "<h4 id=\"PredictionDrift_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-4XKSS\"><b>Prediction Drift</b></h4>"}}, "66dcaa2b4518487595cf9d0c67fec2a1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ceef7a9771dc4df48c84d8f8775cad93": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7f1e2a9630b349a78d89117583cece6d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1f2ab44c849c442bb4107a48a0d14222", "style": "IPY_MODEL_3bb61e2dfbb449c18f155c2e7454d8d6", "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>"}}, "1f2ab44c849c442bb4107a48a0d14222": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3bb61e2dfbb449c18f155c2e7454d8d6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f2ad5dee93c043dba501bddacf6f0a16": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_761cd18b90544eaaa951ef4ebf99140c", "style": "IPY_MODEL_bc15e84645c645a2be532d650920c9d9", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_5681d table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5681d thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5681d tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5681d th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5681d td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_5681d\">\n  <thead>\n    <tr>\n      <th id=\"T_5681d_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_5681d_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_5681d_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_5681d_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_5681d_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_5681d_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': '0.21'}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "761cd18b90544eaaa951ef4ebf99140c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bc15e84645c645a2be532d650920c9d9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9454c4b3250b46bb9a2b34a40c39bb1d": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_3b9e1d547f9144cc9c7396b4c5312774", "IPY_MODEL_9e1bb81346a24f1ca531e37781915fc9", "IPY_MODEL_8abc06e144ea41ceaa0b018d7a7a121a", "IPY_MODEL_9ae2c7ecc149499492bf702d7396a059", "IPY_MODEL_7a92ff629c4844c2b930376beef2adc4"], "layout": "IPY_MODEL_518824423df841a79d569a961fb8d99a"}}, "3b9e1d547f9144cc9c7396b4c5312774": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f732276212414c49bf57a9429ddc39e7", "style": "IPY_MODEL_7e3faf08376a4fbe92c48181b1a63ee1", "value": "<h5><b>Additional Outputs</b></h5>"}}, "f732276212414c49bf57a9429ddc39e7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7e3faf08376a4fbe92c48181b1a63ee1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9e1bb81346a24f1ca531e37781915fc9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b0f53612cac4411798c4b7190735af34", "style": "IPY_MODEL_fdf594722a1c4dc38e208fd43c9a2129", "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>"}}, "b0f53612cac4411798c4b7190735af34": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fdf594722a1c4dc38e208fd43c9a2129": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8abc06e144ea41ceaa0b018d7a7a121a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_42a5743ab9214abc929830af094d24cb", "style": "IPY_MODEL_817301da35554bd3953dbb3558b2a2a2", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "42a5743ab9214abc929830af094d24cb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "817301da35554bd3953dbb3558b2a2a2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9ae2c7ecc149499492bf702d7396a059": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#01B8AA"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.1], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "328c2018-c9c0-44a7-92bc-fa145fda40c4"}, {"base": 0.1, "marker": {"color": "#F2C80F"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.1], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "f23fe7e3-296a-409b-8844-655c97e2d991"}, {"base": 0.2, "marker": {"color": "#FE9666"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.011325251384170232], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "6b0c780a-9747-497d-82f8-1036835a07a7"}, {"marker": {"color": "#00008b"}, "name": "Train Dataset", "x": ["aedes_aegypti", "aedes_albopictus", "culex_quinquefasciatus"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [3]}, "yaxis": "y2", "type": "bar", "uid": "f9374ee2-a2b6-49bb-a294-ebf27329c695"}, {"marker": {"color": "#69b3a2"}, "name": "Test Dataset", "x": ["aedes_aegypti", "aedes_albopictus", "culex_quinquefasciatus"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [3]}, "yaxis": "y2", "type": "bar", "uid": "97d47515-e437-45cd-9a47-e3981d4fb704"}], "_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": 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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": "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, 5], "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", 3, "y", "buffer"], "data": "VVVVVVVV1T9VVVVVVVXVP1VVVVVVVdU/"}, {"encoding": "base64", "path": ["_data", 4, "y", "buffer"], "data": "s/uNsvuN0j+XlpaWlpbGP4FcE4FcE+E/"}]}, "7a92ff629c4844c2b930376beef2adc4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f2e310b87c634dd9bade6d007b3bc096", "style": "IPY_MODEL_4c87e580047a42bebf2e4f35b5a37d2d", "value": "<br><a href=\"#summary_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-4XKSS\" style=\"font-size: 14px;\">Go to top</a>"}}, "f2e310b87c634dd9bade6d007b3bc096": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4c87e580047a42bebf2e4f35b5a37d2d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "518824423df841a79d569a961fb8d99a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ab7d87fb8d3a45c3a19102cd3cf7fd44": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "321e36e56dad4c44b7abbc07e302f1cd": {"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_56dd4c0286d041c0a3c6ddbbb40cedc0", "IPY_MODEL_ed6f6f5952c44c3e8a4c7d1e8739a6f1", "IPY_MODEL_d827d4dfe2504d168946142c3c44a8c1", "IPY_MODEL_538ed7aad766439491cfb4c58202329a"], "layout": "IPY_MODEL_9c05b6037a404f64ad26739b56183cb4"}}, "56dd4c0286d041c0a3c6ddbbb40cedc0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0b8f0ba03bb444feaf0deea71aca1b3e", "style": "IPY_MODEL_f9a64acc267b49f89c91304cee30218a", "value": "<h4 id=\"SimpleModelComparison_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-4XKSS\"><b>Simple Model Comparison</b></h4>"}}, "0b8f0ba03bb444feaf0deea71aca1b3e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f9a64acc267b49f89c91304cee30218a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ed6f6f5952c44c3e8a4c7d1e8739a6f1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_066f243e25894a6abb4e4cd195e631ea", "style": "IPY_MODEL_8a03e6641a2d4b45beb9aa61b8d77351", "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>"}}, "066f243e25894a6abb4e4cd195e631ea": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8a03e6641a2d4b45beb9aa61b8d77351": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d827d4dfe2504d168946142c3c44a8c1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_17afb6fdfc6b40528596662a604887f9", "style": "IPY_MODEL_719bbc45087b4f1dab4c7f8dd81c97e5", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_c85e3 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c85e3 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c85e3 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c85e3 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c85e3 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_c85e3\">\n  <thead>\n    <tr>\n      <th id=\"T_c85e3_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_c85e3_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_c85e3_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_c85e3_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_c85e3_row0_col1\" class=\"data row0 col1\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_c85e3_row0_col2\" class=\"data row0 col2\" >Found metrics with gain below threshold: {'F1': {'aedes_aegypti': '-4.6%'}}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "17afb6fdfc6b40528596662a604887f9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, 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"model_module_version": "1.2.0", "state": {}}, "07295014f1a14d6bac846a7e99ba35c5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "39ee3d6a0cc24afe824de4704dde08f7": {"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": "#2191FB", "pattern": {"shape": ""}}, "name": "Simple Model", "offsetgroup": "Simple Model", "orientation": "v", "showlegend": true, "x": ["aedes_aegypti", "aedes_albopictus", "culex_quinquefasciatus"], "xaxis": "x", "y": {"dtype": "float64", "shape": [3]}, "yaxis": "y", "type": "histogram", "uid": "6cad8a65-0bae-43d4-81d4-54518838ab16"}, 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{"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, 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"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": "ZmZmZmZm4D8AAAAAAAAAAAAAAAAAAAAA"}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "fquNoekO5j/7dH26Pl3fP53BCp3BCt0/"}]}, "2eb998559996427bb733cd411d35162f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8858c7faf0f44ccab4b3e7c654c4699a", "style": "IPY_MODEL_ad0673f5651246af91e9d5e6dc53f611", "value": "<br><a href=\"#summary_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-4XKSS\" style=\"font-size: 14px;\">Go to top</a>"}}, "8858c7faf0f44ccab4b3e7c654c4699a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ad0673f5651246af91e9d5e6dc53f611": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f73c97b6653b4899bbe7824e314d7783": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9c05b6037a404f64ad26739b56183cb4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "75a7f1eeb83f4d40a99d8e8743f95399": {"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_5cf3952f681d48838a943f3895d00556", "IPY_MODEL_8f0dedc346ff4b998c33ca11e86284f5", "IPY_MODEL_ef9d61e2204b4a13a737d206da1033a0", "IPY_MODEL_96b771d4dfce49a0945b5be6496b0e63"], "layout": "IPY_MODEL_9e0bdf93325f4251b05c2f74ce292fd3"}}, "5cf3952f681d48838a943f3895d00556": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9591df1cfefd4878a258b163502d3373", "style": "IPY_MODEL_fbc9f03c629f4197b733434613b42bdc", "value": "<h4 id=\"WeakSegmentsPerformance_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-4XKSS\"><b>Weak Segments Performance</b></h4>"}}, "9591df1cfefd4878a258b163502d3373": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fbc9f03c629f4197b733434613b42bdc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "8f0dedc346ff4b998c33ca11e86284f5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_733eb28177604d5fa8405f73d4c05f3a", "style": "IPY_MODEL_177df33716444157930fa96fc8ccc806", "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>"}}, "733eb28177604d5fa8405f73d4c05f3a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "177df33716444157930fa96fc8ccc806": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ef9d61e2204b4a13a737d206da1033a0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2a460706e98942c0aadebb526f8ee071", "style": "IPY_MODEL_0d805e59e38b46939950a16374f5fc54", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_3fd88 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3fd88 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3fd88 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3fd88 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_3fd88 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_3fd88\">\n  <thead>\n    <tr>\n      <th id=\"T_3fd88_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_3fd88_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_3fd88_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_3fd88_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_3fd88_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_3fd88_row0_col2\" class=\"data row0 col2\" >Found a segment with cross entropy of -5.204 in comparison to an average score of -1.94 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "2a460706e98942c0aadebb526f8ee071": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0d805e59e38b46939950a16374f5fc54": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "96b771d4dfce49a0945b5be6496b0e63": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_85c582f080cf4739a723785771233f2a", "IPY_MODEL_e42a070a15ac40a889248929de47980e", "IPY_MODEL_101e230c4fa64928b661ea8cd48bbac9", "IPY_MODEL_3880e4e5e45046cba4a40cf6291e2fdc"], "layout": "IPY_MODEL_93693cb7d9134993bc6981aa164b2fdf"}}, "85c582f080cf4739a723785771233f2a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_58384ab1a4db42249eb2fb847a9e21ed", "style": "IPY_MODEL_ffa18e2d8d5d4cd09c1afe096dbf0448", "value": "<h5><b>Additional Outputs</b></h5>"}}, "58384ab1a4db42249eb2fb847a9e21ed": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ffa18e2d8d5d4cd09c1afe096dbf0448": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e42a070a15ac40a889248929de47980e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_674f6c04946641ea8ad72de03f0f4b85", "style": "IPY_MODEL_b9bf61ae4a7d4fefb58fb18948c16c51", "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. 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Cross Entropy on the full data set is -1.94.</div>"}}, "4fcd47a41b9f4489b32fffc92ac884da": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "72129af83f5a4b059133308c032ba073": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4b8869601db243a8881b7293d1495162": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "54f7375d059b4fa8b17aa8b765fd2f1b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6d732b4f94f4471f9c9510052db62ae9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3880e4e5e45046cba4a40cf6291e2fdc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": 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"model_module_version": "1.2.0", "state": {}}, "b2514de4c6244a09a9fd7a08b82aa907": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9b9fb27f928f49fc95148da25585ef84": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4fdf926132b74ac09feb1309e6e1bbea": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_6df8f926171a476b86bdb28951d10d65", "IPY_MODEL_6d843b7c7a1d4cb8bdc2eb945a1d4eb2"], "layout": "IPY_MODEL_e9ba7897ecc34243a83b42888b4ced0f"}}, "6df8f926171a476b86bdb28951d10d65": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_80b983cbba464076a95d2c26d66ac517", "style": "IPY_MODEL_1ab5662d58b74e8899dddc17985a8287", "value": "<span id=\"summary_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-0KB68\"></span>"}}, "80b983cbba464076a95d2c26d66ac517": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1ab5662d58b74e8899dddc17985a8287": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6d843b7c7a1d4cb8bdc2eb945a1d4eb2": {"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_c93bb0e734c74c8483c12aef891d8d1f"], "layout": "IPY_MODEL_a124860b9db24abba027ff461bcd8e7b", "selected_index": null}}, "c93bb0e734c74c8483c12aef891d8d1f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_ccb1cca02f664ba8a65f0d0d5f806adc", "IPY_MODEL_1a2e8803eb164dfaa0f41b6da6c1fdaf", "IPY_MODEL_c4939c279dd343eaaa259d9c53839e5f", "IPY_MODEL_d7ecf1f2de2c48e788f5a25b86ca6379", "IPY_MODEL_123c4b6de8ff4e1593b308907a62c572"], "layout": "IPY_MODEL_e18630b9112e4fc3ba39eede68ec4f8e"}}, "ccb1cca02f664ba8a65f0d0d5f806adc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_347faed6761e4e33ab13d514795ab40b", "style": "IPY_MODEL_a13969921a034b0c997cde85767dcb08", "value": "<style type=\"text/css\">\n#T_81af2 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_81af2 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_81af2 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_81af2 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_81af2 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_81af2\">\n  <thead>\n    <tr>\n      <th id=\"T_81af2_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_81af2_level0_col1\" class=\"col_heading level0 col1\" >Check</th>\n      <th id=\"T_81af2_level0_col2\" class=\"col_heading level0 col2\" >Condition</th>\n      <th id=\"T_81af2_level0_col3\" class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_81af2_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_81af2_row0_col1\" class=\"data row0 col1\" ><a href=\"#ImagePropertyDrift_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-0KB68\">Image Property Drift</a></td>\n      <td id=\"T_81af2_row0_col2\" class=\"data row0 col2\" >drift score < 0.2 for image properties drift</td>\n      <td id=\"T_81af2_row0_col3\" class=\"data row0 col3\" >Passed for 7 properties out of 7 properties.\nFound property \"RMS Contrast\" has the highest numerical drift score: 0.17</td>\n    </tr>\n    <tr>\n      <td id=\"T_81af2_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_81af2_row1_col1\" class=\"data row1 col1\" ><a href=\"#LabelDrift_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-0KB68\">Label Drift</a></td>\n      <td id=\"T_81af2_row1_col2\" class=\"data row1 col2\" >categorical drift score < 0.15 and numerical drift score < 0.15</td>\n      <td id=\"T_81af2_row1_col3\" class=\"data row1 col3\" >Passed for 1 label properties out of 1 label properties.\nFound label property \"Samples Per Class\" has the highest categorical drift score: 0</td>\n    </tr>\n    <tr>\n      <td id=\"T_81af2_row2_col0\" class=\"data row2 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_81af2_row2_col1\" class=\"data row2 col1\" >Property Label Correlation Change</td>\n      <td id=\"T_81af2_row2_col2\" class=\"data row2 col2\" >Train-Test properties' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_81af2_row2_col3\" class=\"data row2 col3\" >0 PPS found for all properties</td>\n    </tr>\n    <tr>\n      <td id=\"T_81af2_row3_col0\" class=\"data row3 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_81af2_row3_col1\" class=\"data row3 col1\" >New Labels</td>\n      <td id=\"T_81af2_row3_col2\" class=\"data row3 col2\" >Percentage of new labels in the test set is less or equal to 0.5%</td>\n      <td id=\"T_81af2_row3_col3\" class=\"data row3 col3\" >No new labels were found in test set.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "347faed6761e4e33ab13d514795ab40b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a13969921a034b0c997cde85767dcb08": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1a2e8803eb164dfaa0f41b6da6c1fdaf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bef62876660646c58af90669cd8d199b", "style": "IPY_MODEL_3c0f6c9aff6744239356621a85974e33", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "bef62876660646c58af90669cd8d199b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3c0f6c9aff6744239356621a85974e33": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c4939c279dd343eaaa259d9c53839e5f": {"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_e256fa56ed2d47b89de8b0d99c2040f1", "IPY_MODEL_7a89a56c947943b08ece15a693a67c8e", "IPY_MODEL_13408bf4e0f740c8ac108c47452bb7e8", "IPY_MODEL_6880c3ef3c7b46da9916f52b94a59af3"], "layout": "IPY_MODEL_d1fc5077b58d40088286d978eec5ca41"}}, "e256fa56ed2d47b89de8b0d99c2040f1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e4a966fc91514bff97ae599a530e4c99", "style": "IPY_MODEL_68fdb50b993e4349b4093ec4001f506a", "value": "<h4 id=\"ImagePropertyDrift_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-0KB68\"><b>Image Property Drift</b></h4>"}}, "e4a966fc91514bff97ae599a530e4c99": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "68fdb50b993e4349b4093ec4001f506a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7a89a56c947943b08ece15a693a67c8e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d3e741002c0a443d9e7700c948bd84f1", "style": "IPY_MODEL_c9fc82afe5484687b423e29498049942", "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>"}}, "d3e741002c0a443d9e7700c948bd84f1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c9fc82afe5484687b423e29498049942": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "13408bf4e0f740c8ac108c47452bb7e8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_50283f2cde42416cb6812968885a1f00", "style": "IPY_MODEL_fb3d434d557a423ea642681e5ffe715c", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_6c1d2 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6c1d2 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6c1d2 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6c1d2 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_6c1d2 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_6c1d2\">\n  <thead>\n    <tr>\n      <th id=\"T_6c1d2_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_6c1d2_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_6c1d2_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_6c1d2_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_6c1d2_row0_col1\" class=\"data row0 col1\" >drift score < 0.2 for image properties drift</td>\n      <td id=\"T_6c1d2_row0_col2\" class=\"data row0 col2\" >Passed for 7 properties out of 7 properties.\nFound property \"RMS Contrast\" has the highest numerical drift score: 0.17</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "50283f2cde42416cb6812968885a1f00": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fb3d434d557a423ea642681e5ffe715c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6880c3ef3c7b46da9916f52b94a59af3": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_74989dab65554872ac2b5921ad4dce61", "IPY_MODEL_0c1065c6830040a8936aec9edc6ce31d", "IPY_MODEL_e4ac1b106dd24605abfa0d0322a6a9eb", "IPY_MODEL_c9fd4172909b41c788b9c65d86e88966", "IPY_MODEL_835f57b40b1145e490f46472bfe1a2a3", "IPY_MODEL_729eebeb221343588140c55722f64a07", "IPY_MODEL_7d5fe1e13fd34a44ba20cc8e478d607d", "IPY_MODEL_5e96a37047a84e36bd2ba7853a80f356", "IPY_MODEL_145060edbe6a4decab810b3991559e95", "IPY_MODEL_5247fb9a95ea472091b792a66ee618c7", "IPY_MODEL_6d133d6969b94bb29cd9276f610e66f8"], "layout": "IPY_MODEL_f53e940cd1b04aff89be99956b10a6d6"}}, "74989dab65554872ac2b5921ad4dce61": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f3044078a7494b03b4c095067d53d1b2", "style": "IPY_MODEL_801e60e21d864cf4845e120a5364ec96", "value": "<h5><b>Additional Outputs</b></h5>"}}, "f3044078a7494b03b4c095067d53d1b2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "801e60e21d864cf4845e120a5364ec96": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0c1065c6830040a8936aec9edc6ce31d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ad39843c9d7d44eb8b0a941cf805baf9", "style": "IPY_MODEL_3e45a89df26c4b0c845c765fa3830849", "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>"}}, "ad39843c9d7d44eb8b0a941cf805baf9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3e45a89df26c4b0c845c765fa3830849": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e4ac1b106dd24605abfa0d0322a6a9eb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_174caf7451ca4aeda00f3691cf7e0479", "style": "IPY_MODEL_466009a9530c45419b29fb2d3a6be51f", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, 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{"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f53e940cd1b04aff89be99956b10a6d6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d1fc5077b58d40088286d978eec5ca41": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d7ecf1f2de2c48e788f5a25b86ca6379": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_44dc8a03361446e488838f50681e2024", "style": "IPY_MODEL_90d88d7b724843f1a29a76a9f9b1c99f", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "44dc8a03361446e488838f50681e2024": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "90d88d7b724843f1a29a76a9f9b1c99f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "123c4b6de8ff4e1593b308907a62c572": {"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_2801b372ac14412e8ea0435f6c60e61d", "IPY_MODEL_120ca4c22b4b4660accf51a4e171b694", "IPY_MODEL_c640b4d550bd4bd0a44091af05bdbe46", "IPY_MODEL_5f2b6e2529fd4aff85ddc7e2c68a4570"], "layout": "IPY_MODEL_a1c9804389e44e07be6a2255ad3ef6ac"}}, "2801b372ac14412e8ea0435f6c60e61d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_988bc564fa2444fdb5044326cbcb8803", "style": "IPY_MODEL_bc31c8db7e7a4723aa160fe6200a8713", "value": "<h4 id=\"LabelDrift_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-0KB68\"><b>Label Drift</b></h4>"}}, "988bc564fa2444fdb5044326cbcb8803": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bc31c8db7e7a4723aa160fe6200a8713": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "120ca4c22b4b4660accf51a4e171b694": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_043843f7ed6846ffb18e786483183efe", "style": "IPY_MODEL_6ba2863c217e45578e90ed2cc89897ec", "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>"}}, "043843f7ed6846ffb18e786483183efe": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6ba2863c217e45578e90ed2cc89897ec": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c640b4d550bd4bd0a44091af05bdbe46": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a52a8b1e2a31422d8adad9fe091c47ad", "style": "IPY_MODEL_daf24da2264f4e02b2d45ee09e064743", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_a12cd table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a12cd thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a12cd tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a12cd th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_a12cd td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_a12cd\">\n  <thead>\n    <tr>\n      <th id=\"T_a12cd_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_a12cd_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_a12cd_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_a12cd_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_a12cd_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.15 and numerical drift score < 0.15</td>\n      <td id=\"T_a12cd_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</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "a52a8b1e2a31422d8adad9fe091c47ad": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "daf24da2264f4e02b2d45ee09e064743": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5f2b6e2529fd4aff85ddc7e2c68a4570": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_74de7775e72240d7af03df164ae25be3", "IPY_MODEL_2912dfb4abaf429cbac442e59f2b6bbd", "IPY_MODEL_5d6319a858674e69805752a1b1e74a75", "IPY_MODEL_5d806fbc9e9b486cbf66d3f0c044340a", "IPY_MODEL_4462f19320b84340a2189764aaa64d02"], "layout": "IPY_MODEL_1dfd9bab34174985ae8a1f11ec87f12f"}}, "74de7775e72240d7af03df164ae25be3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f2e4f470df8641628bde53e235f97646", "style": "IPY_MODEL_f1a2e3cc06714d639a70edafbd9a9a33", "value": "<h5><b>Additional Outputs</b></h5>"}}, "f2e4f470df8641628bde53e235f97646": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f1a2e3cc06714d639a70edafbd9a9a33": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2912dfb4abaf429cbac442e59f2b6bbd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_af4d707cb03d4bac9edbb82876d17c9e", "style": "IPY_MODEL_b1fbca3c02794eb3a1ba73f538702504", "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>"}}, "af4d707cb03d4bac9edbb82876d17c9e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b1fbca3c02794eb3a1ba73f538702504": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5d6319a858674e69805752a1b1e74a75": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_73fae88c259b4f038beaa72df7f8b791", "style": "IPY_MODEL_1ae9d5050fe64c8fb4ecd1e98ae62e9c", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "73fae88c259b4f038beaa72df7f8b791": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": 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pre-wrap;\n}\n#T_64afe thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_64afe tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_64afe th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_64afe td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_64afe\">\n  <thead>\n    <tr>\n      <th id=\"T_64afe_level0_col0\" class=\"col_heading level0 col0\" >Check</th>\n      <th id=\"T_64afe_level0_col1\" class=\"col_heading level0 col1\" >Summary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_64afe_row0_col0\" class=\"data row0 col0\" ><a href=\"#ImageDatasetDrift_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\">Image Dataset Drift</a></td>\n      <td id=\"T_64afe_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_64afe_row1_col0\" class=\"data row1 col0\" ><a href=\"#ImagePropertyOutliers_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\">Image Property Outliers</a></td>\n      <td id=\"T_64afe_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_64afe_row2_col0\" class=\"data row2 col0\" ><a href=\"#ImagePropertyOutliers_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\">Image Property Outliers</a></td>\n      <td id=\"T_64afe_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_64afe_row3_col0\" class=\"data row3 col0\" ><a href=\"#PropertyLabelCorrelation_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\">Property Label Correlation</a></td>\n      <td id=\"T_64afe_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_64afe_row4_col0\" class=\"data row4 col0\" ><a href=\"#PropertyLabelCorrelation_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\">Property Label Correlation</a></td>\n      <td id=\"T_64afe_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_64afe_row5_col0\" class=\"data row5 col0\" ><a href=\"#ConfusionMatrix_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\">Confusion Matrix</a></td>\n      <td id=\"T_64afe_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_64afe_row6_col0\" class=\"data row6 col0\" ><a href=\"#ConfusionMatrix_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\">Confusion Matrix</a></td>\n      <td id=\"T_64afe_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_64afe_row7_col0\" class=\"data row7 col0\" ><a href=\"#HeatmapComparison_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\">Heatmap Comparison</a></td>\n      <td id=\"T_64afe_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"}}, "a49111957606416cb3ac301d1f9a784c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e42bf1827ecc404687782bcaf48c9ac5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "597d1bd8ef6948f1a3b2d78e4c58e3a5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c1ee05b149de4af8a19ef0fc30874ec9", "style": "IPY_MODEL_3f7690200b77421a86e0a2477d2bcdf1", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "c1ee05b149de4af8a19ef0fc30874ec9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3f7690200b77421a86e0a2477d2bcdf1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fbcd4e6d39b449698f93e888a9390713": {"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_d1cfea74259b46f68faf559fdd4bc6d3", "IPY_MODEL_4f6a99bf307d4253b8406697972a09c0", "IPY_MODEL_fef744ca677a431480c3f927a49329a1"], "layout": "IPY_MODEL_2f6248c785a7440fa17c43cf996e8061"}}, "d1cfea74259b46f68faf559fdd4bc6d3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8078ec0a26ac49719e3e0435ec77f97d", "style": "IPY_MODEL_281e8b13a1424205b22cc96a508af4ba", "value": "<h4 id=\"ImageDatasetDrift_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\"><b>Image Dataset Drift</b></h4>"}}, "8078ec0a26ac49719e3e0435ec77f97d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "281e8b13a1424205b22cc96a508af4ba": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4f6a99bf307d4253b8406697972a09c0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d61dd376654d4e2eba2cd25da592e960", "style": "IPY_MODEL_9ac0ea5c50f64d3e955de07c4fdbf4ab", "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>"}}, "d61dd376654d4e2eba2cd25da592e960": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9ac0ea5c50f64d3e955de07c4fdbf4ab": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fef744ca677a431480c3f927a49329a1": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_1aad9d4aafea408f8c162b240935a196", "IPY_MODEL_f5a1ab371a4d48bda0c3c8daf3138653", "IPY_MODEL_26ec67674d7f43d89bf2bd80ae79966d", "IPY_MODEL_7f30fdd05d4b42c0b6f83c8a7e510b54", "IPY_MODEL_de2e30dedce14807a914bac004476e47", "IPY_MODEL_0d1dba89569645d086e37c556dfedd04", "IPY_MODEL_fdcb2bd5673a40ba95ccb27d1d9597db", "IPY_MODEL_721b8a05f50b4032babf8afb39807ff5", "IPY_MODEL_e3465c354f3340feb8538c9cafed6c9c"], "layout": "IPY_MODEL_87e5af936f9e43f49b0d6a0d25f8d538"}}, "1aad9d4aafea408f8c162b240935a196": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8bb82cee514744e4942f558152be0f7a", "style": "IPY_MODEL_44756a50a55e462ba266431194219fac", "value": "<h5><b>Additional Outputs</b></h5>"}}, "8bb82cee514744e4942f558152be0f7a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "44756a50a55e462ba266431194219fac": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f5a1ab371a4d48bda0c3c8daf3138653": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0338dcca084f44679b031d50ce6cc670", "style": "IPY_MODEL_b7173af6373b40c88c62c97586731e85", "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>"}}, "0338dcca084f44679b031d50ce6cc670": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b7173af6373b40c88c62c97586731e85": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "26ec67674d7f43d89bf2bd80ae79966d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_01943635945c4b99a7d0b894387a6932", "style": "IPY_MODEL_42d79ba8a0b446a7b8f0e38ab8fb881f", "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>"}}, "01943635945c4b99a7d0b894387a6932": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "42d79ba8a0b446a7b8f0e38ab8fb881f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7f30fdd05d4b42c0b6f83c8a7e510b54": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#01B8AA"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.1], "y": ["Drift Score"], "type": "bar", "uid": "a5b335bb-f8bd-4fe3-a28c-805dbf5a0e9b"}, {"base": 0.1, "marker": {"color": "#F2C80F"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.1], "y": ["Drift Score"], "type": "bar", "uid": "2d321f0c-ff86-4306-9bba-ffdd613ae136"}, {"base": 0.2, "marker": {"color": "#FE9666"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.09999999999999998], "y": ["Drift Score"], "type": "bar", "uid": "0c37d5c5-e2eb-43d9-b9cc-dde64d61d0a6"}, {"base": 0.3, "marker": {"color": "#FD625E"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.0022300469483567675], "y": ["Drift Score"], "type": "bar", "uid": "b24e4604-dbf5-4ee7-ba1b-ad38d814de15"}], "_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, 0.40223004694835673], "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}}, "de2e30dedce14807a914bac004476e47": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4057336b72c24fde9d41ed492247d1ba", "style": "IPY_MODEL_aedf1e3d4a6f4e4fafac4e00ed753877", "value": "<div><h3>Main features contributing to drift</h3></div>"}}, "4057336b72c24fde9d41ed492247d1ba": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "aedf1e3d4a6f4e4fafac4e00ed753877": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0d1dba89569645d086e37c556dfedd04": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9c45a54e10f04b5a9bdd9a784396e393", "style": "IPY_MODEL_945118e528aa4b7f94ad95fd7de49a90", "value": "<div>Showing only the top 3 columns, you can change it using n_top_columns param</div>"}}, "9c45a54e10f04b5a9bdd9a784396e393": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "945118e528aa4b7f94ad95fd7de49a90": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fdcb2bd5673a40ba95ccb27d1d9597db": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cfa627b3c38f40d4be3136f34fd93f0b", "style": "IPY_MODEL_33bee6b0718d456abe71144d573e61da", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "cfa627b3c38f40d4be3136f34fd93f0b": 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"e3465c354f3340feb8538c9cafed6c9c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ee5af9c911804fa7a0b2329c91a972a6", "style": "IPY_MODEL_18b37f1e83f14b01a2e412a512c78582", "value": "<br><a href=\"#summary_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\" style=\"font-size: 14px;\">Go to top</a>"}}, "ee5af9c911804fa7a0b2329c91a972a6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "18b37f1e83f14b01a2e412a512c78582": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "87e5af936f9e43f49b0d6a0d25f8d538": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2f6248c785a7440fa17c43cf996e8061": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cfc3d1a0663d45c0ba9c93008969942d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_87de4ee6c070473ab9675d1c170bb8ce", "style": "IPY_MODEL_d93cd9f94a0b40fd8985e7e1f1007c42", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "87de4ee6c070473ab9675d1c170bb8ce": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d93cd9f94a0b40fd8985e7e1f1007c42": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6d67e6f888c64f7e8850a1af1bf9ff76": {"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_061bab8df41a408a96aafbd6f916fdbb", "IPY_MODEL_5080af248c0c40bbada59563502fa47e", "IPY_MODEL_abf65ce9766a475e9abdcf8d1d050e44"], "layout": "IPY_MODEL_31059b4be5e74ba0bab93a04ca2a0a08"}}, "061bab8df41a408a96aafbd6f916fdbb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d3f3ddf6c7514ae6b7680783db997c3e", "style": "IPY_MODEL_e5b720e33733442daba43451f49334b0", "value": "<h4 id=\"ImagePropertyOutliers_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\"><b>Image Property Outliers</b></h4>"}}, "d3f3ddf6c7514ae6b7680783db997c3e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e5b720e33733442daba43451f49334b0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5080af248c0c40bbada59563502fa47e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d39d7f62503e4e4f8be065fcfc93844d", "style": "IPY_MODEL_0dd2a010067b480db2ff9e925841b78a", "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>"}}, "d39d7f62503e4e4f8be065fcfc93844d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0dd2a010067b480db2ff9e925841b78a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "abf65ce9766a475e9abdcf8d1d050e44": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_2b0c23b000ca49158d34c7af8d5c082b", "IPY_MODEL_2e3e81f6e0bf4de7870174b39d4a8a8e", "IPY_MODEL_3ffbe23e853249059be5ff9780a2cdce", "IPY_MODEL_af3604af5ef2430eaa40578b5ea700de", "IPY_MODEL_bae5e3560562455199efcd00c353f092"], "layout": "IPY_MODEL_9f9bf2976ae94844bfdd5fe312857963"}}, "2b0c23b000ca49158d34c7af8d5c082b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6fcd724ad2914c2f9a19e2d7a437b7c5", "style": "IPY_MODEL_5ad9a19a1f244382abb2975a4c202a92", "value": "<h5><b>Additional Outputs</b></h5>"}}, "6fcd724ad2914c2f9a19e2d7a437b7c5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5ad9a19a1f244382abb2975a4c202a92": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2e3e81f6e0bf4de7870174b39d4a8a8e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_650ea4c77ce741a29d062ae77e3a8c49", "style": "IPY_MODEL_818923da46fb47f4a44a33bd017b0508", "value": "<div>\n<style>\n    .TQVLDG-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .TQVLDG-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .TQVLDG-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    .TQVLDG-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: 4\n</div>\n<div>\nNon-outliers range: 59.96 to 210.19\n</div>\n<div class=\"TQVLDG-container\">\n    <div class=\"TQVLDG-row\">\n        <h5 class=\"TQVLDG-item\">Brightness</h5>\n        <div class=\"TQVLDG-item\">51.08</div><div class=\"TQVLDG-item\">51.06</div><div class=\"TQVLDG-item\">59.09</div><div class=\"TQVLDG-item\">210.32</div>\n    </div>\n    <div class=\"TQVLDG-row\">\n        <h5 class=\"TQVLDG-item\">Image</h5>\n        <div class=\"TQVLDG-item\"><img 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   </div>\n</div>\n\n<style>\n    .XEQLTS-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .XEQLTS-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .XEQLTS-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    .XEQLTS-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: 6\n</div>\n<div>\nNon-outliers range: 21.49 to 56.17\n</div>\n<div class=\"XEQLTS-container\">\n    <div class=\"XEQLTS-row\">\n        <h5 class=\"XEQLTS-item\">RMS Contrast</h5>\n        <div class=\"XEQLTS-item\">68.74</div><div class=\"XEQLTS-item\">68.94</div><div class=\"XEQLTS-item\">83.44</div>\n    </div>\n    <div class=\"XEQLTS-row\">\n        <h5 class=\"XEQLTS-item\">Image</h5>\n        <div class=\"XEQLTS-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"XEQLTS-item\"><img 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class=\"XEQLTS-item\"><img 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   </div>\n</div>\n\n<style>\n    .XWEFHS-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .XWEFHS-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .XWEFHS-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    .XWEFHS-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: 7\n</div>\n<div>\nNon-outliers range: 0.3 to 0.48\n</div>\n<div class=\"XWEFHS-container\">\n    <div class=\"XWEFHS-row\">\n        <h5 class=\"XWEFHS-item\">Mean Red Relative Intensity</h5>\n        <div class=\"XWEFHS-item\">0.56</div><div class=\"XWEFHS-item\">0.68</div><div class=\"XWEFHS-item\">0.71</div>\n    </div>\n    <div class=\"XWEFHS-row\">\n        <h5 class=\"XWEFHS-item\">Image</h5>\n        <div class=\"XWEFHS-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"XWEFHS-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"XWEFHS-item\"><img src=\"data:image/png;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA0JCgsKCA0LCgsODg0PEyAVExISEyccHhcgLikxMC4pLSwzOko+MzZGNywtQFdBRkxOUlNSMj5aYVpQYEpRUk//2wBDAQ4ODhMREyYVFSZPNS01T09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT0//wAARCADIAMgDASIAAhEBAxEB/8QAGgABAQADAQEAAAAAAAAAAAAAAQACBAUDBv/EADUQAAICAQQBAgUABwkBAAAAAAABAgMRBCExQRIFURMiMmFxFEJDUoGRwRUjJDM0YnKC0fH/xAAaAQEBAQEBAQEAAAAAAAAAAAAAAQIDBAUG/8QAJxEBAQACAgIBBAAHAAAAAAAAAAECEQMhEjEyBBMiQRQjUWFikbH/2gAMAwEAAhEDEQA/AOMyXBMkjyP1YaMlwBZACyQAPQdmXQARLYiAgZJk+AJbk0KwiZFS2BkyCgUDFcAAl2WQLgmy6yACA9AgFrYiZAZgyBlZK36B8DEeSoxXBYHgAFvYxyEmaGmvlC+VVsnLMnv7MM3LToLkWYpmRGtjBEQVEiYpADJCwCh8iG5EUgzKPAdhDjYMD0AVEHZljYDHJDggMlnIsMlk05kkYsYsiphyLRIqMZI5WuXjqG87tJo6zOZ6rDCjYsbbMOWd1NtrS3K2qL77R7pnF9Ou8L3BvaXH5OxHcWLxZ+U29AECOpHoxW7FhRyYsTytsjXFyk8JAZTtjVByk9keem1MdQpOKaw8bnNcrtfZhfLWjqaemNMFGKwhpzxzud3PT3AhXBHUMlwQgGDLoxLICRYIBBcihKwsAlgQYCYsUDKLo19VV8WqUH2jYXBjJBjKPm94STXMWd7T2KyuMk85Rx9dDw1M+svyRt+mXL4brb3jvj7Fvp5uG+OdxdPIs17NRCuEZ7yTePlWT1rmrK1NJpP3WGZeuZT0zXIs8YX1znOEW/KDw00F10K4OU5JIL5T2x1OojTBt/wXucxO7X2eLeILnAwhZrrnN5UFtk6lFMaoqMVhIvpy75b/AI/9FFMaq1GK2R7xQtIlsR3nQHguwbIpBjyGAEOzLoEBl0QNkBEQmnMJkxwQAiJkAA1sMiXASuV6rVtGxdbM0tPNQvWXiEtpfg7WsqVtMo4ztsfPy4x7cmo8XN+OXlH0NEIV1qMElFcI9G9jV0N3xdNGXeMMtZf8KiUk9+iad/uSTbV101Xqq5wfzLnfo8Yueu1EU8+C6NRzc2nJ5fbZ1/S4r9H8kt22WzTlhl9zLX6bldcYRUYxSSR6oEOTD3RMgXIsKkTQouwJcB2OQCkCfIpbEAyFLcgjLACHZpyQN7ic31Gy+uyt1OSW/C7CZZeM26JHOp9Qk1i2qe3Litkb7nGMPKTSj7sEyl9MgLkGGhLdHzuprdd04PhP/wCH0fRy/UalKxNLeawvyt1/U1Hl5puNb0zUfDsnCckovdZPHV6p6izbaC4R45aflw+Dz4exuR4ss7MfF6VuClFyy12dn0xr9GwvdnCSb2ydf0lt1Tb/AHv6Eyjr9Nl+WnURkYRMzk+pCkTLokFQgxQUMUtiHoAEPcskEQ9EAomiA04k8LrHCyuKhlTeG88Htn3MZQhJxlKKbi8xb6YSjCKcI2QcJxUovlPsewcklu0DbyjXctR5fEXwsfRjg9ZHhbrqKWlOay/bc17fU6sPwhKXs+Cudzk/bbnYoptvCRwtdqZ2aj5Z5ri8xweWouvsbdkpYfXRrtbZOkjw83Nb1HpOTlLy9+fyDSeF32Ed1hk9yuW99rxw+cr3RvaTXKmCrdeEu0zSiRL21hlcLuPotPdG2ClB5TNhPJxvS7MSlX/E68DlZqvrcOfljKzFJkJHVMuiSJgRCiAkgaMlwHYU9ERABEzystjVBym0kuWaefbNs87LoVxzOajn3Zz9R6mvpoWf9z/8Ofm7UWYj5Sl/Nl045csnrt0I6+jTUquuU7MZw3zyaVuo1Gql474fEY9m3pfSv1r3/BP+p0K6qKPpjCJXPWWU1eo5dPpl0prziox7eUzq16OiC2rjnGMsxn6hpa/2n8k2jWt9Zpi/GEZTf8kNG8cWOtporjPyUI1+GyX1eWTiS3+nhe5t6m6Wt1DlCr5lHHPBrTpnBKU1hdbmo83JfL0yjCE4zmrFHwS+V8y/Bj0GMbolkMwxxusseWC23FBqNv07/U/9Ttx4OL6cv8S/+J2orY55e30fpvgzQgjIy9KiZNAhAcYRiLZLcAImKQAQ9kB5X2qqqVks4iujg3am/W2qMYvGdoo+hklJNPdGMaoQXyxUc+ywbjxckuX76crTemNpO9/9V/6dGumrTxxCKSM52Qri3KSSXucLV6i7V6p10y+RbLfGfuak25ZZzCdOhq/UoVJxq+eX2eyOQ536yyUm8433eyML6ZUYUpqWeVFnpl1Vr+7SbW2+5rWnDLK5XtjdObjCucVGKXXZsSlpXpowreW/tumam/a5CKwyJN7Kcq2/F88mf+ZGMdlhbNvgp4aW2MGKwibXWroJeMG8fYPsMvzk2Fob3CMopZfWeC7Jjb1jHg3nC9iRs/2dqPeP8za03pzjLytl5Y4WDNsdceLO3uNXQ2V1X+U5NN7L2O5XKMopxaaNOfp1Njy44f22PB6XVaablppZj+62Zuq9WEz45qzc/s66E5VfqcoS8dRW4texvU6mu5ZhJNE07Y8mOXqtjokGUMSNligbLIExyY8kFJAlsQGDmkjneoeo/Cj4Uyi5v+ODn6zWWWzcYycYJ7Y7NVJI6yPk58tvUeuom7YxnZe5zf6uODCqGXmUsLOCUknlIpy8pOWMZNbcdTe21qHRCPhU/ivH1NcGplt5fIZJhbWaa8cA3vsEW28JZPaGjvt4h4/8tjLU3lOnl5YW7PSnT3X/AER2zjLN/TemKElK1+T9ujpQqUVsiXL+jvhwW/Nq6bRQqjxmXOWbcYYM8Dgw9mOMk1GPiiwZYLBG5AkODJIgrys09dkcTin+Uadnpdbl5VScH1g6TAbZuGOXuOaoa3T4UGro+z2wEfVFHa6uUX9jpYyYWUQsWJxTX3Klxs+NeFeuon+1ivzsbCsi+Gc+/wBKhJuVb8PslsaktLqtM04Nvf8AUGox554/Kf6d2LQtnBr119U/mk37qR06NbTaliaTfT2Y03jy45em5sRgpprZkRvb5jU6eyiXzRzF8NBVp7rVmFba9+D6Z1p9Eq0ujp5Pn/w83vb56Og1LWfFR+zZ7Q9LtcU5Tin7YydzwROA8q1Pp8I+f1Ghlp6vOVie+MJHjpofG1EYddnU9XhZKmMa4SlmW+Fk8/StJOKdlkXFvZJ7bF31ty+1/MmMnTdp08ILEYJfhHuq17GaWEZYOb3SaYKKMsCWA0CwKQsKEiECKmRCgoEh6ChIWCEAaMHHJ6MktwPCdEJ/VCL/ACsmpL0qmSfj5Rf5Ok0WBtnLDHL3HLhotTV8teqxH2wR08bkNpOORFgi4NOQwTWwkBi4rBKKzwZNEgowKEiKBIAp6AiQVNAkZERoMmQMKQFF2BJbCWQCklsWSAmGSW7FoAIySIDHJNkRt50XZEQJEQVAyIKVwBERSREFHZMiIsSLG5EGjwBEBMCIKULIgBCyICzsREB//9k=\"/></div>\n    </div>\n</div>\n\n<style>\n    .APKWYG-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .APKWYG-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .APKWYG-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    .APKWYG-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: 12\n</div>\n<div>\nNon-outliers range: 0.28 to 0.37\n</div>\n<div class=\"APKWYG-container\">\n    <div class=\"APKWYG-row\">\n        <h5 class=\"APKWYG-item\">Mean Green Relative Intensity</h5>\n        <div class=\"APKWYG-item\">0.13</div><div class=\"APKWYG-item\">0.15</div><div class=\"APKWYG-item\">0.26</div><div class=\"APKWYG-item\">0.42</div><div class=\"APKWYG-item\">0.42</div>\n    </div>\n    <div class=\"APKWYG-row\">\n        <h5 class=\"APKWYG-item\">Image</h5>\n        <div class=\"APKWYG-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"APKWYG-item\"><img 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   </div>\n</div>\n\n<style>\n    .DQUDYK-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .DQUDYK-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .DQUDYK-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    .DQUDYK-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: 5\n</div>\n<div>\nNon-outliers range: 0.19 to 0.38\n</div>\n<div class=\"DQUDYK-container\">\n    <div class=\"DQUDYK-row\">\n        <h5 class=\"DQUDYK-item\">Mean Blue Relative Intensity</h5>\n        <div class=\"DQUDYK-item\">0.16</div><div class=\"DQUDYK-item\">0.12</div><div class=\"DQUDYK-item\">0.17</div>\n    </div>\n    <div class=\"DQUDYK-row\">\n        <h5 class=\"DQUDYK-item\">Image</h5>\n        <div class=\"DQUDYK-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"DQUDYK-item\"><img 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class=\"DQUDYK-item\"><img 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VImD3PzCAeTAkgIQnQrlrUr3TXVSBCEUKtKqAIzCEmRGRMIQCBgIQiNYnfMuDCEJn7+1/j9RLfH4nzCEqsi+WjxRH4oxCEXxh7S8b5h40IR/GHtI3/IkG1IHrCElOYNqptTk+XJMah3+o4EIR2Ye1orVE7y3xhjA4hCVnqJt9jCEIsh7X//Z\"/></div>\n    </div>\n</div>\n</div>"}}, "650ea4c77ce741a29d062ae77e3a8c49": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "818923da46fb47f4a44a33bd017b0508": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3ffbe23e853249059be5ff9780a2cdce": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_41e17ab1ec24460492e4147ce72f766f", "style": "IPY_MODEL_113a326e54b14df0884da9938bb66ca1", "value": "<div><h5><b>Properties With No Outliers Found</h5></b></div>"}}, "41e17ab1ec24460492e4147ce72f766f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "113a326e54b14df0884da9938bb66ca1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "af3604af5ef2430eaa40578b5ea700de": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b4b8d6dde3cb447e8e08422af070757a", "style": "IPY_MODEL_a62de60fa62442e685fb951f4f256f63", "value": "<style type=\"text/css\">\n#T_4e79b table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_4e79b thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_4e79b tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_4e79b th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_4e79b td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_4e79b\">\n  <thead>\n    <tr>\n      <th id=\"T_4e79b_level0_col0\" class=\"col_heading level0 col0\" >More Info</th>\n      <th id=\"T_4e79b_level0_col1\" class=\"col_heading level0 col1\" >Properties</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_4e79b_row0_col0\" class=\"data row0 col0\" >No outliers found.</td>\n      <td id=\"T_4e79b_row0_col1\" class=\"data row0 col1\" >Aspect Ratio, Area</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "b4b8d6dde3cb447e8e08422af070757a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a62de60fa62442e685fb951f4f256f63": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bae5e3560562455199efcd00c353f092": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2024f3174f2946d5af8994d40815f914", "style": "IPY_MODEL_c3309260228742a9b2812550bc2681c6", "value": "<br><a href=\"#summary_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\" style=\"font-size: 14px;\">Go to top</a>"}}, "2024f3174f2946d5af8994d40815f914": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c3309260228742a9b2812550bc2681c6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9f9bf2976ae94844bfdd5fe312857963": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "31059b4be5e74ba0bab93a04ca2a0a08": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6f7b44cacfc341e58932469fe5be1463": {"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_d1c37f7811c84cc28b966eba3861b45a", "IPY_MODEL_e4ddf6079f364c65adff6ff91398f391", "IPY_MODEL_f50f1701d17049938085ece3caeceb9f"], "layout": "IPY_MODEL_7f6909646db54b079f093dcf82a4c9a8"}}, "d1c37f7811c84cc28b966eba3861b45a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1b6784ae1623482bb2fcd82eb478605c", "style": "IPY_MODEL_38a7905a376d4eaaaaef106f0976f6bf", "value": "<h4 id=\"ImagePropertyOutliers_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\"><b>Image Property Outliers</b></h4>"}}, "1b6784ae1623482bb2fcd82eb478605c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "38a7905a376d4eaaaaef106f0976f6bf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e4ddf6079f364c65adff6ff91398f391": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9ba147571ffe4f7ba8133eda908f7b13", "style": "IPY_MODEL_64fc4cda0eff4b3e8698923e8adb69e7", "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>"}}, "9ba147571ffe4f7ba8133eda908f7b13": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "64fc4cda0eff4b3e8698923e8adb69e7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f50f1701d17049938085ece3caeceb9f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_2a8ee1468de14b85962de8b820995156", "IPY_MODEL_b0ae2224692141f6a57ce333a26e2b42", "IPY_MODEL_59f73b3092b146669b54f352e2b1bf98", "IPY_MODEL_af9281b836224d24a9fffb294f8939ef", "IPY_MODEL_bce2361d183d49bfa537e337dc04d036"], "layout": "IPY_MODEL_b1123b05872a476d970a305d25e4bbff"}}, "2a8ee1468de14b85962de8b820995156": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f9a7110fef8a421b844f7e6faa6d2ea4", "style": "IPY_MODEL_f9f29d6898044dd4b8c8b4f165688ce0", "value": "<h5><b>Additional Outputs</b></h5>"}}, "f9a7110fef8a421b844f7e6faa6d2ea4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f9f29d6898044dd4b8c8b4f165688ce0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b0ae2224692141f6a57ce333a26e2b42": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_503ff7bd6aa8407ead5adfa6ef743a7e", "style": "IPY_MODEL_b7c0b12183a24d15a772307e4ba94d99", "value": "<div>\n<style>\n    .MNPWNY-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .MNPWNY-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .MNPWNY-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    .MNPWNY-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: 12\n</div>\n<div>\nNon-outliers range: 51.68 to 229.06\n</div>\n<div class=\"MNPWNY-container\">\n    <div class=\"MNPWNY-row\">\n        <h5 class=\"MNPWNY-item\">Brightness</h5>\n        <div class=\"MNPWNY-item\">12.59</div><div class=\"MNPWNY-item\">26.46</div><div class=\"MNPWNY-item\">28.95</div><div class=\"MNPWNY-item\">238.8</div>\n    </div>\n    <div class=\"MNPWNY-row\">\n        <h5 class=\"MNPWNY-item\">Image</h5>\n        <div class=\"MNPWNY-item\"><img 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VCEDcvmzdrKOr/hZv8hUqbIA6N7TzBCDC8JFpopXA3c593dlvLnvCUJjbWG928UtB62K35DljcegKBjD+zk+qEz3aFxHJpKEFPCTpfstULHwl1ox3C127BSLSqnI61VlHMK4dlUlZ+1Nd2N0InYQAVHfPBIfVStAyqCIh0Uo5XNkDaAaEqzM7LE93QKGhFob9UtcbUj/AEQJQfwrVZVeh/hmhWFUV6yhpa6Lh1cEcrRsHDb06KUBkMbI42hrQMrWgWACkUTxmlaOQ1QSBI4XFkuyVAl1G0faOKkTQ21+6CvXyZY8o3KWgdenA6KvXZn1AAGjR+KsULCyHXclFWkISIhU21735qNkrjUyRlvlaAQeqlQVaCnFNC5rQPM8uU04JjKeBYJk7g2IlAkjb0zm/dQkDr07j90oRYy8K1az0W0sfCxle0dlsKRfQOxVcXc9p7Kc7KvTSZy490SJnnJGT0CqURvnCmrX5ID30VTDn3mcOyEXqdobFZQYkfsMo5lWoxZio4pJlyN6lBYoRamarKhpRlgYO11KqgTbefN2skkDiWhp56p6ASoSIEcbJUWuNUqBmQXJIBKcNNkJHmzSeyBSQkaQ4XGyoVUjmtZr3VmidmpwUXCawBvzKVNdfMLIe4MaXHluiHKpKZHNka9ugdoeytg3F0hFxY6hBRp5TLHNFsQxCnipmxTPe3Z4AshFUcNB4+2gC1AbvIUUcDIjdo1Uke5KkWnkXBCq0bcrHaa5irSZG3Lf1VRUxU2ptPVZmDVIfVFvQFauKC9IfoudwqTh4sG20cCos06yP3As3E2OfURADS602izbILQTci5RkN0aB0CVMkcI2F52G6rUtT7RI4j3QVRcQNk12tgE5AqEIQIlTW3tqlugEjtiEqYDdzh0QUMS0aNdgrGGkGjYQs/HJeHSuN9QFo4fl9iiLNsoUjV0sW1UdR/DyWNvKVI82aVFM0vpnN6iyrJ0Ds0DD90KRMhZkia08gna5+1kA45Wlx5C6EqEEchytuUsWrUIUilc7LbubJW80IQRVMXFjyFVYsMgbKJMurUIQml5pvdOQhVGfjj3Mw6Qs3sqfh8lzA7kQhCK201pJe7shCiHpUIVCJhJ4tuouhCB6rxPBmlHdCEWMHxA18wZCz4na+i36FnDo4mHcNF0IUi0lXKGBjebnKbcBCFWTkW1QhA0OBe5vRCEIP/Z\"/></div>\n    </div>\n</div>\n\n<style>\n    .GMPNYI-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .GMPNYI-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .GMPNYI-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    .GMPNYI-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: 10.03 to 74.01\n</div>\n<div class=\"GMPNYI-container\">\n    <div class=\"GMPNYI-row\">\n        <h5 class=\"GMPNYI-item\">RMS Contrast</h5>\n        <div class=\"GMPNYI-item\">83</div><div class=\"GMPNYI-item\">83.88</div><div class=\"GMPNYI-item\">85.56</div>\n    </div>\n    <div class=\"GMPNYI-row\">\n        <h5 class=\"GMPNYI-item\">Image</h5>\n        <div class=\"GMPNYI-item\"><img 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\"/></div><div 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\"/></div>\n    </div>\n</div>\n\n<style>\n    .YPIXIP-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .YPIXIP-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .YPIXIP-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    .YPIXIP-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: 25\n</div>\n<div>\nNon-outliers range: 0.25 to 0.5\n</div>\n<div class=\"YPIXIP-container\">\n    <div class=\"YPIXIP-row\">\n        <h5 class=\"YPIXIP-item\">Mean Red Relative Intensity</h5>\n        <div class=\"YPIXIP-item\">0.07</div><div class=\"YPIXIP-item\">0.13</div><div class=\"YPIXIP-item\">0.22</div><div class=\"YPIXIP-item\">0.6</div><div class=\"YPIXIP-item\">0.64</div><div class=\"YPIXIP-item\">0.71</div>\n    </div>\n    <div class=\"YPIXIP-row\">\n        <h5 class=\"YPIXIP-item\">Image</h5>\n        <div class=\"YPIXIP-item\"><img 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\"/></div><div class=\"YPIXIP-item\"><img src=\"data:image/png;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA0JCgsKCA0LCgsODg0PEyAVExISEyccHhcgLikxMC4pLSwzOko+MzZGNywtQFdBRkxOUlNSMj5aYVpQYEpRUk//2wBDAQ4ODhMREyYVFSZPNS01T09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT0//wAARCADIAMgDASIAAhEBAxEB/8QAGwAAAgMBAQEAAAAAAAAAAAAAAAECBAUDBgf/xAA5EAABBAEDAwMBBgQGAQUAAAABAAIDEQQSITEFQVETYXEiBhSBkaHwMrHB0RUjM0JS4WJEcpKi8f/EABkBAQEBAQEBAAAAAAAAAAAAAAABAgMEBf/EAB8RAQEBAQACAwEBAQAAAAAAAAABEQISMQMhQVETIv/aAAwDAQACEQMRAD8A8ID2TSQuD6SQT7KN0jVtwgkonynqUTuoDfsm0F7g1osk0B5KS9F9lOl/eJ/vsrQY4t2jeyfP7/oqnVya0ui/Z9uLGJpW6pyAbdxH8LQ6j03FnxJHSwsc5sZIJG4oEjflaTNJJcHf5Ta3HnwuHUSG4M75aaPTdZvYbFR5fK26+YDdoPshDeAPZNV60eSmmQK2SQBSKaSA7oA5RSmBaGIgb7qQBKkGrqxmyza6c8WosbZulYjZvwE2MvgLs1u1BYtd5zhNZ4FLoGbLo1q6BovcLLWuQaUKw1oJqkKyM3t5tCSa7PIEk0kAE0k22SA0Ek7ADugt9Mwn5mU1uk+m03Ifbx8le9wo9OO2NjdAb3Gx/wDasvovTzgYY9QBkjvqkdyT7D9+VtQRymnbMbVsvsf6qe68/wAnW1aLmsZTjoP+1reAvM/a7qZbhDEY6jMbIB4aP7rbzMqGCJxJDI/+TzufJXzzqeYc3PknN6CaYD2aOFU+PnbqqnSj7p2j0mkn2SQOkwEgptFlSrAGqYZupNC6NpZtdueYTWLqxtoAAXRpHZZtdJEmCl0aF0xceTJsRabbV26l3/w3M9MvEYNO00HC/mvCyuxxAAFp91CdskDi2Rjm+CRVqAeL5QWmOHhC4tk8IWo5de3n0IQurgEdkUkgY3Xofsv0xkxfm5BAjZtGNNkuB5F/zVfp2Lgz40Zdj5EkpJ+ppFWO1eF6OEQfdw31dEEYANd/ZTXHv5PyL2NH6jhK4aWA8u/hb8f8iukuW0Esha8tc7cnl/8AYLPysl8jKh3YwU2O6JHsqbOqviOiTEniJ7v5Pwq4q/2qyQyL7vf+a4gOINbckDyOB+K8rytTq7Zsnq+l9Rl7QWCVwbtXn5tVJsOWGH1XOh0aywaZA6yOapSPTxMivSKTCsYuDk5bg2GJzgf9x2b+arashbOP0CRxP3mZsdcBn1kq3JhdJwsRr8mMyUdJkJdbifYFZ8ouV5wJh61X5fRG6hH06R5H8JMhaD+thTx5Oh/d/VyMfTI95qJr3u0AflV/irooY0E+U8sx4nSOaLNdgr0fSM9zC50TWV2e8An4XR3XcaNobh4Ajb3Fhv8ALkrlL9oJiAIoI2ebJdf8liy38dJ1Iu4/SIzGx2RO9rzu6NjRsPF+VdfBh+uyUQtAjZp0kNDSK5O2572vLSZ+VLqEmRIQ42RdD8uESZuTK3TJPI5tVRdsU8Knm3petY0cYjx2FwaKaANIH9VWm63NK3QwNjafG5/NYeo6ky6lfAnyLrsguNvc51ChZugoiffYqkX8o1lXxa/0aAmPf9EKiHmvdCmHk5oRaFtxC64+PJkyiOIb8kngDyVYjzITE2LIxI3htDUNjQ/fla/Tn9N0SCBgBIt25uh89lLWbcWumQxNw/u7HOGnc7UT5K7T4k2kOicGiq/YUsbJxWv+iR4sckcKx6kck7dEjSB27rGvNbtZTI3wmnsLged/1V6LHM+HJjyOtzvqidfcLR2LQGwtIB3FcfCJIAz642jY22uxU8qmS1lT9Tx8N95L6LgBpDdR2HhcpIundZDWOyoy5h+kteG1fajzwPyXnetuc/rGUXuDjr7Dj2/DhUuVqc/r2b9PXN6Bh4czDIXTPbyHkaSfiv0T6n1NmEWRvBc6v9NlCh7+F5n7/mj/ANXP/wDMquSSSSSSe5Tw2/bU6yNOTreY5ha0RR33a3cfmq8XUsuI/VKZWOrUyT6g4eN1UQtZGb9rrxiZRlfGRivABbE42w77/V578KkhCoEIQUB+KLR22QiHaVoCVIQJ/KBaEUIQhAk0ghENdcab0Z2yGy0bOANWFxHKsYcZlzIWCrLxz7b/ANEX8bmPHMwm22SNiQtWLGaQ2SO2PA4Pcqh67A4xBxDo3AOsdyLC2MRx9EAcWuVrzWTUIcmdkrvVY54b2b/L5U8rMd6EpxrdPpuOLhwd4+V5fq3VvWkk+6yzRPDzGSx50yMF0fnc/muXTerZLJIsaUieJzmsAfuWgkDY8rXi3/nfbMkDw9wkvWCdV833Ue69X1Hp2Fm5UuPHOG5zarWCC4V/9hXcbiu681lYs2JL6c7C0m9J7OHkLbpz1K4ko7oQjRhFbITQL5QmkgPCK900kASkn8IPyhhcJi7SCdhDDGyCVElK96QMoS5QiaRNlMcpJhETCv8ARo9eeH1YjF88E7BUAFsYB9Dpc07QS4k96qgAP5qVb6Lp7mZGVkEhwBk9TfxZH9V6aJzmY59Mguo6QeL7X+K8l0p2jM0lxAe0j5PIXqsQtfEGjcrn17cu5leFeHNeQ9pDr3B7Ld+x0EU/UphM0Oa2MOotBF6hRPx7Ll9qYpWdRZJJHpY5gDHXequT+tfgq3Qc89P6i2SrDxoP5/yXT3G7d5e6OGx07i+PU8OLmOoamHyD2ItZuTiCHpWSOrzGdjX6WShllzCBRNb3d7rZbNFNK2SJxp40u8gjsfBWL9rMk4/RvQhNCSX03G99Faq/SlI48+8eUz+nvxWMmjf6uPILbIB+/wB/kqXyrmF1GTGHovAlxXm3xO73zR7FdpOkzTTh2Ax0mLKbikce3g+607y57Ztq5j4Rki9WeQRRh1b8/wDQ4H4qV4+A4BunIyBvq1fQz4rk/wAtvdVJppJ5DJKQXHwKH4BF+76absXpEkbRD1F0c2kk+ow6SewuhSrf4bkPt2JpymDYuh3o1e4O4/qqNqTXOb/C4j4NIn2ZBaSHAgjkHYhLlXHdRmn0tzmtyWD/AJCnc7nUN7253XWTFwssh2DO2B55hndW/YNP9z8ou/1m8bJrtk4s+K5oyInR692k8OHkeVwRQkbT5RpQJCaKKBBCaERFMJIVZTBWvITB0UNFAuA1UObN/wBvyWTBGZp2RA1qNX4Wt19wZHBHzqJfYPbgbLN9qo4cujNhcAD9YG/vt/Veqhd6Ep0n6TsvFxP9OVknOhwdXmja9fI4bSjjk0sdxjuaxPtH6h6vI55+l7WuZve1V+G4Kyitn7QBpdjzW4Pewtojagf+1i7rc9Nc+no/sx1J4yzjzve8FttJPYDj+3/a3+r40XUscwyXE1rgDJpvQezh5Hb8V4LHmdj5EczP4o3agvd4ssWbhte2T1Gu3DQ6vwPjxus2ZdjHU+9jA/wbCwGGXqGSJNJ2DdmOrt5de2wr5Sj6m7qmQcJjBB67fTZI2y4eG7bBp4oV8q5966dl6cXqkMmPJG8gRzA3zw2QVXA52Wrh9E6M3IgLcSaPIB1tikkdZrvV0Qtb/U3+vBTxPx5nwyUHMNGjsofivTfagY2eZMvDOp+I/wBKcVtRJojyLB/PtS80Aq683YVqQUUwjRppIQWcbNngJDSHxn+KOQamuH722VkN6bmNppOFN/5HVG7n8v3ys4BMikTHSbEnijMpZcOqhK3djvgriDfC6w5EsDg6J5HsdwfkcJ5OVNlafXLXadgQ0Db8EPtw4TSKPdFBQgIQRCEI4VYaHRYvUz9R4jaTfvwFLrsjXZjGNaP8tgB23vmv35Vv7OQEslmvd30gfH/6szqczZ+ozSNqi6gQbBoVf6KfqT2q/CvwdVyIIBDpY9rdhqvjxsqCfZGljLyTlESyPcZONGmmsHsb/oqyfbZBtALpBPNjv1wSvjdd200uaSDch682eL7t1aAZEJ7jYj48duP1Wo5mTF0xp6VK7qELnm4HDU5oI7OFO2PYefleQVjEzcnCfrxpXMPccg/IUxm8/wAeiGbBkMzMbKY/FnmaGSNkpj9th9X+/wCDR35KwMzAyMMu9RtsDtOscX7+D7Fapz8XrWIY+o6Y8xo0xy+2+18Ec7Hzt4VjpPTc6XLGJlNMkBa7RkM+oMI4snetq0mubRJfF5hG60+udNd03qDowP8AKO7HAbcCwPi1QpV1l2ainV7pgBO67oFXukUi6lEuQMlK/CRQOUDQgDdB55QNCLQggEKN90Fyrm9L9nHtbikvIDA5xd8bbrzhIsm+6sR5zo8F2NG2i8nU++xrYfkqw5SRJ9U0cWhCY2YSJStHZMBumkE+yBoSPCVoqQC1ei9Ym6bksJcXRVprnSPb+yyh2tPuoZL7fQOqwY/W+kunx3Nc5osf+PcH4/oSvCHU0lrgQQaIOxC0ug9U/wAPyqlcfRcCKO7Rfn2Pf810+0mM2PLGTECGSgau4Brbfg2O/sUY5/5uMiwouUdSLtHRFyipkJUqgHum07opFqCYHgoIXPV+SkHWqp1SE0KDnJG+M09pB8EKHayvez4cE/8AqRg/gs/M+z+JJETCSyTseyk7jm8kE+6s5OBPjOLXsJ9wFWIINLW6Q0JfCfKNBCdI0kqhIu0VvSK8KBIHKdFOt0ABR5UrpLZO6UU7paOBlxTwHAzZHiM7REVTT4/Pj8fKy3FJpo2iX7dsjHlxZ3RTt0vH6+49lABemx8aLrHQm6heRGCA8vsh/v7Hx+K8y8Oje5jhTmmiPdE5609u6RPhIpHdVoykgWmBugQaFIBPhJFO9kI7boUH0En2UCLXQijylQXBzV5ImP2e0FZmd0THn+phMbv0WyQCPCg4eFZbFeMy+k5WNuW629iFT0ngiivfEW2i0G+bVDI6Xj5BJdGAfIW52jyjAF0DLWhk9FnheTAQ9vhUpPUhOmVhafccrW6agWDv2XIgcBdHv/Vc3OHZGoidiki0WtQIlFoSQBTASTBQWcTMyMGQyY7ywnZw5Dh4IWjliDrEBnw4yMyMAyR93t9vJG37pY9oilkhlbJE4te02CFEs/USCxxa5pa5pogiiCnytib0OtRh7C2LqAG7OBLXv58fkfKyQ1zXlr2lpBogiiCqc3QAmAjupAhRtEpKRpQJCBlCiLJQmrj6NVdwjc+EiAfKABtyuDkDqrgEKLt/Cl8Gkr9wUECCivKkaKCg5FoXCfEjnbpkYCPFK3QTPCDAyugY7mkwudG734WJldNy8ey+Mub/AMm7r3OkHwuRivlbneD58XUaIooDvK9hn9GxsolxZof/AMmrEy+hzwAujcJGj810nUqZWaOEkyC3kEHwle6rYpRJpT5tRpVKA61Luo0phKQ2ktcC0kEGwRyFuNZF1uHW+RsfUGijWwl8EjzQO4/FYRUmSGN7XscWuabBBogrK2O2RBNjSGKeMseNyCqxJW23qeN1DHbj9WD9bSdGQyrbfkeP7Kn1PpsmDMRZkgNFkoGxB4/FDWeCVNrbTYCO2y6tYSeEta5mpxRiwTWyF1jrvshcrXr55mPbb+UyD5QhZeAgPACVHxXuhCA+NkvxQhAFG/shCApFDyEIQR0hRLBflCEFXJwMfIaRLE037LCzugStfqxPqaf9pQhanVWsibHnxzU0bm/KghC7RApBCErUI7qFGyhCF9ujbKvYWZkYv0xvLoj/ABRn+EjuEIWa6TmVZbgx5Ic7AcXOALnQu2cB7eVXDAACEIXOt8e8dpIgWAt/BCEKO7//2Q==\"/></div>\n    </div>\n</div>\n\n<style>\n    .CWHKZI-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .CWHKZI-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .CWHKZI-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    .CWHKZI-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: 81\n</div>\n<div>\nNon-outliers range: 0.3 to 0.36\n</div>\n<div class=\"CWHKZI-container\">\n    <div class=\"CWHKZI-row\">\n        <h5 class=\"CWHKZI-item\">Mean Green Relative Intensity</h5>\n        <div class=\"CWHKZI-item\">0.16</div><div class=\"CWHKZI-item\">0.2</div><div class=\"CWHKZI-item\">0.22</div><div class=\"CWHKZI-item\">0.37</div><div class=\"CWHKZI-item\">0.37</div><div class=\"CWHKZI-item\">0.39</div>\n    </div>\n    <div class=\"CWHKZI-row\">\n        <h5 class=\"CWHKZI-item\">Image</h5>\n        <div class=\"CWHKZI-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"CWHKZI-item\"><img 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\"/></div><div 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\"/></div>\n    </div>\n</div>\n\n<style>\n    .LGYTLD-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .LGYTLD-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .LGYTLD-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    .LGYTLD-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: 35\n</div>\n<div>\nNon-outliers range: 0.21 to 0.4\n</div>\n<div class=\"LGYTLD-container\">\n    <div class=\"LGYTLD-row\">\n        <h5 class=\"LGYTLD-item\">Mean Blue Relative Intensity</h5>\n        <div class=\"LGYTLD-item\">0.13</div><div class=\"LGYTLD-item\">0.13</div><div class=\"LGYTLD-item\">0.12</div><div class=\"LGYTLD-item\">0.44</div><div class=\"LGYTLD-item\">0.45</div><div class=\"LGYTLD-item\">0.45</div>\n    </div>\n    <div class=\"LGYTLD-row\">\n        <h5 class=\"LGYTLD-item\">Image</h5>\n        <div class=\"LGYTLD-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"LGYTLD-item\"><img 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RbJ+cBI3c/nEbAPWc5sn5ytsg+8PVUldJskCUPlTnm0mRLk+seKa3ySZS1pMoLRcpcgWFtxEyHKItK2QsWbiLyst2kdxXs/VMtI8ojCRauQjAfOPUcnVYR1s68t9txgQhvRk2nIJNFJMSjc1VV6mPfTXjlKpNDymhRqCIJYF9pha6ZMRhJlTCLTc03H3kTcfeEJ6DysiJsJ9ZEuYQlGRb5xbhCAHKG4QgC3DcIQMQhCBlDUIQB6hCEVMQhCZ1UEYUwhI6rTmNFdZ85pQahCYWujmYtBHpJBtQhJWmGHpCEIg/9k=\"/></div>\n    </div>\n</div>\n</div>"}}, "503ff7bd6aa8407ead5adfa6ef743a7e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b7c0b12183a24d15a772307e4ba94d99": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "59f73b3092b146669b54f352e2b1bf98": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_36f9a94594fd461ca9ee12b43d87bcf5", "style": "IPY_MODEL_e4a628ffe85240bf99cf2d28c6a43d9d", "value": "<div><h5><b>Properties With No Outliers Found</h5></b></div>"}}, "36f9a94594fd461ca9ee12b43d87bcf5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e4a628ffe85240bf99cf2d28c6a43d9d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "af9281b836224d24a9fffb294f8939ef": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_06baca98e17a48bbacc7022770f7b112", "style": "IPY_MODEL_ced00fdbdbd04e8b847b0b4d399419f8", "value": "<style type=\"text/css\">\n#T_c71c9 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c71c9 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c71c9 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c71c9 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c71c9 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_c71c9\">\n  <thead>\n    <tr>\n      <th id=\"T_c71c9_level0_col0\" class=\"col_heading level0 col0\" >More Info</th>\n      <th id=\"T_c71c9_level0_col1\" class=\"col_heading level0 col1\" >Properties</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_c71c9_row0_col0\" class=\"data row0 col0\" >No outliers found.</td>\n      <td id=\"T_c71c9_row0_col1\" class=\"data row0 col1\" >Aspect Ratio, Area</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "06baca98e17a48bbacc7022770f7b112": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ced00fdbdbd04e8b847b0b4d399419f8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "bce2361d183d49bfa537e337dc04d036": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e6eeb0366e994f5086d593cfb4a1f74e", "style": "IPY_MODEL_b690774a64d24efd9c271e5c1829f2bb", "value": "<br><a href=\"#summary_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\" style=\"font-size: 14px;\">Go to top</a>"}}, "e6eeb0366e994f5086d593cfb4a1f74e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b690774a64d24efd9c271e5c1829f2bb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b1123b05872a476d970a305d25e4bbff": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7f6909646db54b079f093dcf82a4c9a8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2d49dd0d7aea4a4bba2dc2f644eaf95b": {"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_32361d61ec764ff0a014db2bc47c35ab", "IPY_MODEL_786b3dd48ca74c38abd38866ec17f03f", "IPY_MODEL_aac27d65633d4495ad8496973e77c6de"], "layout": "IPY_MODEL_34b8b678c540402aa76d70f65fb5ccdd"}}, "32361d61ec764ff0a014db2bc47c35ab": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_89d7bf152c664c0cbec0bd9890701e42", "style": "IPY_MODEL_d4cdb82a6bd74fa4aed0efaa0e7c852e", "value": "<h4 id=\"PropertyLabelCorrelation_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\"><b>Property Label Correlation</b></h4>"}}, "89d7bf152c664c0cbec0bd9890701e42": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d4cdb82a6bd74fa4aed0efaa0e7c852e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "786b3dd48ca74c38abd38866ec17f03f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_13bb812761ac490bbc32288ca0864fe8", "style": "IPY_MODEL_a2731f7c59c444d1adf555e41d6ada9c", "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>"}}, "13bb812761ac490bbc32288ca0864fe8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a2731f7c59c444d1adf555e41d6ada9c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "aac27d65633d4495ad8496973e77c6de": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_c42caf4bbc2547f9b772ab354f90344c", "IPY_MODEL_97e8335c046d4ee0b616f95ecc41899b", "IPY_MODEL_571e8d6e231345968427fcec4c2ddb82", "IPY_MODEL_414d4c50a163407a92fa139531abab85", "IPY_MODEL_fac03b4868444aed82e3fb2ede5219cd", "IPY_MODEL_de20ad373f4a45f68da3f18c055abefc", "IPY_MODEL_68fe64932299440796ebd4795b6a8da8"], "layout": "IPY_MODEL_d556ce0bfd15451f8d729d75df6c3640"}}, "c42caf4bbc2547f9b772ab354f90344c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b905df2e64534b5cb6f6b20b2521831f", "style": "IPY_MODEL_ba75106bfbf34f20a7c25460319e648b", "value": "<h5><b>Additional Outputs</b></h5>"}}, "b905df2e64534b5cb6f6b20b2521831f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ba75106bfbf34f20a7c25460319e648b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "97e8335c046d4ee0b616f95ecc41899b": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "#69b3a2"}, "name": "Test", "text": ["<b>0.02</b>", "<b>0.02</b>", "<b>0.01</b>"], "textposition": "outside", "x": ["RMS Contrast", "Mean Red Relative Intensity", "Mean Green Relative Intensity"], "y": {"dtype": "float64", "shape": [3]}, "type": "bar", "uid": "0ca4da82-f4f4-40d0-bdfa-d6cba032726a"}], "_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": 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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, 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"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": "9GL7nEUEmD+ab29CGTuUPx0sGEXqKYc/"}]}, "571e8d6e231345968427fcec4c2ddb82": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_10daad46c27e4734b6109e58e729d68b", "style": "IPY_MODEL_166d800c214644a2abd7e466187571c9", "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>"}}, "10daad46c27e4734b6109e58e729d68b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "166d800c214644a2abd7e466187571c9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "414d4c50a163407a92fa139531abab85": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_08854092391e45a1beb8f4001882a7e8", "style": "IPY_MODEL_ff4d1b4b231843028508cff49d9f89b2", "value": "<div><u>In the graph above</u>, we should suspect we have problems in our data if:</div>"}}, "08854092391e45a1beb8f4001882a7e8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ff4d1b4b231843028508cff49d9f89b2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fac03b4868444aed82e3fb2ede5219cd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_640ee91a8fa745d090734798ec6b8f2e", "style": "IPY_MODEL_ac79d728107f4883abcb6a991e33f698", "value": "<div><b>Train dataset PPS values are high</b>:</div>"}}, "640ee91a8fa745d090734798ec6b8f2e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ac79d728107f4883abcb6a991e33f698": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "de20ad373f4a45f68da3f18c055abefc": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_da93bca75dff4d5d805d90a5b0a8cc04", "style": "IPY_MODEL_90c27216694445deaf98f143097e1270", "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>"}}, "da93bca75dff4d5d805d90a5b0a8cc04": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "90c27216694445deaf98f143097e1270": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "68fe64932299440796ebd4795b6a8da8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1f66df59fbfa4e039e8f833bc596a0b7", "style": "IPY_MODEL_82e6b3cfda3e430282791036f2c153d9", "value": "<br><a href=\"#summary_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\" style=\"font-size: 14px;\">Go to top</a>"}}, "1f66df59fbfa4e039e8f833bc596a0b7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "82e6b3cfda3e430282791036f2c153d9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d556ce0bfd15451f8d729d75df6c3640": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "34b8b678c540402aa76d70f65fb5ccdd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "84459fafdd9e430681857a81cfe623fc": {"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_586741dcbeab4212b5ae5b9af3a32a26", "IPY_MODEL_45e033ba5f14495db43b821f487d5777", "IPY_MODEL_e6e99178a80e41189d0d2eb4fcc48762"], "layout": "IPY_MODEL_d26f45ff34bb43ba8129838ad3592bdd"}}, "586741dcbeab4212b5ae5b9af3a32a26": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_791773e06eeb49f6a42b2fa4790d0dd9", "style": "IPY_MODEL_a6d87404ba694054a3829311be6fbfee", "value": "<h4 id=\"PropertyLabelCorrelation_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\"><b>Property Label Correlation</b></h4>"}}, "791773e06eeb49f6a42b2fa4790d0dd9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a6d87404ba694054a3829311be6fbfee": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "45e033ba5f14495db43b821f487d5777": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c13a75ad9fb0422399872efb28cd5560", "style": "IPY_MODEL_d8df3597c1d042b09ad147378c1682b1", "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>"}}, "c13a75ad9fb0422399872efb28cd5560": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d8df3597c1d042b09ad147378c1682b1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e6e99178a80e41189d0d2eb4fcc48762": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_e7a8548a786b45d7a6d7d1fa264f6c09", "IPY_MODEL_e76d7405775a4c0393e91ddcc2683d0d", "IPY_MODEL_fd551962456a4aecb56ce22bbe0f5f47", "IPY_MODEL_a341122ee0bd41c7b5e566639aa6a367", "IPY_MODEL_53731861d9054a6ea71102bc1321cc8d", "IPY_MODEL_4d64c46c62b044adaab68b39c13b773a", "IPY_MODEL_cd8f6b309c564e87947df87abc07ddf8"], "layout": "IPY_MODEL_a031fbc80ff54fc99505d59f9320d5fa"}}, "e7a8548a786b45d7a6d7d1fa264f6c09": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a00bcb0551d34a7c9266fca396fa25aa", "style": "IPY_MODEL_8fe6394b9c534811bd5a2bda45d900da", "value": "<h5><b>Additional Outputs</b></h5>"}}, "a00bcb0551d34a7c9266fca396fa25aa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8fe6394b9c534811bd5a2bda45d900da": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e76d7405775a4c0393e91ddcc2683d0d": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "#00008b"}, "name": "Train", "text": ["<b>0.04</b>", "<b>0.0</b>", "<b>0.0</b>"], "textposition": "outside", "x": ["RMS Contrast", "Aspect Ratio", "Area"], "y": {"dtype": "float64", "shape": [3]}, "type": "bar", "uid": "ec774455-5b3f-4a2a-a790-9a8d447e9b2e"}], "_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, 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"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, 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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": "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": "NREyqnlbpT8AAAAAAAAAAAAAAAAAAAAA"}]}, "fd551962456a4aecb56ce22bbe0f5f47": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_6b9d4b46155d42ae8e7aab39be011925", "style": "IPY_MODEL_0d9de3f48f55476dafb93e671d192e17", "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>"}}, "6b9d4b46155d42ae8e7aab39be011925": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "0d9de3f48f55476dafb93e671d192e17": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a341122ee0bd41c7b5e566639aa6a367": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4d34751bf5774c9bbe351d5676f70d5e", "style": "IPY_MODEL_f6cc6daa5f4148d9a09cc92c47f431c6", "value": "<div><u>In the graph above</u>, we should suspect we have problems in our data if:</div>"}}, "4d34751bf5774c9bbe351d5676f70d5e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f6cc6daa5f4148d9a09cc92c47f431c6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "53731861d9054a6ea71102bc1321cc8d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7cf5125af78a4e0580d87d1f4a0feaf2", "style": "IPY_MODEL_c7b7719398904bc580a1b28ec083da9f", "value": "<div><b>Train dataset PPS values are high</b>:</div>"}}, "7cf5125af78a4e0580d87d1f4a0feaf2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c7b7719398904bc580a1b28ec083da9f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4d64c46c62b044adaab68b39c13b773a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_82301f8c82274724b8a10db5bd792055", "style": "IPY_MODEL_4a52e03a3a064038bb8ca80427c6bd53", "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>"}}, "82301f8c82274724b8a10db5bd792055": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4a52e03a3a064038bb8ca80427c6bd53": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cd8f6b309c564e87947df87abc07ddf8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c9ace964cac4418eb95bf2bc831dc3f6", "style": "IPY_MODEL_755a11ffc8e64118a208a7f7ec1e6c65", "value": "<br><a href=\"#summary_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\" style=\"font-size: 14px;\">Go to top</a>"}}, "c9ace964cac4418eb95bf2bc831dc3f6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "755a11ffc8e64118a208a7f7ec1e6c65": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a031fbc80ff54fc99505d59f9320d5fa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d26f45ff34bb43ba8129838ad3592bdd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "beec6087584044c79b65a813d993123a": {"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_a3c0df7a32134f45bb9e153190cf133e", "IPY_MODEL_6004b63b7c244f18a4bee37fca6247a4", "IPY_MODEL_cb516c2d71264359abdfebdcf77414be"], "layout": "IPY_MODEL_e0e2e905f1da4460b371f072683df6eb"}}, "a3c0df7a32134f45bb9e153190cf133e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_430fae5566354044bb3b1350c095b5ba", "style": "IPY_MODEL_cafde0dca9e0454fb280fcfc3c72da97", "value": "<h4 id=\"ConfusionMatrix_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\"><b>Confusion Matrix</b></h4>"}}, "430fae5566354044bb3b1350c095b5ba": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cafde0dca9e0454fb280fcfc3c72da97": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6004b63b7c244f18a4bee37fca6247a4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_78cbf94c6db14bbb829a2b52d522bfba", "style": "IPY_MODEL_6e92ece9f86c4f40a3198c1f3fb0f860", "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>"}}, "78cbf94c6db14bbb829a2b52d522bfba": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6e92ece9f86c4f40a3198c1f3fb0f860": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cb516c2d71264359abdfebdcf77414be": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_e03f1efbada246ddb2670d37a72c5f81", "IPY_MODEL_d08187a926004c6099a3c124b927e297", "IPY_MODEL_cfe8dafa542f4bc2a792fea002f8e27f", "IPY_MODEL_686ccbafd3584c49a1cbf9388feb5ff1", "IPY_MODEL_cc84e3ad8beb4ddf8bcda69c580c1504"], "layout": "IPY_MODEL_a69cba27a558413887702a08d2b86dcb"}}, "e03f1efbada246ddb2670d37a72c5f81": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9545fe8538fd4c91ac644bc3e2f8b235", "style": "IPY_MODEL_78d321d409134fc59fe22175568cf445", "value": "<h5><b>Additional Outputs</b></h5>"}}, "9545fe8538fd4c91ac644bc3e2f8b235": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "78d321d409134fc59fe22175568cf445": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d08187a926004c6099a3c124b927e297": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_63888dab4d4a4ab7a48a5aac33fb74d0", "style": "IPY_MODEL_a042fe0161ee4c89a355af948460bb9c", "value": "<div>Showing 10 of 3 classes:</div>"}}, "63888dab4d4a4ab7a48a5aac33fb74d0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a042fe0161ee4c89a355af948460bb9c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cfe8dafa542f4bc2a792fea002f8e27f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4273480ba5544d4c9871e0c5a6199448", "style": "IPY_MODEL_6d72b20ada29499bb4efa79294fd1299", "value": "<div>The overall accuracy of your model is: 56.68%.<br>Best accuracy achieved on samples with <b>culex_quinquefasciatus</b> label (89.87%).<br>Worst accuracy achieved on samples with <b>aedes_albopictus</b> label (35.06%).</div>"}}, "4273480ba5544d4c9871e0c5a6199448": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6d72b20ada29499bb4efa79294fd1299": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "686ccbafd3584c49a1cbf9388feb5ff1": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"customdata": [[[15.55, 45.12, 53.62], [5.88, 17.07, 20.29], [13.03, 37.8, 44.93]], [[10.5, 32.47, 59.52], [11.34, 35.06, 64.29], [10.5, 32.47, 59.52]], [[2.94, 8.86, 5.51], [0.42, 1.27, 0.79], [29.83, 89.87, 55.91]]], "hovertemplate": "% out of all data: 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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}}, "cc84e3ad8beb4ddf8bcda69c580c1504": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": 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"@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": ["rendered_html", "jp-RenderedHTMLCommon", "jp-RenderedHTML", "jp-OutputArea-output"], "children": ["IPY_MODEL_deafc4dbc6864e25a83ff9731e7c6a47", "IPY_MODEL_17259d61ef9d4f099e11b5f22aecc819", "IPY_MODEL_cef5e8de4f264704b98e6f03189cf0b0"], "layout": "IPY_MODEL_26ef77d1f89a462e973065b256004e32"}}, "deafc4dbc6864e25a83ff9731e7c6a47": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3c7dd1f650194c5aada2377e13fd1524", "style": "IPY_MODEL_4efe4712b3724b43a402438848a15127", "value": "<h4 id=\"ConfusionMatrix_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\"><b>Confusion Matrix</b></h4>"}}, "3c7dd1f650194c5aada2377e13fd1524": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4efe4712b3724b43a402438848a15127": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "17259d61ef9d4f099e11b5f22aecc819": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_03cabb2ad2da410eb9d85ee54bbc203d", "style": "IPY_MODEL_08db6636b4164929a4d4d6374baa72b8", "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>"}}, "03cabb2ad2da410eb9d85ee54bbc203d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "08db6636b4164929a4d4d6374baa72b8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": 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"model_module_version": "1.5.0", "state": {}}, "62f6b862c9214f2f9528333a56719a6f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2e3d39704378463bb5250e1773e952f5", "style": "IPY_MODEL_86c34871a6964bd1a132c293fc4b1acf", "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>"}}, "2e3d39704378463bb5250e1773e952f5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "86c34871a6964bd1a132c293fc4b1acf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": 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2}}}, "title": {"text": "Compare average image brightness"}, "xaxis": {"anchor": "y", "automargin": true, "constrain": "domain", "domain": [0.0, 0.45], "fixedrange": true, "showticklabels": false, "visible": true}, "yaxis": {"anchor": "x", "automargin": true, "autorange": "reversed", "constrain": "domain", "domain": [0.0, 1.0], "fixedrange": true, "showticklabels": false, "visible": true}, "xaxis2": {"anchor": "y2", "automargin": true, "constrain": "domain", "domain": [0.55, 1.0], "fixedrange": true, "showticklabels": false, "visible": true}, "yaxis2": {"anchor": "x2", "automargin": true, "autorange": "reversed", "constrain": "domain", "domain": [0.0, 1.0], "fixedrange": true, "showticklabels": false, "visible": true}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, "_py2js_moveTraces": {}, "_py2js_relayout": {}, "_py2js_removeLayoutProps": {}, "_py2js_removeTraceProps": {}, "_py2js_restyle": {}, "_py2js_update": {}, "_view_count": 0}}, "86585e7bd28b46859b8d5853fd98b878": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2f9b47dc128d47ac8e1befbe416955a2", "style": "IPY_MODEL_23f9eebfe301417e92c08ce7ffbebff4", "value": "<br><a href=\"#summary_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\" style=\"font-size: 14px;\">Go to top</a>"}}, "2f9b47dc128d47ac8e1befbe416955a2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "23f9eebfe301417e92c08ce7ffbebff4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0683c5e8bc6a41f0bd4ad2070aee87d4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9d09127ef1c3434b977b95e9dccd8621": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fe8d0c210a2a48d48752d730e51fbbfb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7c5af267cec449d18975e38707ed3d2a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "71780aa73ca54db2b58ee6e84cc529c9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e8d66bbd42ae4a3ba69d2de13ecfe35d": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_dcd75eb0ffa14188aa36495d489b8121", "IPY_MODEL_42e9eafa8c514271af529113cd0ca18f"], "layout": "IPY_MODEL_f2921e4a46964eb8907ae311bbdbfbc8"}}, "dcd75eb0ffa14188aa36495d489b8121": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_aa334e5f81d3470695dabc8f575517fd", "style": "IPY_MODEL_b435eddaba154682ab3c37e06c118ea5"}}, "aa334e5f81d3470695dabc8f575517fd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b435eddaba154682ab3c37e06c118ea5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "42e9eafa8c514271af529113cd0ca18f": {"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_df5f78dcd6c3477fadd19a79d364ebde"], "layout": "IPY_MODEL_72bb08fdcf654656a7195a98f18f0f4c", "selected_index": null}}, "df5f78dcd6c3477fadd19a79d364ebde": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f4909348165c4feba6fb8ab89aae0f23", "style": "IPY_MODEL_f7914a9f74c9482b9878d4caf3de2d22", "value": "<style type=\"text/css\">\n#T_5ea74 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5ea74 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5ea74 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5ea74 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5ea74 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_5ea74\">\n  <thead>\n    <tr>\n      <th id=\"T_5ea74_level0_col0\" class=\"col_heading level0 col0\" >Check</th>\n      <th id=\"T_5ea74_level0_col1\" class=\"col_heading level0 col1\" >Reason</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_5ea74_row0_col0\" class=\"data row0 col0\" >Mean Average Precision Report</td>\n      <td id=\"T_5ea74_row0_col1\" class=\"data row0 col1\" >Check is irrelevant for task of type TaskType.CLASSIFICATION</td>\n    </tr>\n    <tr>\n      <td id=\"T_5ea74_row1_col0\" class=\"data row1 col0\" >Mean Average Precision Report</td>\n      <td id=\"T_5ea74_row1_col1\" class=\"data row1 col1\" >Check is irrelevant for task of type TaskType.CLASSIFICATION</td>\n    </tr>\n    <tr>\n      <td id=\"T_5ea74_row2_col0\" class=\"data row2 col0\" >Label Property Outliers</td>\n      <td id=\"T_5ea74_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_5ea74_row3_col0\" class=\"data row3 col0\" >Label Property Outliers</td>\n      <td id=\"T_5ea74_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_5ea74_row4_col0\" class=\"data row4 col0\" >Mean Average Recall Report</td>\n      <td id=\"T_5ea74_row4_col1\" class=\"data row4 col1\" >Check is irrelevant for task of type TaskType.CLASSIFICATION</td>\n    </tr>\n    <tr>\n      <td id=\"T_5ea74_row5_col0\" class=\"data row5 col0\" >Mean Average Recall Report</td>\n      <td id=\"T_5ea74_row5_col1\" class=\"data row5 col1\" >Check is irrelevant for task of type TaskType.CLASSIFICATION</td>\n    </tr>\n    <tr>\n      <td id=\"T_5ea74_row6_col0\" class=\"data row6 col0\" >Weak Segments PerformanceTrain</td>\n      <td id=\"T_5ea74_row6_col1\" class=\"data row6 col1\" >DeepchecksProcessError: WeakSegmentsPerformance was unable to train an error model to find weak segments. Try increasing n_samples or supply additional properties.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "f4909348165c4feba6fb8ab89aae0f23": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f7914a9f74c9482b9878d4caf3de2d22": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "72bb08fdcf654656a7195a98f18f0f4c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f2921e4a46964eb8907ae311bbdbfbc8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2a12e0f309c54a5b86b4380ff2349879": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "deed9a7b1877441bb3b0a4eb01828c36": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}}}
+{"version_major": 2, "version_minor": 0, "state": {"f06e9e4da3774cab8ad6b3332b4832bf": {"model_name": "AccordionModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_titles": {"0": "Full Suite"}, "children": ["IPY_MODEL_9fd03eb8674f43c68328d9609e17ae95"], "layout": "IPY_MODEL_eeb8739d0d4e4794869c876fc1855990"}}, "9fd03eb8674f43c68328d9609e17ae95": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_8f80692e00db4278ac158b7369e20282", "IPY_MODEL_9e75f21a6b9d430393523d71dd4cad41", "IPY_MODEL_be0ee10d9f16448994548c7545ad41db", "IPY_MODEL_f55e1fa5c1cb4bb38db4a18af7b98eae", "IPY_MODEL_296d114268484b59bac023e1242d82f0"], "layout": "IPY_MODEL_2bfa18b6e20841e4b4a3bc7c0bef12ad"}}, "8f80692e00db4278ac158b7369e20282": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b924bf8985494caf94117b4434037ae4", "style": "IPY_MODEL_c4690ce80fad4b60bd7679b348a6c9d8", "value": "\n<h1 id=\"summary_TAXP0WFOY4DD1VN8LFJDTVV2Y\">Full Suite</h1>\n<p>\n    The suite is composed of various checks such as: Class Performance, Image Property Outliers, Property Label Correlation Change, 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"}}, "b924bf8985494caf94117b4434037ae4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c4690ce80fad4b60bd7679b348a6c9d8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9e75f21a6b9d430393523d71dd4cad41": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_0bdcc858a10b4b009dd3198e0299ee4b", "IPY_MODEL_0f2e831b90b746478e8ecd95e72bf083"], "layout": "IPY_MODEL_baf43a300ae44ee78c962e9d3dc86bba"}}, "0bdcc858a10b4b009dd3198e0299ee4b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d25afa4330a548768e09e094d8b13e7d", "style": "IPY_MODEL_208f7e52daa94a01a2146732e0656c8c", "value": "<span id=\"summary_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-4XKSS\"></span>"}}, "d25afa4330a548768e09e094d8b13e7d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "208f7e52daa94a01a2146732e0656c8c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0f2e831b90b746478e8ecd95e72bf083": {"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_1b72b11ec5fe49ada5fdef8c9bd7b7a8"], "layout": "IPY_MODEL_292894e8507d48198d06e8b476bc3718", "selected_index": null}}, "1b72b11ec5fe49ada5fdef8c9bd7b7a8": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_f254c3cd1d6c4e98b7618cc39f17db7b", "IPY_MODEL_be1d2e217d4c4654a3fae9252ddec770", "IPY_MODEL_a1dd0015b8c74547b7a27c59189dd4c1", "IPY_MODEL_1fc79668950e474b87f263e4acab9a46", "IPY_MODEL_3548386851534658975861c2951e29f2", "IPY_MODEL_1fc79668950e474b87f263e4acab9a46", "IPY_MODEL_e4633b39923c405aa0e46bb793b40b0f", "IPY_MODEL_1fc79668950e474b87f263e4acab9a46", "IPY_MODEL_630a1f6a942240e794711bfe3f644335"], "layout": "IPY_MODEL_edc443b6408a4d89ace6f2c2ee877e36"}}, "f254c3cd1d6c4e98b7618cc39f17db7b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ee18b58964834e9ca168262e66761bb3", "style": "IPY_MODEL_1b76e0b2cd504b5a9b5b654dda60bfaf", "value": "<style type=\"text/css\">\n#T_e0de1 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e0de1 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e0de1 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e0de1 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_e0de1 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_e0de1\">\n  <thead>\n    <tr>\n      <th id=\"T_e0de1_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_e0de1_level0_col1\" class=\"col_heading level0 col1\" >Check</th>\n      <th id=\"T_e0de1_level0_col2\" class=\"col_heading level0 col2\" >Condition</th>\n      <th id=\"T_e0de1_level0_col3\" class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_e0de1_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_e0de1_row0_col1\" class=\"data row0 col1\" ><a href=\"#ClassPerformance_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-4XKSS\">Class Performance</a></td>\n      <td id=\"T_e0de1_row0_col2\" class=\"data row0 col2\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_e0de1_row0_col3\" class=\"data row0 col3\" >6 scores failed. Found max degradation of 71.68% for metric Recall and class aedes_aegypti.</td>\n    </tr>\n    <tr>\n      <td id=\"T_e0de1_row1_col0\" class=\"data row1 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_e0de1_row1_col1\" class=\"data row1 col1\" ><a href=\"#PredictionDrift_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-4XKSS\">Prediction Drift</a></td>\n      <td id=\"T_e0de1_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_e0de1_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': '0.26'}</td>\n    </tr>\n    <tr>\n      <td id=\"T_e0de1_row2_col0\" class=\"data row2 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_e0de1_row2_col1\" class=\"data row2 col1\" ><a href=\"#SimpleModelComparison_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-4XKSS\">Simple Model Comparison</a></td>\n      <td id=\"T_e0de1_row2_col2\" class=\"data row2 col2\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_e0de1_row2_col3\" class=\"data row2 col3\" >Found metrics with gain below threshold: {'F1': {'aedes_aegypti': '-25.16%'}}</td>\n    </tr>\n    <tr>\n      <td id=\"T_e0de1_row3_col0\" class=\"data row3 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_e0de1_row3_col1\" class=\"data row3 col1\" ><a href=\"#WeakSegmentsPerformance_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-4XKSS\">Weak Segments Performance</a></td>\n      <td id=\"T_e0de1_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_e0de1_row3_col3\" class=\"data row3 col3\" >Found a segment with cross entropy of -5.664 in comparison to an average score of -2.005 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "ee18b58964834e9ca168262e66761bb3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1b76e0b2cd504b5a9b5b654dda60bfaf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "be1d2e217d4c4654a3fae9252ddec770": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8b802bb6217b46e59f83a48a7726f483", "style": "IPY_MODEL_a3eff0622b1a46bb90e9541569fec7d8", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "8b802bb6217b46e59f83a48a7726f483": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a3eff0622b1a46bb90e9541569fec7d8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a1dd0015b8c74547b7a27c59189dd4c1": {"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_54e0fc0d1db44e538c2b9d930b944731", "IPY_MODEL_9221293f284c4050aa992c85fa4ff70d", "IPY_MODEL_943c01ed053c4ef9a694d15739940c72", "IPY_MODEL_7b5480970d1148388679d31d5662ef0e"], "layout": "IPY_MODEL_26783157fd7a4db7a45debb0845fb8ca"}}, "54e0fc0d1db44e538c2b9d930b944731": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a37b91cfc7264af5888fbfcc893cd7c1", "style": "IPY_MODEL_ad2f3e9bcbbf4ad6b8c43ae81c05ef21", "value": "<h4 id=\"ClassPerformance_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-4XKSS\"><b>Class Performance</b></h4>"}}, "a37b91cfc7264af5888fbfcc893cd7c1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ad2f3e9bcbbf4ad6b8c43ae81c05ef21": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9221293f284c4050aa992c85fa4ff70d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_88450068077d4445b06222de9740cf75", "style": "IPY_MODEL_79536fa5be674c048ded55c486550e61", "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>"}}, "88450068077d4445b06222de9740cf75": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "79536fa5be674c048ded55c486550e61": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "943c01ed053c4ef9a694d15739940c72": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d307e35cd7254b93bcb5387c6235119f", "style": "IPY_MODEL_bff6e6a9b4474bc59562e5788e69623b", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_5ad47 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5ad47 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5ad47 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5ad47 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5ad47 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_5ad47\">\n  <thead>\n    <tr>\n      <th id=\"T_5ad47_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_5ad47_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_5ad47_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_5ad47_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_5ad47_row0_col1\" class=\"data row0 col1\" >Train-Test scores relative degradation is less than 0.1</td>\n      <td id=\"T_5ad47_row0_col2\" class=\"data row0 col2\" >6 scores failed. Found max degradation of 71.68% for metric Recall and class aedes_aegypti.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "d307e35cd7254b93bcb5387c6235119f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bff6e6a9b4474bc59562e5788e69623b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7b5480970d1148388679d31d5662ef0e": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_66797a39ad1245fab2c027f736f746cd", "IPY_MODEL_16eecdc7000b4084810d44f37e9f5dcf", "IPY_MODEL_90b9d6b35e9c46389807757d958b95ab"], "layout": "IPY_MODEL_7c52f19407aa4fe9b4987e7931299d57"}}, "66797a39ad1245fab2c027f736f746cd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ab09695560474229b5be4d040d885373", "style": "IPY_MODEL_d016dc49e3c142f2af18bacf6ca16c46", "value": "<h5><b>Additional Outputs</b></h5>"}}, "ab09695560474229b5be4d040d885373": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d016dc49e3c142f2af18bacf6ca16c46": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "16eecdc7000b4084810d44f37e9f5dcf": {"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": "#00008b", "pattern": {"shape": ""}}, "name": "Train", "offsetgroup": "Train", "orientation": "v", "showlegend": true, "x": ["aedes_aegypti", "aedes_albopictus", "culex_quinquefasciatus"], "xaxis": "x", "y": {"dtype": "float64", "shape": [3]}, "yaxis": "y", "type": "histogram", "uid": "31304d81-1e1b-4b4b-af64-48ab4e340527"}, {"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": "#00008b", "pattern": {"shape": ""}}, "name": "Train", "offsetgroup": "Train", "orientation": "v", "showlegend": false, "x": ["culex_quinquefasciatus", "aedes_albopictus", "aedes_aegypti"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [3]}, "yaxis": "y2", "type": "histogram", "uid": "15e7c3b7-576b-4e23-8b43-73c367024dca"}, {"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": "#69b3a2", "pattern": {"shape": ""}}, "name": "Test", "offsetgroup": "Test", "orientation": "v", "showlegend": true, "x": ["aedes_aegypti", "aedes_albopictus", "culex_quinquefasciatus"], "xaxis": "x", "y": {"dtype": "float64", "shape": [3]}, "yaxis": "y", "type": "histogram", "uid": "282da63d-546b-4345-86e3-e8a896572f20"}, {"alignmentgroup": "True", "bingroup": "x", "histfunc": "sum", "hovertemplate": "Dataset=Test<br>Metric=Recall<br>Class Name=%{x}<br>sum of Value=%{y}<extra></extra>", "legendgroup": "Test", "marker": {"color": "#69b3a2", "pattern": {"shape": ""}}, "name": "Test", "offsetgroup": "Test", "orientation": "v", "showlegend": false, "x": ["culex_quinquefasciatus", "aedes_albopictus", "aedes_aegypti"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [3]}, "yaxis": "y2", "type": "histogram", "uid": "a62a595c-d6bb-47cc-a8be-913e265cc9a1"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"annotations": [{"showarrow": false, "text": "Precision", "x": 0.2375, "xanchor": "center", "xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}, {"showarrow": false, "text": "Recall", "x": 0.7625, "xanchor": "center", "xref": "paper", "y": 1.0, "yanchor": "bottom", "yref": "paper"}], "barmode": "group", "legend": {"title": {"text": "Dataset"}, "tracegroupgap": 0}, "margin": {"t": 60}, "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": 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"data": "HMdxHMdx5D8AAAAAAADkP/kZJX5Gid8/"}, {"encoding": "base64", "path": ["_data", 3, "y", "buffer"], "data": "FzdhfleL6z9WfkKclZ/gP/QxOB+D89E/"}]}, "90b9d6b35e9c46389807757d958b95ab": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7c044d5f10a04245840a1bf09dd54c9c", "style": "IPY_MODEL_a90f6cf5175d49389ebee91ffe7b24d8", "value": "<br><a href=\"#summary_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-4XKSS\" style=\"font-size: 14px;\">Go to top</a>"}}, "7c044d5f10a04245840a1bf09dd54c9c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a90f6cf5175d49389ebee91ffe7b24d8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7c52f19407aa4fe9b4987e7931299d57": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "26783157fd7a4db7a45debb0845fb8ca": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1fc79668950e474b87f263e4acab9a46": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_98e3599dee2a4dfda3eb490465964080", "style": "IPY_MODEL_5e1332343b3a44cdbb2bc34bc36ced0e", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "98e3599dee2a4dfda3eb490465964080": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5e1332343b3a44cdbb2bc34bc36ced0e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3548386851534658975861c2951e29f2": {"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_02840dc859d146ed8c2bb471f048f984", "IPY_MODEL_fc9a9f9dff874cfaa88fb0242dffc682", "IPY_MODEL_2d17eb39d82648c8a245aed52c48d147", "IPY_MODEL_94a5279ecbed4aa99fd25b8bf4ad1650"], "layout": "IPY_MODEL_837f72299de641da853d3b8358765f58"}}, "02840dc859d146ed8c2bb471f048f984": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e9ff8f7e9f5d4e1a86ab859656835454", "style": "IPY_MODEL_bd9ed8552e6c4d46b1d845b1210997f2", "value": "<h4 id=\"PredictionDrift_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-4XKSS\"><b>Prediction Drift</b></h4>"}}, "e9ff8f7e9f5d4e1a86ab859656835454": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bd9ed8552e6c4d46b1d845b1210997f2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fc9a9f9dff874cfaa88fb0242dffc682": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5f7f062c68c3429eaaa21ad8c8b50c3b", "style": "IPY_MODEL_b7a6e9828653442bab2de7f8ca5770a8", "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>"}}, "5f7f062c68c3429eaaa21ad8c8b50c3b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b7a6e9828653442bab2de7f8ca5770a8": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2d17eb39d82648c8a245aed52c48d147": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c3c02d63c5b445fe8c70361c435a614a", "style": "IPY_MODEL_efc958fe2a2c441786804026d864c890", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_04863 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_04863 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_04863 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_04863 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_04863 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_04863\">\n  <thead>\n    <tr>\n      <th id=\"T_04863_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_04863_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_04863_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_04863_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_04863_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_04863_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': '0.26'}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "c3c02d63c5b445fe8c70361c435a614a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "efc958fe2a2c441786804026d864c890": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "94a5279ecbed4aa99fd25b8bf4ad1650": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_68d8e43067dc463e92783277a6db0e2e", "IPY_MODEL_fa63ba9e1dcf4617a6527178ebe17a94", "IPY_MODEL_7f67351c77bb4537800087dce019c856", "IPY_MODEL_c83e82706b1841eaa0dbe9edf1a6c29a", "IPY_MODEL_1e74eddfe68b403aaf3bab7d329ee865"], "layout": "IPY_MODEL_e2f4186995b149c8b44f1a0c9361dca4"}}, "68d8e43067dc463e92783277a6db0e2e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9306a6e6c5a54e5ea7b4d20e31b33812", "style": "IPY_MODEL_821acec3f3f94d63bc8580ddf3ec239c", "value": "<h5><b>Additional Outputs</b></h5>"}}, "9306a6e6c5a54e5ea7b4d20e31b33812": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "821acec3f3f94d63bc8580ddf3ec239c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "fa63ba9e1dcf4617a6527178ebe17a94": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_1187304911e640278196d3ea842f6381", "style": "IPY_MODEL_df919a4f18c5444c83b5d2f346812fa6", "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>"}}, "1187304911e640278196d3ea842f6381": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "df919a4f18c5444c83b5d2f346812fa6": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7f67351c77bb4537800087dce019c856": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c22b44c67810469a8058c1bdedf31f57", "style": "IPY_MODEL_6e994f20dc864a04b0df77aec231167c", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "c22b44c67810469a8058c1bdedf31f57": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6e994f20dc864a04b0df77aec231167c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c83e82706b1841eaa0dbe9edf1a6c29a": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#01B8AA"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.1], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "89e93c1b-9169-4bd4-b402-11d75b09db50"}, {"base": 0.1, "marker": {"color": "#F2C80F"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.1], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "3003d7fb-51b4-475f-b692-fe6bb904815c"}, {"base": 0.2, "marker": {"color": "#FE9666"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": 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{"encoding": "base64", "path": ["_data", 4, "y", "buffer"], "data": "E4FcE4Fcwz8RyDURyDXRP7P7jbL7jeI/"}]}, "1e74eddfe68b403aaf3bab7d329ee865": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_85d1cc2713b34504bfa7fab91ee95b87", "style": "IPY_MODEL_d6d4193c96dc4eabb929172d90198d9c", "value": "<br><a href=\"#summary_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-4XKSS\" style=\"font-size: 14px;\">Go to top</a>"}}, "85d1cc2713b34504bfa7fab91ee95b87": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d6d4193c96dc4eabb929172d90198d9c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e2f4186995b149c8b44f1a0c9361dca4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "837f72299de641da853d3b8358765f58": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e4633b39923c405aa0e46bb793b40b0f": {"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_5d9076dd19874b28aa5d31d5d8d00171", "IPY_MODEL_e2c3d57ca84442a5a08dd5d7a3cbb77e", "IPY_MODEL_0aef30aeb18e48459902826046e982db", "IPY_MODEL_02483617514148bbb15aa262ea482aab"], "layout": "IPY_MODEL_61f903fd081d466a9983499812ec0f55"}}, "5d9076dd19874b28aa5d31d5d8d00171": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bdd313599b434e9fb2808fc7d9fa046a", "style": "IPY_MODEL_f93573ad826a45fcb28b8e9f14441382", "value": "<h4 id=\"SimpleModelComparison_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-4XKSS\"><b>Simple Model Comparison</b></h4>"}}, "bdd313599b434e9fb2808fc7d9fa046a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f93573ad826a45fcb28b8e9f14441382": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e2c3d57ca84442a5a08dd5d7a3cbb77e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8f5133b92e2343c78df3e50e01736a71", "style": "IPY_MODEL_8c58427121a64f38a39dea9e11272c2a", "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>"}}, "8f5133b92e2343c78df3e50e01736a71": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8c58427121a64f38a39dea9e11272c2a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0aef30aeb18e48459902826046e982db": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_48efcee70c1144fba453332fce109626", "style": "IPY_MODEL_fdecbd3262d04b78a19e5fc3738c4144", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_13c67 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_13c67 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_13c67 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_13c67 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_13c67 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_13c67\">\n  <thead>\n    <tr>\n      <th id=\"T_13c67_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_13c67_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_13c67_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_13c67_row0_col0\" class=\"data row0 col0\" ><div style=\"color: red;text-align: center\">\u2716</div></td>\n      <td id=\"T_13c67_row0_col1\" class=\"data row0 col1\" >Model performance gain over simple model is greater than 10%</td>\n      <td id=\"T_13c67_row0_col2\" class=\"data row0 col2\" >Found metrics with gain below threshold: {'F1': {'aedes_aegypti': '-25.16%'}}</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "48efcee70c1144fba453332fce109626": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fdecbd3262d04b78a19e5fc3738c4144": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "02483617514148bbb15aa262ea482aab": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_419eac80b9724ac486c139ff97071eca", "IPY_MODEL_47c8eb7c59734ff8addbab99a8b99353", "IPY_MODEL_9ce1be0b1e2741108198d1b185c04f37"], "layout": "IPY_MODEL_dc903b9a1b1547bfb32ed600fd527d88"}}, "419eac80b9724ac486c139ff97071eca": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0dec69a4fcd84a428482d7e5ad4a7159", "style": "IPY_MODEL_6561b0b40fc8493badacc677511ba2ba", "value": "<h5><b>Additional Outputs</b></h5>"}}, "0dec69a4fcd84a428482d7e5ad4a7159": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6561b0b40fc8493badacc677511ba2ba": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "47c8eb7c59734ff8addbab99a8b99353": {"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": "#2191FB", "pattern": {"shape": ""}}, "name": "Simple Model", "offsetgroup": "Simple Model", "orientation": "v", "showlegend": true, "x": ["aedes_aegypti", "aedes_albopictus", "culex_quinquefasciatus"], "xaxis": "x", "y": {"dtype": "float64", "shape": [3]}, "yaxis": "y", "type": "histogram", "uid": "658b9da9-ab1e-4d2b-9569-a938ee104c40"}, {"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": "#b287a3", "pattern": {"shape": ""}}, "name": "Given Model", "offsetgroup": "Given Model", "orientation": "v", "showlegend": true, "x": ["culex_quinquefasciatus", "aedes_albopictus", "aedes_aegypti"], "xaxis": "x", "y": {"dtype": "float64", "shape": [3]}, "yaxis": "y", "type": "histogram", "uid": "5b7242e9-79a5-4c06-9905-7e00823d60ad"}], "_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, 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[{"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": "ZmZmZmZm4D8AAAAAAAAAAAAAAAAAAAAA"}, {"encoding": "base64", "path": ["_data", 1, "y", "buffer"], "data": "oThQHCgO5D9JOKV58SfiPyRoOKn78tg/"}]}, "9ce1be0b1e2741108198d1b185c04f37": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_dcbd689259744162a795c50cf3575d2e", "style": "IPY_MODEL_7dce2d683f844b84a508f30ec97516ad", "value": "<br><a href=\"#summary_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-4XKSS\" style=\"font-size: 14px;\">Go to top</a>"}}, "dcbd689259744162a795c50cf3575d2e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7dce2d683f844b84a508f30ec97516ad": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "dc903b9a1b1547bfb32ed600fd527d88": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "61f903fd081d466a9983499812ec0f55": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "630a1f6a942240e794711bfe3f644335": {"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_dd77f81bbc844622b09a0aa6f4aa6bb5", "IPY_MODEL_93b6fe70fbd64ad59df540528604527c", "IPY_MODEL_76057a33f7184c48a872215691bb88d3", "IPY_MODEL_5ae4aa23e00043bfb1262002d44a1d4c"], "layout": "IPY_MODEL_03ad341462c248e5bca5ab35ed192f1a"}}, "dd77f81bbc844622b09a0aa6f4aa6bb5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_386bcfdca5f04dde998dfc21300b04f5", "style": "IPY_MODEL_1b238a8d9c214260aff5af09ff3c1a44", "value": "<h4 id=\"WeakSegmentsPerformance_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-4XKSS\"><b>Weak Segments Performance</b></h4>"}}, "386bcfdca5f04dde998dfc21300b04f5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1b238a8d9c214260aff5af09ff3c1a44": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "93b6fe70fbd64ad59df540528604527c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d8cd1f67614b4ddeb3a30af5c20beb24", "style": "IPY_MODEL_6b28ab7b9a3a4b9d8723ad4e0205cabc", "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>"}}, "d8cd1f67614b4ddeb3a30af5c20beb24": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6b28ab7b9a3a4b9d8723ad4e0205cabc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "76057a33f7184c48a872215691bb88d3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c1649e0ff43646379556c8b7c9aa61d7", "style": "IPY_MODEL_2e307c183c8c48dda91ecfddb7e1d12d", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_c3a18 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c3a18 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c3a18 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c3a18 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_c3a18 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_c3a18\">\n  <thead>\n    <tr>\n      <th id=\"T_c3a18_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_c3a18_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_c3a18_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_c3a18_row0_col0\" class=\"data row0 col0\" ><div style=\"color: orange;text-align: center;font-weight:bold\">!</div></td>\n      <td id=\"T_c3a18_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_c3a18_row0_col2\" class=\"data row0 col2\" >Found a segment with cross entropy of -5.664 in comparison to an average score of -2.005 in sampled data.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "c1649e0ff43646379556c8b7c9aa61d7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2e307c183c8c48dda91ecfddb7e1d12d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5ae4aa23e00043bfb1262002d44a1d4c": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_2027ddb535434062a3abb1b4f12ae397", "IPY_MODEL_129c86cbb74649eaad3c2cdeb63c2f33", "IPY_MODEL_dbd9b960763b4f4d8a78b7507f15f11e", "IPY_MODEL_4242aa00e819434583e646e6eba3f31e"], "layout": "IPY_MODEL_1dd84b7e23bc4d44ac59d4fac0e23d68"}}, "2027ddb535434062a3abb1b4f12ae397": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f8fb466fdd364cdfad6dc522713f649b", "style": "IPY_MODEL_7d338359496f4828ad14b25c3ff06130", "value": "<h5><b>Additional Outputs</b></h5>"}}, "f8fb466fdd364cdfad6dc522713f649b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7d338359496f4828ad14b25c3ff06130": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "129c86cbb74649eaad3c2cdeb63c2f33": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_876ba002ed014edfac49c5bb505df8a2", "style": "IPY_MODEL_14859c0f9b2845059b49b6c830da51bb", "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>"}}, "876ba002ed014edfac49c5bb505df8a2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "14859c0f9b2845059b49b6c830da51bb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "dbd9b960763b4f4d8a78b7507f15f11e": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_10f1e86f2f994221815ee12124793688", "IPY_MODEL_83bf1d573b474f78a94844613fd22792"], "layout": "IPY_MODEL_efbdbd902c8f481ab37b1b0e0faf7656"}}, "10f1e86f2f994221815ee12124793688": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0a95d9bbc61246f787c4492b766e68e0", "style": "IPY_MODEL_5881203ad5864b7b80c2a319a72c4ba5", "value": "<style>.jupyter-widgets.widget-tab > .p-TabBar .p-TabBar-tab {min-width: fit-content;}</style>"}}, "0a95d9bbc61246f787c4492b766e68e0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5881203ad5864b7b80c2a319a72c4ba5": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "83bf1d573b474f78a94844613fd22792": {"model_name": "TabModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_titles": {"0": "RMS Contrast vs Mean Red Relative Intensity", "1": "Brightness vs RMS Contrast", "2": "Brightness vs Mean Red Relative Intensity"}, "children": ["IPY_MODEL_833017efeca5459d9aa39fefbe911fb3", "IPY_MODEL_73dd37ba925b4af7af7021b09626eaca", "IPY_MODEL_365a529e316a48979304d0030b755e2e"], "layout": "IPY_MODEL_76979935885547d0bda039909fd576cd"}}, "833017efeca5459d9aa39fefbe911fb3": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_169624b8802547a99b5b10db955b85a9", "IPY_MODEL_bd4cc207742743569a2f90012e9a4c21"], "layout": "IPY_MODEL_678188e1d0ef475cbf19022d324374ba"}}, "169624b8802547a99b5b10db955b85a9": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"coloraxis": "coloraxis", "hovertemplate": "RMS Contrast: %{x}<br>Mean Red Relative Intensity: %{y}<br>Cross Entropy: %{z}<extra></extra>", "name": "0", "text": [["-1.63\n(14.29%)", "-2.77\n(9.66%)", "-5.66\n(5.46%)", "-0.87\n(5.46%)", "-0.95\n(5.46%)"], ["-1.31\n(2.94%)", "-1.49\n(5.46%)", "-3.45\n(2.1%)", "-0.07\n(1.68%)", "-0.76\n(2.94%)"], ["-2.66\n(2.94%)", "-0.57\n(4.2%)", "-4.09\n(1.26%)", "-3.1\n(2.52%)", "-1.18\n(3.78%)"], ["-1.69\n(4.2%)", "-3.04\n(5.46%)", "-0.36\n(0.42%)", "-2.91\n(2.1%)", "-3.44\n(2.94%)"], 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"model_module_version": "1.2.0", "state": {}}, "292894e8507d48198d06e8b476bc3718": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "baf43a300ae44ee78c962e9d3dc86bba": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "be0ee10d9f16448994548c7545ad41db": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_fea3df3326684bc2add58001c1712df1", "IPY_MODEL_b03c6221625d4389b5d33df74106c347"], "layout": "IPY_MODEL_f16724d17c514a12ba21f5d61f01fc08"}}, "fea3df3326684bc2add58001c1712df1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2c4fb3b2af454ac8b9b0965943279841", "style": "IPY_MODEL_d1303bd1d4074be7b01d41e384bfcd46", "value": "<span id=\"summary_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-0KB68\"></span>"}}, "2c4fb3b2af454ac8b9b0965943279841": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "d1303bd1d4074be7b01d41e384bfcd46": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b03c6221625d4389b5d33df74106c347": {"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_857d73d1caba4592a6b3d6958cf0a531"], "layout": "IPY_MODEL_5da90640b70040b88cedb8a257e91980", "selected_index": null}}, "857d73d1caba4592a6b3d6958cf0a531": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_c656f379c30f42e7b180e32daaacf10d", "IPY_MODEL_7d4c645494b84d3eb81669727ae5d22b", "IPY_MODEL_2adc680707a84477b239f8d755ef6135", "IPY_MODEL_1bb10b66d194439e9daf5e10194ca6cc", "IPY_MODEL_32dd6dd661e34380a72d692596e708a0"], "layout": "IPY_MODEL_306bb6dbb8754be9977e2b2c57fb8865"}}, "c656f379c30f42e7b180e32daaacf10d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5a49baf242a2422ba2452f5b0d65fd10", "style": "IPY_MODEL_2fac0919de524b41a6d31c7c550f49e0", "value": "<style type=\"text/css\">\n#T_25c8f table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_25c8f thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_25c8f tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_25c8f th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_25c8f td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_25c8f\">\n  <thead>\n    <tr>\n      <th id=\"T_25c8f_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_25c8f_level0_col1\" class=\"col_heading level0 col1\" >Check</th>\n      <th id=\"T_25c8f_level0_col2\" class=\"col_heading level0 col2\" >Condition</th>\n      <th id=\"T_25c8f_level0_col3\" class=\"col_heading level0 col3\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_25c8f_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_25c8f_row0_col1\" class=\"data row0 col1\" ><a href=\"#ImagePropertyDrift_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-0KB68\">Image Property Drift</a></td>\n      <td id=\"T_25c8f_row0_col2\" class=\"data row0 col2\" >drift score < 0.2 for image properties drift</td>\n      <td id=\"T_25c8f_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: 0.16</td>\n    </tr>\n    <tr>\n      <td id=\"T_25c8f_row1_col0\" class=\"data row1 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_25c8f_row1_col1\" class=\"data row1 col1\" ><a href=\"#LabelDrift_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-0KB68\">Label Drift</a></td>\n      <td id=\"T_25c8f_row1_col2\" class=\"data row1 col2\" >categorical drift score < 0.15 and numerical drift score < 0.15</td>\n      <td id=\"T_25c8f_row1_col3\" class=\"data row1 col3\" >Passed for 1 label properties out of 1 label properties.\nFound label property \"Samples Per Class\" has the highest categorical drift score: 0</td>\n    </tr>\n    <tr>\n      <td id=\"T_25c8f_row2_col0\" class=\"data row2 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_25c8f_row2_col1\" class=\"data row2 col1\" >Property Label Correlation Change</td>\n      <td id=\"T_25c8f_row2_col2\" class=\"data row2 col2\" >Train-Test properties' Predictive Power Score difference is less than 0.2</td>\n      <td id=\"T_25c8f_row2_col3\" class=\"data row2 col3\" >0 PPS found for all properties</td>\n    </tr>\n    <tr>\n      <td id=\"T_25c8f_row3_col0\" class=\"data row3 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_25c8f_row3_col1\" class=\"data row3 col1\" >New Labels</td>\n      <td id=\"T_25c8f_row3_col2\" class=\"data row3 col2\" >Percentage of new labels in the test set is less or equal to 0.5%</td>\n      <td id=\"T_25c8f_row3_col3\" class=\"data row3 col3\" >No new labels were found in test set.</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "5a49baf242a2422ba2452f5b0d65fd10": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2fac0919de524b41a6d31c7c550f49e0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "7d4c645494b84d3eb81669727ae5d22b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_f445cb5c4fe14fb596aa0d4fa5c619be", "style": "IPY_MODEL_c49eab774c1349f2bcd46c9b68c35cbc", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "f445cb5c4fe14fb596aa0d4fa5c619be": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c49eab774c1349f2bcd46c9b68c35cbc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2adc680707a84477b239f8d755ef6135": {"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_0732b65787f7493ebd90456b68965ba9", "IPY_MODEL_047c717564e140fa8c201e07e7e7b5b0", "IPY_MODEL_5c4022e2065f413e9301da5c6cf22c1a", "IPY_MODEL_15dc4a15656c40f3b5c6b9e781519226"], "layout": "IPY_MODEL_2aeed013cd99469782f97bf377935855"}}, "0732b65787f7493ebd90456b68965ba9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_182b52782d62479db06dc594bd58487e", "style": "IPY_MODEL_c4a678de5d46456aa4c47bf28aa24c75", "value": "<h4 id=\"ImagePropertyDrift_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-0KB68\"><b>Image Property Drift</b></h4>"}}, "182b52782d62479db06dc594bd58487e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c4a678de5d46456aa4c47bf28aa24c75": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "047c717564e140fa8c201e07e7e7b5b0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5e967c0823b2423ba3e544004567edc5", "style": "IPY_MODEL_2de525336cdb4cdcb6b46668e51504ec", "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>"}}, "5e967c0823b2423ba3e544004567edc5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2de525336cdb4cdcb6b46668e51504ec": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "5c4022e2065f413e9301da5c6cf22c1a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_68ab9ef7732b444f8434a3d1bd67c4b8", "style": "IPY_MODEL_cfadd986f4124eea8cbcbb9067c08d4c", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_77296 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_77296 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_77296 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_77296 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_77296 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_77296\">\n  <thead>\n    <tr>\n      <th id=\"T_77296_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_77296_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_77296_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_77296_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_77296_row0_col1\" class=\"data row0 col1\" >drift score < 0.2 for image properties drift</td>\n      <td id=\"T_77296_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: 0.16</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "68ab9ef7732b444f8434a3d1bd67c4b8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cfadd986f4124eea8cbcbb9067c08d4c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "15dc4a15656c40f3b5c6b9e781519226": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_af3ec718acfa4391901f225a2acc1fa1", "IPY_MODEL_ad3c3a86b2c94e0b90ee1aa72e937f74", "IPY_MODEL_120076726b8b464f851d43cc35ead87f", "IPY_MODEL_124bf6ca9a2446fa800e669593dd63ed", "IPY_MODEL_1ba48573415449b3aa5b7b1767536b05", "IPY_MODEL_4454a1e1102e47b0ac898a88fd2595a4", "IPY_MODEL_1fb7c03bd54c4eeeaf86413abfc64378", "IPY_MODEL_a1edcd64b35e489eaf72f69328f55bae", "IPY_MODEL_5a4c02138ec64388920580909d3e554e", "IPY_MODEL_b30cdfdc8fcc453291dcb0855a0d0fe0", "IPY_MODEL_5c871f7737494576b29c936f9a420ab8"], "layout": "IPY_MODEL_73f39a3bd3eb4e71ba082b9d243593d9"}}, "af3ec718acfa4391901f225a2acc1fa1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_8367cded080f4bf7a105b3df219fc158", "style": "IPY_MODEL_cf02296ecff748d7b577fc9161d3acad", "value": "<h5><b>Additional Outputs</b></h5>"}}, "8367cded080f4bf7a105b3df219fc158": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "cf02296ecff748d7b577fc9161d3acad": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ad3c3a86b2c94e0b90ee1aa72e937f74": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_284a9b4eb491431d8af8b9aecf4b158b", "style": "IPY_MODEL_7a63c45c846a4279b641916e8c7aa862", "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>"}}, "284a9b4eb491431d8af8b9aecf4b158b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7a63c45c846a4279b641916e8c7aa862": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "120076726b8b464f851d43cc35ead87f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ed965c6852654d7d9ebc1b05d66de950", "style": "IPY_MODEL_80f2898df97b4411b3eb4fb6d217c29b", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, 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"7b68338be23944b6a6666cc4b586277e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "32dd6dd661e34380a72d692596e708a0": {"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_e46d78c23272415cb7aba4afcbe9ce64", "IPY_MODEL_f296132cdb7040ea9ab56a043ed9b396", "IPY_MODEL_78d372bec6b9453e926fc313d05796f7", "IPY_MODEL_24f797a550b24ae99b0542bc07cf99fa"], "layout": "IPY_MODEL_8fd7c66c533849dda59b46682c93692d"}}, "e46d78c23272415cb7aba4afcbe9ce64": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fd3ddb9630e44e23bb3e19998c6e12eb", "style": "IPY_MODEL_6ccf69e9bc32452fafefc4dfae97d490", "value": "<h4 id=\"LabelDrift_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-0KB68\"><b>Label Drift</b></h4>"}}, "fd3ddb9630e44e23bb3e19998c6e12eb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6ccf69e9bc32452fafefc4dfae97d490": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f296132cdb7040ea9ab56a043ed9b396": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_deb3734348cb4386bd0626777a19c3bf", "style": "IPY_MODEL_525aa10217c94a55a765d50ca5014664", "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>"}}, "deb3734348cb4386bd0626777a19c3bf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "525aa10217c94a55a765d50ca5014664": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "78d372bec6b9453e926fc313d05796f7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_cfeae9fb37544a05b2b6df9aabcbee72", "style": "IPY_MODEL_10cd137713b94fe19e47da7afccc25e7", "value": "<h5><b>Conditions Summary</b></h5><style type=\"text/css\">\n#T_5159a table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5159a thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5159a tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5159a th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_5159a td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_5159a\">\n  <thead>\n    <tr>\n      <th id=\"T_5159a_level0_col0\" class=\"col_heading level0 col0\" >Status</th>\n      <th id=\"T_5159a_level0_col1\" class=\"col_heading level0 col1\" >Condition</th>\n      <th id=\"T_5159a_level0_col2\" class=\"col_heading level0 col2\" >More Info</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_5159a_row0_col0\" class=\"data row0 col0\" ><div style=\"color: green;text-align: center\">\u2713</div></td>\n      <td id=\"T_5159a_row0_col1\" class=\"data row0 col1\" >categorical drift score < 0.15 and numerical drift score < 0.15</td>\n      <td id=\"T_5159a_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</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "cfeae9fb37544a05b2b6df9aabcbee72": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "10cd137713b94fe19e47da7afccc25e7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "24f797a550b24ae99b0542bc07cf99fa": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_b28ffbf30b7b44f3912e2c3355f7150b", "IPY_MODEL_d3631d3a0c794482bba9de1695512f4c", "IPY_MODEL_3a356ace1ee542ce852e41f4ed92d910", "IPY_MODEL_f4e43cdd3fdb4d94a8b28b57a1ee355a", "IPY_MODEL_8743c0c25d254ffa8b2e19573fb13f70"], "layout": "IPY_MODEL_ed7b146ec91d401395ad602391ba9e21"}}, "b28ffbf30b7b44f3912e2c3355f7150b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_62a6be321e704adab5fabdb648f96299", "style": "IPY_MODEL_da79e2d085ea44eb8dd166ddeb12a076", "value": "<h5><b>Additional Outputs</b></h5>"}}, "62a6be321e704adab5fabdb648f96299": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "da79e2d085ea44eb8dd166ddeb12a076": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d3631d3a0c794482bba9de1695512f4c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_863a755e4dea42c79045816226de4479", "style": "IPY_MODEL_abb4ec88d9f440949ac037548178255f", "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>"}}, "863a755e4dea42c79045816226de4479": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "abb4ec88d9f440949ac037548178255f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3a356ace1ee542ce852e41f4ed92d910": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_33460860f3e440c18b182d74c78b019e", "style": "IPY_MODEL_daba086701934322be0ab9efedba67bc", "value": "<div>For discrete distribution plots, showing the top 10 categories with largest difference between train and test.</div>"}}, "33460860f3e440c18b182d74c78b019e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "daba086701934322be0ab9efedba67bc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f4e43cdd3fdb4d94a8b28b57a1ee355a": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#01B8AA"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.0], "xaxis": "x", "y": ["Drift Score"], "yaxis": "y", "type": "bar", "uid": "69f2db82-4b1a-4d47-af6a-8b133b9e47c0"}, {"marker": {"color": "#00008b"}, "name": "Train Dataset", "x": ["aedes_aegypti", "aedes_albopictus", "culex_quinquefasciatus"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [3]}, "yaxis": "y2", "type": "bar", "uid": "e2ac45a6-c64a-419e-ae4f-728654a235a5"}, {"marker": {"color": "#69b3a2"}, "name": "Test Dataset", "x": ["aedes_aegypti", "aedes_albopictus", "culex_quinquefasciatus"], "xaxis": "x2", "y": {"dtype": "float64", "shape": [3]}, "yaxis": "y2", "type": "bar", "uid": "5b0653b4-8657-4ee8-a18a-8ef431e01731"}], "_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"}], 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"LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f55e1fa5c1cb4bb38db4a18af7b98eae": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_6e22344bfdc44c53a09f086f5fd56de2", "IPY_MODEL_d1375bd909b4436cb77df58774f01294"], "layout": "IPY_MODEL_ef32479fc03247b498aec0f55947d9f1"}}, "6e22344bfdc44c53a09f086f5fd56de2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_30d9f83c4f98493199586b114335d068", "style": "IPY_MODEL_11c50552e20641b89af93659b4489b54", "value": "<span id=\"summary_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\"></span>"}}, "30d9f83c4f98493199586b114335d068": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "11c50552e20641b89af93659b4489b54": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d1375bd909b4436cb77df58774f01294": {"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_38cd905d335742279eb04c2b1acea29c"], "layout": "IPY_MODEL_3caeee57d3e54fb389b6e2f1f57656cd", "selected_index": null}}, "38cd905d335742279eb04c2b1acea29c": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_ba95233f0b224826b66b30fad369c941", "IPY_MODEL_70b7e4e1d1b74eb2898926c82bcd4ad2", "IPY_MODEL_d393a96843144244a817f445647628bf", "IPY_MODEL_a5774e886f7d400e8165fd868eba1107", "IPY_MODEL_dac4554219cc4ceab3d74ca213d36a43", "IPY_MODEL_a5774e886f7d400e8165fd868eba1107", "IPY_MODEL_eb4932d3d43c43ca9831e214251dbcfc", "IPY_MODEL_a5774e886f7d400e8165fd868eba1107", "IPY_MODEL_7d3cd174be9948d78e841c2f8a55efbc", "IPY_MODEL_a5774e886f7d400e8165fd868eba1107", "IPY_MODEL_58a98f54f6f24d5c8a140a3d4a7c089a", "IPY_MODEL_a5774e886f7d400e8165fd868eba1107", "IPY_MODEL_2e2e347151ac475b96e3589cfea76826", "IPY_MODEL_a5774e886f7d400e8165fd868eba1107", "IPY_MODEL_508864d3fffb45c2b0725aabbc7b5153", "IPY_MODEL_a5774e886f7d400e8165fd868eba1107", "IPY_MODEL_97d5e951db0748f5bd62e65bef1c30e6"], "layout": "IPY_MODEL_5afd2dbe0a73405bbeedd183472b4f82"}}, "ba95233f0b224826b66b30fad369c941": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_73af25051d2949e1bcf654895ddabada", "style": "IPY_MODEL_bdad03e5d8b74a879fe47f017ca88766", "value": "<style type=\"text/css\">\n#T_0ffad table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0ffad thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0ffad tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0ffad th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_0ffad td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_0ffad\">\n  <thead>\n    <tr>\n      <th id=\"T_0ffad_level0_col0\" class=\"col_heading level0 col0\" >Check</th>\n      <th id=\"T_0ffad_level0_col1\" class=\"col_heading level0 col1\" >Summary</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_0ffad_row0_col0\" class=\"data row0 col0\" ><a href=\"#ImageDatasetDrift_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\">Image Dataset Drift</a></td>\n      <td id=\"T_0ffad_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_0ffad_row1_col0\" class=\"data row1 col0\" ><a href=\"#ImagePropertyOutliers_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\">Image Property Outliers</a></td>\n      <td id=\"T_0ffad_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_0ffad_row2_col0\" class=\"data row2 col0\" ><a href=\"#ImagePropertyOutliers_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\">Image Property Outliers</a></td>\n      <td id=\"T_0ffad_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_0ffad_row3_col0\" class=\"data row3 col0\" ><a href=\"#PropertyLabelCorrelation_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\">Property Label Correlation</a></td>\n      <td id=\"T_0ffad_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_0ffad_row4_col0\" class=\"data row4 col0\" ><a href=\"#PropertyLabelCorrelation_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\">Property Label Correlation</a></td>\n      <td id=\"T_0ffad_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_0ffad_row5_col0\" class=\"data row5 col0\" ><a href=\"#ConfusionMatrix_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\">Confusion Matrix</a></td>\n      <td id=\"T_0ffad_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_0ffad_row6_col0\" class=\"data row6 col0\" ><a href=\"#ConfusionMatrix_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\">Confusion Matrix</a></td>\n      <td id=\"T_0ffad_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_0ffad_row7_col0\" class=\"data row7 col0\" ><a href=\"#HeatmapComparison_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\">Heatmap Comparison</a></td>\n      <td id=\"T_0ffad_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"}}, "73af25051d2949e1bcf654895ddabada": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "bdad03e5d8b74a879fe47f017ca88766": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "70b7e4e1d1b74eb2898926c82bcd4ad2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_783b8df2533f491c8463433b9572ab36", "style": "IPY_MODEL_ffcce32ae01f4965892d0046aab0535a", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "783b8df2533f491c8463433b9572ab36": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ffcce32ae01f4965892d0046aab0535a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d393a96843144244a817f445647628bf": {"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_49064c6d00c54ce9b28f19b19d456ab9", "IPY_MODEL_df6c0a93f8e0409287a595a4a6105802", "IPY_MODEL_4c1b812805ab486bb22f92ecd77ede1f"], "layout": "IPY_MODEL_3d0b53c0a1374682acc1681328033e42"}}, "49064c6d00c54ce9b28f19b19d456ab9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_90dbf9f4bdd34e7d9863e46787556f4f", "style": "IPY_MODEL_51ce2711aac14b51be22d60800871020", "value": "<h4 id=\"ImageDatasetDrift_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\"><b>Image Dataset Drift</b></h4>"}}, "90dbf9f4bdd34e7d9863e46787556f4f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "51ce2711aac14b51be22d60800871020": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "df6c0a93f8e0409287a595a4a6105802": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e623d30a9cf14c1c924c59a13cd30e72", "style": "IPY_MODEL_32332fad727d450292319b52ac0bd241", "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>"}}, "e623d30a9cf14c1c924c59a13cd30e72": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "32332fad727d450292319b52ac0bd241": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "4c1b812805ab486bb22f92ecd77ede1f": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_3c71a3bd7a8244ffb69eb3ec8d1927ff", "IPY_MODEL_61675d9487ae4e57943d05e656a84276", "IPY_MODEL_b19ff016f3e2459486476c45965f2701", "IPY_MODEL_1141a85f6eb748fdae50e0915439617d", "IPY_MODEL_a70ebaac24734759a9f0e26adfa3b321", "IPY_MODEL_fdd73fe4dd2448a5a01c4dc5781ba6c6", "IPY_MODEL_9538c19a7b7d45d08f248d67f1b1fb70", "IPY_MODEL_6bf0076ab5324bfea8f90a1269b0c0ca", "IPY_MODEL_8aa70ea3122445db8a25a8a0941ad334", "IPY_MODEL_f7cad2d32b8c45f983c7dc26d3d6d0d1", "IPY_MODEL_1616007d65504cfb865fd6e9520b9a85"], "layout": "IPY_MODEL_1f6cc23722b747cabd99185bdc21ad97"}}, "3c71a3bd7a8244ffb69eb3ec8d1927ff": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e236efb9d2ec4422b86773f8946d2131", "style": "IPY_MODEL_a9231d6335c544b5b08e68a876e510fc", "value": "<h5><b>Additional Outputs</b></h5>"}}, "e236efb9d2ec4422b86773f8946d2131": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "a9231d6335c544b5b08e68a876e510fc": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "61675d9487ae4e57943d05e656a84276": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_9f65d7ea2a3d42dcbcf925c740783bbe", "style": "IPY_MODEL_9d2d05e45da448369f576a2f2336d75b", "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>"}}, "9f65d7ea2a3d42dcbcf925c740783bbe": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9d2d05e45da448369f576a2f2336d75b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b19ff016f3e2459486476c45965f2701": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_7e7a116454ac4aacab8fda07b2f0456e", "style": "IPY_MODEL_ddd955437d0f491abfc1ecc9537ab725", "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>"}}, "7e7a116454ac4aacab8fda07b2f0456e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ddd955437d0f491abfc1ecc9537ab725": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1141a85f6eb748fdae50e0915439617d": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"base": 0, "marker": {"color": "#01B8AA"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.1], "y": ["Drift Score"], "type": "bar", "uid": "6542432c-f623-48b7-8ce2-ab7e1930874c"}, {"base": 0.1, "marker": {"color": "#F2C80F"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.1], "y": ["Drift Score"], "type": "bar", "uid": "428a7d99-4d85-455e-bcca-6f769c9d3b8c"}, {"base": 0.2, "marker": {"color": "#FE9666"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.09999999999999998], "y": ["Drift Score"], "type": "bar", "uid": "b59b214c-8637-4e22-99ea-53d974656ce2"}, {"base": 0.3, "marker": {"color": "#FD625E"}, "offsetgroup": "0", "orientation": "h", "showlegend": false, "x": [0.13153364632237913], "y": ["Drift Score"], "type": "bar", "uid": "b25c820c-d1d7-43f8-8e06-6bc921d929fa"}], "_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"], 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{"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 13.78% of dataset difference"}, "xaxis": {"fixedrange": false, "range": [0.30790131974864204, 0.503689127168689], "title": {"text": "Mean Red Relative Intensity"}}, "yaxis": {"fixedrange": true, "title": {"text": "Probability Density"}}}, "_py2js_addTraces": {}, "_py2js_animate": {}, "_py2js_deleteTraces": {}, 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"model_module_version": "1.2.0", "state": {}}, "a5774e886f7d400e8165fd868eba1107": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_619361369fe64237884782745e4493b5", "style": "IPY_MODEL_70e9dd7dddd14ba4a1e0201aac34fb25", "value": "<hr style=\"background-color: #eee;border: 0 none;color: #eee;height: 4px;\">"}}, "619361369fe64237884782745e4493b5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "70e9dd7dddd14ba4a1e0201aac34fb25": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "dac4554219cc4ceab3d74ca213d36a43": {"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_f6e1f3136db440328e82150012d8d2a4", "IPY_MODEL_a3ed8ba5f8b948829447f8472906f06f", "IPY_MODEL_aefe5439824b4bd9a887e2d058c8f3f3"], "layout": "IPY_MODEL_e7ba0fdf3cfc478d8ec7a32b9fd613b6"}}, "f6e1f3136db440328e82150012d8d2a4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4aa849355dab4baaa8f97b1e3dec5633", "style": "IPY_MODEL_4fc318b3c5a446edbead41dfe95d9df1", "value": "<h4 id=\"ImagePropertyOutliers_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\"><b>Image Property Outliers</b></h4>"}}, "4aa849355dab4baaa8f97b1e3dec5633": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "4fc318b3c5a446edbead41dfe95d9df1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a3ed8ba5f8b948829447f8472906f06f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_fc42d142f71643f6bdffd3c4a428659e", "style": "IPY_MODEL_8dea7bb060944c6a87c60c480d17c5ef", "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>"}}, "fc42d142f71643f6bdffd3c4a428659e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "8dea7bb060944c6a87c60c480d17c5ef": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "aefe5439824b4bd9a887e2d058c8f3f3": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_ab7db42db0bd46c3966c775e83bae200", "IPY_MODEL_42f7d59cf04b4373b4c948f62eb9e793", "IPY_MODEL_860aed3bee684f339aac617af01f663a", "IPY_MODEL_f336a215bc804ccc9667ebbd6d6b9253", "IPY_MODEL_1e6d9a47e41b48d4adc5098fa1cd67c0"], "layout": "IPY_MODEL_55367b2271ad41d29c2dc709d46f1fb2"}}, "ab7db42db0bd46c3966c775e83bae200": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_47829e978b514f22b66d29c577d4ff18", "style": "IPY_MODEL_3cf94f71bdd34d5d9eec8265abab6617", "value": "<h5><b>Additional Outputs</b></h5>"}}, "47829e978b514f22b66d29c577d4ff18": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3cf94f71bdd34d5d9eec8265abab6617": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "42f7d59cf04b4373b4c948f62eb9e793": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_76baefe9173a4c96828973c23eaec039", "style": "IPY_MODEL_3cd22a85acb4430b9beae4e840366e99", "value": "<div>\n<style>\n    .GODRMD-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .GODRMD-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .GODRMD-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    .GODRMD-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: 2\n</div>\n<div>\nNon-outliers range: 58.97 to 209.53\n</div>\n<div class=\"GODRMD-container\">\n    <div class=\"GODRMD-row\">\n        <h5 class=\"GODRMD-item\">Brightness</h5>\n        <div class=\"GODRMD-item\">51.19</div><div class=\"GODRMD-item\">50.98</div>\n    </div>\n    <div class=\"GODRMD-row\">\n        <h5 class=\"GODRMD-item\">Image</h5>\n        <div class=\"GODRMD-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"GODRMD-item\"><img src=\"data:image/png;base64,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\"/></div>\n    </div>\n</div>\n\n<style>\n    .IZBYMM-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .IZBYMM-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .IZBYMM-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    .IZBYMM-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: 6\n</div>\n<div>\nNon-outliers range: 21.1 to 56.18\n</div>\n<div class=\"IZBYMM-container\">\n    <div class=\"IZBYMM-row\">\n        <h5 class=\"IZBYMM-item\">RMS Contrast</h5>\n        <div class=\"IZBYMM-item\">68.94</div><div class=\"IZBYMM-item\">69.62</div><div class=\"IZBYMM-item\">83.36</div>\n    </div>\n    <div class=\"IZBYMM-row\">\n        <h5 class=\"IZBYMM-item\">Image</h5>\n        <div class=\"IZBYMM-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"IZBYMM-item\"><img 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   </div>\n</div>\n\n<style>\n    .FVYXQB-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .FVYXQB-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .FVYXQB-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    .FVYXQB-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: 6\n</div>\n<div>\nNon-outliers range: 0.3 to 0.48\n</div>\n<div class=\"FVYXQB-container\">\n    <div class=\"FVYXQB-row\">\n        <h5 class=\"FVYXQB-item\">Mean Red Relative Intensity</h5>\n        <div class=\"FVYXQB-item\">0.56</div><div class=\"FVYXQB-item\">0.68</div><div class=\"FVYXQB-item\">0.71</div>\n    </div>\n    <div class=\"FVYXQB-row\">\n        <h5 class=\"FVYXQB-item\">Image</h5>\n        <div class=\"FVYXQB-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"FVYXQB-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"FVYXQB-item\"><img src=\"data:image/png;base64,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\"/></div>\n    </div>\n</div>\n\n<style>\n    .LQLVQT-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .LQLVQT-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .LQLVQT-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    .LQLVQT-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: 12\n</div>\n<div>\nNon-outliers range: 0.28 to 0.37\n</div>\n<div class=\"LQLVQT-container\">\n    <div class=\"LQLVQT-row\">\n        <h5 class=\"LQLVQT-item\">Mean Green Relative Intensity</h5>\n        <div class=\"LQLVQT-item\">0.26</div><div class=\"LQLVQT-item\">0.13</div><div class=\"LQLVQT-item\">0.15</div><div class=\"LQLVQT-item\">0.41</div><div class=\"LQLVQT-item\">0.42</div>\n    </div>\n    <div class=\"LQLVQT-row\">\n        <h5 class=\"LQLVQT-item\">Image</h5>\n        <div class=\"LQLVQT-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"LQLVQT-item\"><img 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   </div>\n</div>\n\n<style>\n    .YDBLNQ-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .YDBLNQ-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .YDBLNQ-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    .YDBLNQ-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: 5\n</div>\n<div>\nNon-outliers range: 0.19 to 0.38\n</div>\n<div class=\"YDBLNQ-container\">\n    <div class=\"YDBLNQ-row\">\n        <h5 class=\"YDBLNQ-item\">Mean Blue Relative Intensity</h5>\n        <div class=\"YDBLNQ-item\">0.16</div><div class=\"YDBLNQ-item\">0.12</div><div class=\"YDBLNQ-item\">0.17</div>\n    </div>\n    <div class=\"YDBLNQ-row\">\n        <h5 class=\"YDBLNQ-item\">Image</h5>\n        <div class=\"YDBLNQ-item\"><img src=\"data:image/png;base64,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\"/></div><div class=\"YDBLNQ-item\"><img 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class=\"YDBLNQ-item\"><img 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VImD3PzCAeTAkgIQnQrlrUr3TXVSBCEUKtKqAIzCEmRGRMIQCBgIQiNYnfMuDCEJn7+1/j9RLfH4nzCEqsi+WjxRH4oxCEXxh7S8b5h40IR/GHtI3/IkG1IHrCElOYNqptTk+XJMah3+o4EIR2Ye1orVE7y3xhjA4hCVnqJt9jCEIsh7X//Z\"/></div>\n    </div>\n</div>\n</div>"}}, "76baefe9173a4c96828973c23eaec039": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3cd22a85acb4430b9beae4e840366e99": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "860aed3bee684f339aac617af01f663a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_164798bf770248a6aa2832bab4227bb2", "style": "IPY_MODEL_c6cf3c34329842f9959469825417613b", "value": "<div><h5><b>Properties With No Outliers Found</h5></b></div>"}}, "164798bf770248a6aa2832bab4227bb2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c6cf3c34329842f9959469825417613b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "f336a215bc804ccc9667ebbd6d6b9253": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4aa5986e73b34784b6b85ec2b73b23b8", "style": "IPY_MODEL_1d21dac165f64e8a8e2a5179d941def7", "value": "<style type=\"text/css\">\n#T_7514f table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7514f thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7514f tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7514f th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_7514f td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_7514f\">\n  <thead>\n    <tr>\n      <th id=\"T_7514f_level0_col0\" class=\"col_heading level0 col0\" >More Info</th>\n      <th id=\"T_7514f_level0_col1\" class=\"col_heading level0 col1\" >Properties</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_7514f_row0_col0\" class=\"data row0 col0\" >No outliers found.</td>\n      <td id=\"T_7514f_row0_col1\" class=\"data row0 col1\" >Aspect Ratio, Area</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "4aa5986e73b34784b6b85ec2b73b23b8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1d21dac165f64e8a8e2a5179d941def7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1e6d9a47e41b48d4adc5098fa1cd67c0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_22f269a7d6a94a1c88f03de6ec2d2a0a", "style": "IPY_MODEL_11e91037c5c1458bb33b6a655a90e8ea", "value": "<br><a href=\"#summary_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\" style=\"font-size: 14px;\">Go to top</a>"}}, "22f269a7d6a94a1c88f03de6ec2d2a0a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "11e91037c5c1458bb33b6a655a90e8ea": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "55367b2271ad41d29c2dc709d46f1fb2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e7ba0fdf3cfc478d8ec7a32b9fd613b6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "eb4932d3d43c43ca9831e214251dbcfc": {"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_80f16f7f96934c7dab8a272a9210da68", "IPY_MODEL_11c79ea069f64270b1975f9b96429fdb", "IPY_MODEL_579588409c3b44869545e51f1ecb3186"], "layout": "IPY_MODEL_fe88706870694920abfac8746ce326d4"}}, "80f16f7f96934c7dab8a272a9210da68": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0abf71510eb44af4982b1c96796363ba", "style": "IPY_MODEL_15325bc7e8a54e629324576ddad862c4", "value": "<h4 id=\"ImagePropertyOutliers_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\"><b>Image Property Outliers</b></h4>"}}, "0abf71510eb44af4982b1c96796363ba": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "15325bc7e8a54e629324576ddad862c4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "11c79ea069f64270b1975f9b96429fdb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_a677916bc4704dfdb0bbf168e340d6f3", "style": "IPY_MODEL_1f7bdf1c328143f691d0a21e8093ac67", "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>"}}, "a677916bc4704dfdb0bbf168e340d6f3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1f7bdf1c328143f691d0a21e8093ac67": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "579588409c3b44869545e51f1ecb3186": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_babfb2cedabe4c5b99a453b30c8cecd5", "IPY_MODEL_51b2f5b3d9bc4a5c9db7df30744c1a08", "IPY_MODEL_3a12606ce77a446081d823e284af33db", "IPY_MODEL_e9bfa6da5b2d407798ce1d2fc3301b16", "IPY_MODEL_d0fdda61aa6148d5a1aba553543f399c"], "layout": "IPY_MODEL_1bf73d9d75894a8c862ade39c840962d"}}, "babfb2cedabe4c5b99a453b30c8cecd5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_21d98db2b95f4d19916d8529ec2b6579", "style": "IPY_MODEL_6bd781d5ffc34addb41eedc5eb6ae0d4", "value": "<h5><b>Additional Outputs</b></h5>"}}, "21d98db2b95f4d19916d8529ec2b6579": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6bd781d5ffc34addb41eedc5eb6ae0d4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "51b2f5b3d9bc4a5c9db7df30744c1a08": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3d364ea68cfa46dd925687d1caf5fb47", "style": "IPY_MODEL_842a92d5d85649a0bc3d3eb0003fd3d2", "value": "<div>\n<style>\n    .FUUWIT-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .FUUWIT-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .FUUWIT-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    .FUUWIT-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: 21\n</div>\n<div>\nNon-outliers range: 56.89 to 220.39\n</div>\n<div class=\"FUUWIT-container\">\n    <div class=\"FUUWIT-row\">\n        <h5 class=\"FUUWIT-item\">Brightness</h5>\n        <div class=\"FUUWIT-item\">14.51</div><div class=\"FUUWIT-item\">16.75</div><div class=\"FUUWIT-item\">19.55</div><div class=\"FUUWIT-item\">229.18</div><div class=\"FUUWIT-item\">233.48</div><div class=\"FUUWIT-item\">233.06</div>\n    </div>\n    <div class=\"FUUWIT-row\">\n        <h5 class=\"FUUWIT-item\">Image</h5>\n        <div class=\"FUUWIT-item\"><img 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class=\"FUUWIT-item\"><img 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\"/></div>\n    </div>\n</div>\n\n<style>\n    .TNSLVN-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .TNSLVN-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .TNSLVN-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    .TNSLVN-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: 6\n</div>\n<div>\nNon-outliers range: 10.59 to 69.15\n</div>\n<div class=\"TNSLVN-container\">\n    <div class=\"TNSLVN-row\">\n        <h5 class=\"TNSLVN-item\">RMS Contrast</h5>\n        <div class=\"TNSLVN-item\">71.92</div><div class=\"TNSLVN-item\">75.46</div><div class=\"TNSLVN-item\">92.64</div>\n    </div>\n    <div class=\"TNSLVN-row\">\n        <h5 class=\"TNSLVN-item\">Image</h5>\n        <div class=\"TNSLVN-item\"><img src=\"data:image/png;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA0JCgsKCA0LCgsODg0PEyAVExISEyccHhcgLikxMC4pLSwzOko+MzZGNywtQFdBRkxOUlNSMj5aYVpQYEpRUk//2wBDAQ4ODhMREyYVFSZPNS01T09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT0//wAARCADIAMgDASIAAhEBAxEB/8QAGwAAAQUBAQAAAAAAAAAAAAAAAgABAwQFBgf/xAA4EAACAQMCBQIEBQMDBAMAAAABAgMABBESIQUxQVFhEyIGMnGBFCNCkaFSwdFiseEVM3LxJEPw/8QAGQEAAwEBAQAAAAAAAAAAAAAAAAEDAgQF/8QAJBEAAwEAAgICAgIDAAAAAAAAAAECEQMhEjEEQRMiFFEyQmH/2gAMAwEAAhEDEQA/AO3GEnaMbgHeggcSs2rdVJ+9JgIidW7tuKOBNRBUDlv4rQiSBmYbgePpUOPUMkTHJzqG1SSOTknmp5DoKhBO753xkmgB4iGJZd/bhfBoG9sqqMFTyA6mij9gXQAAdsdvNR6cuW3LA7CgA2IV9+e/8VHESqM2djzNMUDMSckjp3qRs+kFwCGcDA6UAIsJYvUwwTOADzY/4p4cmQjGwO/cnsKGTZkUnJ1bjtUkbfmnck5xtQBJfyFYI9O8hbl/migUqUC7nQSc96jlXDszZ5D6kVNr9Ocqo3ChQPNACgGm1yPmOSx/tQNgSpGuAqZ1Y6nFTQARwsgPv3b6eaqJtae3d3Iwe+etAFlGCRjlun3OaCQAoHIy2NOKUhCQRgAls6cChl9mlfuxoAjDYznkNhVKeTALDeiubkaSEU5Gazp5sRBT/TWfLfRpy17KV9ckRuDXHcVuOYBra4lc7Ng865G/m9SUgGmBVY5OaalSoEKnpqegQ43O1KpLdC8yqOppU0gPcFzJpG5B2z1qRysaMFbKjl5NAjenD6h/q2NNEudUkjEqckbUATSZeFAfaG3bzUOr8zSAAo79TRyuxUnGAAAPFRhQ0vhRjegCUkELIG+U4GKh5ow5MxznxU8QwRqG2dgKrj2TEncsdhzwKYC+V9hzwN+tSSssEIZtxkkdM1CSBdHG+kb09ycyoTuq779T4FAArgsJZ2OcFsDlVC5vZZwBExih3ZQp3fPU/wCKk4hJ6arBn3y+3PLbr/j71nySIA2nbfSQP047VO6+jcrTY4dJq4dcOzkmL8tQTuBV2xGZUxktgZJ/msrgq6rVwPnuHy3YKOX85rWjJSJmU+5uZ8DoKc+jNew0OHnOd5G0rjmaCJlMoiwMIMZ81gz3lyI/UtkMY1ZBB+UVsWgZ4EkfZmOSR1pRaro3fG4SbLMmdbKNtJ3qlxe5EMOV3GOnOrk0qh9WOY3rGm3uTK7kld6LbzEKM3WVk9WON2mfdhjT2FKa2/E27yi4SOQL7Ubr/ioWm1y6ByZs48VYiRvxFxrQM43UoNiex8VOq8FiLTP5G6ZwXFLkjI5EnGKwpVdJWWVGR1OCrAgj7V0fGONQQ3sq2lpBJMrDM7rkhh2H/reuclmknlaWZ2eRzlmY5JNVltkaSXWgUqs2sEVxdwQh5SHAL6ItTA7khVHPlWoeCEYlup1srBiXVXlDupxgAjb3cs9qG0hY2YsMMk8yQwozyOcKo5mrVxa29tBiS5L3Z5xRjKx+GJ6/Srh4tb2KFOC2wikZdL3Mg1ORnOBnYedt6yEUswAprsRpcGg13AcjZaVafDIRFEu2/WlW0TZ6i5X0T12oIwWRBuFJ3HWm1syMqbKdvNEn5aBebcgBzpGw3KlWwPZ080EWW97c25KKJyFg0sehyaFD+WuPuaACmZvS3I1seX9IqEDSRkZC/wA1JIv5q5G/OlIVAOTlj/8AtqAI5xpYRpgO51SHsKTEGT1DyXkO1Ag2csN13PnxSk91upY4I3IFIDMvtMl45LYKIAp/1HPOs1ywf09iygK+OhqWZgHmnKmTOdS5+YnYftT8L9GOYvO40xbDO+pqhXbLSnh0dsmiAaFCAAb9qsCWOIKzHCoP3NU452ZkW5BVHOVIGB9KhvHuzInowPKrZOAO1VdfrqMKP2xlPiC+s+UJDsct0FW4rueO2WMRBmRfmJwPFU47WeaZGuV0xlt0VsknpnsKmueIQxztbgMSP0ouo/t281CU1r9HRdLFPsjl4hcJdCOcDSy5wgyQar3MnpKeYLHUc0811Gys6wyFkHis2W4NyFLQzY5fmOB/nFal49bM0vJZKws2jgLNckgiPbGP5q1HxP1gwaMtIxxjGB9apQWzJqiaZN//AK1lL/sAKp8TiubSGSYXDsqnSEiQlye21Ttt10W41Mxlezmb34d4pFcSlLGT0Q5CMGBGM7bk9qkseAhFNzxmQWtqmxAYaieg2zirBnPELz047KUFR75rxmITCkkMFwB9zW1wiw4bxaxa89921vhGLx6VQ4zhU5Y35nJqrppdnMpl+jBl+IIbWM2/C4NMI+XVlRz5kcz9zWdaQX/Hb5YPWMjqjMDK3tQDf7AnA271Nx6wht+LPBw6NimgExLlzGeoPXt+9Wfh6+k4dDfhbcPOoQrHpOtt8Ecs4xvyrXSWoXusozb3hN/YgvcW7CMHHqL7l/cfWj4XbepJrI2FbfxJxKWWOLhsavErxxzSKp+bUM6W67H/AJqKxhEaBcVvjba1mLSTxF2BNI5UquQRxxxFp1fWflQggfXNKh2LwOzJGTp9qrRxAg77D+aigGSWP81Jkk6U3BO9aEFd4VAG5Ny+lMjg5YclGwp5SpVjI3IfQAVnrdtLlLZcRr80p6Dx3NJ0l7NTLr0aM7aRGVxq5HwaijKsSTlscvJqO4nX01WGFysY36f71XiluZMyAJGvIKRn70vNB4P2Tglmft1qK7YpZyMPmbYeKsBSQBg4xk1ncUc/kxq2QWyQOW1OvQl7Mq4dnnwFwE7dT0zRaI400MdW2osRuDVq0gFzcLHyVTqkI55rKvmZdSknOojPiuTk6Wnf8ZKqwvRTzXEkMSzOdwFGchRXSRO0a5LZLbEnmcVx/CZgl3ES2AD1rfluggY6sBRzNU4O02T+YvGkiDjnFl4bZmRR79wn1rA4Lazz+pfz3LpLKCVIO+O5HbxVK8vE43xVFmb07SDOkgbt9frW3DeWFsipCvTc9c9vpSuuw4uJ4VS/El9kluksW+XR9Ocb5yeVBO8kUUotBpckECY5Xlv7l2z9a1Ib1J7aeSeUJFH7QgX5s86yx8PcLuiz2008SqcBUkxnsxBzSTTWM1cUnqRzvrcXtbqMyvdTRIyuVilZlcA5+ZeXKtC6+M3LseH2oQsRl5W1dP6QcD96zZuKz2d/cJbyGaIOyq0vznfmWGCeviiTjMVwmjiFtC7AFRI0YbA7f1D9zVc/tHNv0maHw9xe8kdbS3g/Ez3MjyXbyE/KcKGz4zvt2FVHu77hXqWHAmuTbqxEsoi1es42J5HAHLGela9pdS2LFo+GwIXUZ9JRGT1HT+K0zfTPZvL6GkRjWdJPTfGOZrHmi34azWznPhOC4/GXcrxzJctGVinfOFc89Q/VnIO4PKusnuXS5UWsQE4Ygt0ANYCfFMBuo0ghkldiAhijCkk9Pcc1twW0Ppl5zMHkOWQyD9sjnWL19s1x+M+uypxNY7u0MZ4fJJdIxVZ9AGMEEnOeRydqoJaOmksQoJxudx9hXQObS1jMccIHPOjBCeao31xArAxbyMMZB2CnxW5tpYjFwm/KioWbZS+oKMClQAilT8iPiduhBXA61JEQrDP2qkk4AovW6g1YmVuIrcXF0I2Qi2DbkHn9aKeRjHmAKqxj9I5CpX9OTdgPO9RrFD6boS5VzuNZAqVcbb0uuScSKqSSSNpZmYkHYfSq1tx4RosdzFpdRh4wCGX7HmKsRvLDKSYDyx7cYqC+EN46m4hMbr8smcMKxP6mq/Z4a9vxC1uY8JOodlzoOzftWRxKRpr+OCEEFV2PbPM1lzm4spSj6J4mU6ZOTZ81BacSmilJOhnf2+874qjersniXaOmhItUSOH3EHLHYZ8mqV3YevOzGZY4ycqFXJH71F+OVo2fKow6MapJxdp3MZUAjqDtQ5l9MJ5KjuS9HDbW+DGpZx+pjk1R4zeH8KYtWkyHBB5461RuTO82t59KZ+UHpQXM0bMhBJYDBJ32ppJLDLpt63oFlZqbd2eYKwGQqqSzH68gKmtkUHL6iScb8vvUEd2VGlSMHpQ/jmjB0jesuJKzz2liNRikcxAlWVV+UKCF/mjnZpGgmit2SLWV16xl261lvdkRElVweR61Z4covrW5iSMGaNNab88c6fgh/wAit7NTjnw3w6a1t+IW5iScENJBJIF9buCRyPmoYeD2MfGo7u2jSMFP+2OQY827YA2wKx7S5jScNMgZVO6nkfrWrHxC3juVU3L3FrC2IVZcZU0mmOHDfZa4nbsl2v5iOGGcryHiql9ctwqwN67nJbRGind2/sO5p+JXBa9bSixKMDQvIVQ43ZXXE7W2FuvqNFIQV5YBHPwNt655Sd4zt5PJcWor3HE7Bfw1xZW1r+KMDepoiIKyt+rJGDjHI9zWg3EbYWEMVndSmUHMjTxlmckDJz0x42/vgw8MlaZI7ZkudQB9SLJQHG41ctutWDbtA7RyFdanBA36c810tI8zaN24WGaBRDePI5dVYZATrvgCo52tVdhbKWHLUTt9sVnB2ZQCfaOg2H7VIrVN9G90sasDJpVVll6ClUnXZtSdFHe5A9wqT8YCMA1yKcR251MvER/VXac2HUi8BHOnF5jrXMDiI/qrSsVeeFbhjhCcKp/V/wAUm0lrNTDp4jTa4Zj2Hc1We6B3Yg4OQO1Q3tzDZY/ESCNpgSrBS23I7Vz54jrLaW2UFjk42FYl+XbN3Kh4i5xe8L3Cx8gB+9UJdOA6MMiql1IWk1kkagDv9KhWQ6xnemTZqpdSBCij5qWsQkhWyxqi9yXOBhcCoi7A6gceaAwuy3Bc9TQwTAsBKDp79qhQtIcsw8k7VdgurNrYklF0gAj61l0zalfZnGVgxwcjzR3E5mEYSLSVXHt61YYQKgLtLpP62eo44LOebQLqVn6DUN/4oVD8O8JY57r/AKfLbsqBPmyw326VVguHtplZptBP6VOTg/StLRbNB+HdCqdiT36miuJ7ezQSSINKkAKqjNZ82b/EvtmW09u07m31FT8qkFiBWg4urj01kYnQoCkDktU5OI3qq88MccEJYBNSjJ8Dv5qtPdTX35jzsJA+FiQEKBjmDTc0wm4j/pvGCQ4mEjEgYIxzoZuJG3RVgbVKeYPID7Vjk3h1GWeU68Bssd8cqkhix0rP4knrKV8p1PjKw234zK6oIQV9uG1cs+AKojLMWYkknJJ60Ma4qYDFJ0l6I917HUbc9+1JmxTnahZWAyykA8jU3WlEiNiaVJqVZGY1LJ709Ma7jlFqbvXUcHnkfg8K7EIzLtnbfO/71zCKzuERSzMcAAbmuk4Jwi+iKTmRkVvmi05yPOdgfpmp8i1FeFtVuFD4lnL3cMGQfRj6cwW3wfsB+9YpzW/x7hsVkjTFp5pJGwXdhhTz7b9qwCa1GZ0Y5N8npNG+tBE2AeSsf9jRpAxbTg57CgtoJbh8RJqAOCzD2r9avqxtcvNceox2B6D6dTSrr0OVvsiXh0xGdgMdahxpVg4zpH2o5uIs+QASOmTgftVN5WkxqOcchSSr7BuV6L0gR7QmBQSF92+fr/FZ1XeGzYl9LVp1bqfP/NS33DyIzcwL7R86D9PkeKa6eCa1ajNJOAMnA6ZpvNLNKtmCRbiZE0rIQP5FS299LFIplAnVeSybgeR5qsAScAVat7N5SCRtRg9YbXUt8Ss8KOMHRpyugkfz051qcM4YpCsI2LjnuMGpLLh2Me2up4VYgDlWsE3piXfDwqcqpQWqs7AuF07nbpXY3tmCh2rl7mIwz7bdahyvCnHn2WVsIFgaUMGCHfJ3+mOtVTOVV1jQKCfuKdp2KjAwe9V2JJJNcztnTSn/AFIyaHNEaA1kyImlQE0q0BkZpiabNCTXacpr/DbBOMJIUDFEZgD0Pf8Amutbiz+qfUjQj5VCjGK4Thk6wcQhkdtKhtz9q6NJop3IhmSVuZ0sDiocu7p2/FUNNUXOL/Db8VX8TYcQSSULvBIcA/8Aj2rCg4HNZXAfi0A0KdohIDrPkjkO9aoeRWIVyM1U4pN+GBkkJkKrsGPM9KJ5G+kPk+PMvybKHEeLN6jwRQxxiM6QqjCrjbYf3NZEkjSOXc5Jo7y6a7mEjIiHGMKNj5qDNXSOKn2ETSzQ5pUzIWa1bHjBiGi7DSDOzjmPr3rJCsx2BqxFaO+NjQ1o08NK9sob78/hrxu/60U4z5x0Pis2K1ldsFSuOhFaNpw9gwYZBHUc63IbKSVlL6WwO2//AKoSwG9Mez4YTgla27Th2MbVqW9iAo2rSgtAByrZko21kB0rd4fbALyoYoB2rTtUwBQBTu4PacCuL40micV6DdAaDntXAfEJ/wDkAVz83orxmSTQk0JNCTXHhcdqA0+abNMAGpU7cqVaEYhFCa1GsD0BqJrBvNd2HMZ1EkjRyLIhwyHIPY1dNgexpCwJowRtW3Eba6C6HCSH9DHBz/es3jcxEYjz/wBxsnboP+aji4bqkVWXIJGR461avrT1rosF+UafrU1xJVqOi+ernGYGCelEsTnkK2k4f/pq1Hw/xVcOcwUtHPOrUPD8kZFb0fDhttV6GwUdKeCMODhn+mtKDhwGNq2YrMdquR2oHSmBm29gBjatGKyVl0suxq5HAB0q1FF4oApcPRgWt7gATR8jn516MP71qRxADlVe9tZGVLi2A/EQHKgnAYdQftV2CRJoUlT5XUEUl/QxIgHSrUW1QVPDTEQ3h9hrz/4iyLgGvQbsew1wPxMuJR9ahy/4lOMwC1ImhNMa5cLhZpZoKcUDHJpUzcqVMR0ZsR2qNrEdq3zB4oTb+K9A5TA/AA9KcWAHSt0W/in/AA47UAYJtAjoAPcxqX8EO1XLVPxPEp3UExwjQp7nrV/0PFJAzGWzA6VPHaeK1FgHajEIpiKCW2/KrKW+OlWljGeVSqlAEMcOOlTpEKNVxRigYljAqVcCmFEBQAQao4Y0hZ0RsB2LhM8u+Pvv96Kq9zGTc2kqD3rJpJ/0kHNJgXKngNQVLCcUwGufkNcB8Tn89R5rvLo+01wPxKfz1+tQ5fRvjMI0BojTVylxgKMAUOaWaYCalQsaVPAPTSoptAqRhg0NdyOYHQKqXsjrpgtgGnk5f6R3NHeXgtykaL6k8nyRjr/xTWdqYNUsza55Pnbp9B4of9ASW1stvAIwcnmT3NSECjJoDTEMMU9NSoAJakBqIVIKAJBTihXlRCgA1qQVEOdSKaACxQs2llGCdRxsOW2d6OhZVLhyPcAQD2Bxn/YUAEDtUsdQE7VJGaAAuj7TXB/EZHqj613N0fbXA/EhxOKhyejcezGJoc0JNNmufCweabNDmkGowY7UqEnNKmB6u4qjc3IjYRRL6k7fKg/3PYU0txc3p02AMcXWd15/+I6/WpLe1itlIQEs3zOxyzHzXYnpzsjtbX0iZpiHnb5m7DsPFWaXSmzimIROKEmkTQ0xCzRChp6ADFGKjFSCgAxRChFODQAVSCoxRA0AS5pjTZpZoAYmjhNQsdqkhNAA3XymuA+JjicV31yfYa4H4oH5yt5qN+jcmDmm1UOaYmoYVDzSoM0QNAx6VKlQB65IKgalSrrkgwc0JNKlWhAmhpUqAHFFSpUCEOdSqaVKgA6elSoAcUQpUqAHzT9KVKgATyqSKlSoYEd0faa4P4nPvH1pUqjRqTniKEilSqKLCxTgUqVIBzSpUqAP/9k=\"/></div><div class=\"TNSLVN-item\"><img 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GWcuZhADr/tje6vr2RTK6vUsv4LDfKNrpBQa1xqyf/t/Zcdn6pm6nPHpsX487+DtbTWe5pS4Gk6pqgdPmSOw43OHLm7pZB3q/wAvt/RdNpGmYmk4xhxQbcbfI78zz6koX1PpmI3A07HxGmxCwNv19f5q1YQBCR6qaH6IIcpG6UjuySbZ9ElJ45wHX1Ca8jb2Uj20SAQe/CivkH+q9LgbfflP817mhhedo7JrnE8lDgO6migkRz7IVVnlPJAd8t7VHxXTupEBzzyE5sz2NoOsehQc4kdgkwbjRJAHdGItz3GufoF3vhDw/wCQwZ+W0b3D5GkflWb4R8P/ABMwzckXAw/ID+o+q74UBQ6BZ6rUmkLWfqui4eqsHxLCJW/llZw8ffv9CtG+ECVybec5On5/hjWWZjSZIC6hJXB9L/uF6Fj5DcjHjmjILZGhwpZXizCkztAnZC0ulj/EYPWuv14tUvCGrCbBhwpz+K1pDXHuB29qUHTjn7rzDVZGyazlmBm2BryGtqqXpGVKyDFklldsY1p+b+i8ra57XzE889T3TFXVf9PscSYWdNIGOa+Ty9pbfFWfsb6ey6yLHggJMMMUZIq42Bv9Fh+DMY43h6JxaQ6dxkPN2OgP7BbtmrRaYcaKA9kt3HJRFV2QSpEFLj1SsnolDfCV2hfqh/UqR1gFJAjjhJSeNu7C+PdMd169Eu/B+X+SF12pehxFxDh1AIH7pv3/AOEq+XgfskPlPBUgIq+/unOpoqh/VAniky7IocoRc80V0Hhnw/JqkolmaW4rDyf4voqui6Lk6plhkUZ8ph/Ef2C9OxIIsPGjxom7WsFClnrrDJp8MUePC2GJgaxooAJ/8kbRXJ0NNjvSNu6UkiK+6iDqIILbB6hedahpcum63PG9p+Ent0Lux9vqF6OTx0tZPibAdqOiSxwkiaP8SPnqR2/a1Siuc1vWZMzSY8Vsfl7K3EO/NQofZczISMcjuQrEhlGMwPdZclhYkuXqEGPE0Oc94FHpXe/alqsPTsFjWYGM0NAAiZQAr9IU6BG3hopvYV0CD3BotxpYdDtoJ6JECk0HcPZOoUpGUe3KI3Dsnc2ndQlG9uRymnduFEUpAEaF8UpG9eSkiW2ElJ4xu4THA3x2CJ5Nd0uC2r6mivQ4mWQLBCRIu3c/ToiRRH+E00evA91ILHYLQ0rAydSyGYuO0kOcC70b6m1Vxonz5DImBxc8hrfdepaLo8GkYIhYA6Z4uWT+JZtwyatadgwabhtxcdvyDk3+o+qmPJvoieOvKLfcLjbrpJhcjulbvZGx3SIB6IIOs8FEFvYo9O6p6nNk42J52LjfEua4b4wTu2+oHc+ykubvdNcNzSDZBFH6Ll8jxdhiLbj4uQck8eWW0Gn69+U6KTxTmYwe1sWKOoD6a8/bn+dJyjY5bOx34mq5EEhG2NxA5tdf4S0U4kPx+SAZ52gxtLeY2/5K5zDxZs7xG2LUHOMjnl024UTQsj+S9CEgDaDdo7V0CeqOZ+pDYQsHqLTfMBHVOFUstFwOiXZEBE0Eo3p2SLqaaHKTjfRIfUKRrS53UUnA0OUHEDki0mvDuQpHh3qUk0vbX5gkpPF7PJ/f3SDtrgaB9R2SSXocTDZ57X6p0YBdVWTwEklJ33hTQPgI/i82Iee8AsB52D1+q6YStJ4SSXn7vrrIIcDRtG+OOUklkkKvpSW4Dm+UklGCNxS53c0kkpIjjY4m84Y8XmXe/YN1+t9UZ5hBjyTuaXeW0u2tFk12pJJIc74RwvNE2qStqSR7mxjggDuR9+P/ANXTUP8AhJJN+ifALQRRopvljtYSSQRp46O/dEb+5BSSQg3V1BCLXM7kJJKR3B9CjtaR0SSSgDAEkkkp/9k=\"/></div><div 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U7qwCKWkN2uI5HsqJYKO4Y6oKS4ts7T/AFUJCWiwR/RWPaTbskFVloc2icoCyY7NWUJtBaSPzADhCo6pvb+X7pbDRt2FPzG0QKpVvkbRNLKlTLO4YVVgE7c/KRkABIxSp304kYtUSe7Pqq+wS4F1fVV7iR88KcZJG3gIiDCXSVxhD2m7HVSditpHyh4zg2EFL43UHtH2UNjZHgNBA6nsr3EOuicKJ2iKv2jyUGdzXAuo8dVo0+oOwt5KGsAjNHcqGNIkNCiVR1NNMH4sYz8q9uX2RhckB8Mgr9V1Yb2tx90FuAb/AIJbc2pPFi+FEHc35UCcKIIIKchL2D4ypHDdvVVSTNjBG77IaGWQQCqQdsoB6qtupomm38qfmA/mar1psScaNg0pHbIzIqlW4Ne2mnJ7psL2Dbz9lMDDLFA2FU+Bjj291YX1yKVcknZBS/TtaLaShZ5p3CwTZQtTimunvDM1f3WR8m95HRCFlonOG0iuqgSeav4QhRFjReKo0lW0EHnqhCogXV+XKjLJWW9kIQUmQkAE0SoFxyhCCUMm3nlN0oEovKEINIBcWlgJBC6AdtjAGCEIQXtJfHYynE5pu6sIQis+snMY2t/M7hZIo3Odk25CFueMX1Y7TkdVWWyAnqhCaYi15v1AghXx6kbqePuhC1mxn6JnCvSbWZ0uCCCEIWPrbBqCS72tCELrGK//2Q==\"/></div>\n    </div>\n</div>\n\n<style>\n    .VQBHBM-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .VQBHBM-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .VQBHBM-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    .VQBHBM-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: 31\n</div>\n<div>\nNon-outliers range: 0.26 to 0.47\n</div>\n<div class=\"VQBHBM-container\">\n    <div class=\"VQBHBM-row\">\n        <h5 class=\"VQBHBM-item\">Mean Red Relative Intensity</h5>\n        <div class=\"VQBHBM-item\">0.2</div><div class=\"VQBHBM-item\">0.61</div><div class=\"VQBHBM-item\">0.62</div><div class=\"VQBHBM-item\">0.65</div>\n    </div>\n    <div class=\"VQBHBM-row\">\n        <h5 class=\"VQBHBM-item\">Image</h5>\n        <div class=\"VQBHBM-item\"><img src=\"data:image/png;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA0JCgsKCA0LCgsODg0PEyAVExISEyccHhcgLikxMC4pLSwzOko+MzZGNywtQFdBRkxOUlNSMj5aYVpQYEpRUk//2wBDAQ4ODhMREyYVFSZPNS01T09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT0//wAARCADIAMgDASIAAhEBAxEB/8QAGwAAAgMBAQEAAAAAAAAAAAAAAAIBAwQFBgf/xAA0EAABBAECBAMHAwUAAwAAAAABAAIDEQQSIQUxQVETImEUQnGBkaGxMsHwBiNSYtEVJEP/xAAZAQADAQEBAAAAAAAAAAAAAAAAAQIDBAX/xAAiEQEBAQEAAgIDAQADAAAAAAAAARECITEDEiJBUQQyYXH/2gAMAwEAAhEDEQA/APnFoUIXSgwV8T9KoCkFKh1IcnSOaudm+XmuMHFNqKj6HrZNkl3VZi8uKS0K5MTThTZ7pLQCqTiwPI6pmzOB5lVWi0eA1x5cjequbxB46rngqUrzKNdaPiR6rSziTT1XBtAJ7qLwWvStz2H3lazMaeq8uHuvYroDEyYo455SBCTu6/qFN5k9qm307pzIWEB72tvueaIuJYsr3MZJuDW4q/guRgTNycg4svhtilJDC4foceVdt0smPNjyeFNJGxzH1RG9qJNV4ei1BQXLnxCWOPecPHQdQVWOItDiHGiDRTwOq1yYFc1mcw+8r2ZjD1CWBstCobkNPUIRg14ClICtDKRQWspq6QmKhUQClCEBKEIQSUKEI0JU2lUp6SVNpVKNLDWi0tqxgpviuALQaruUrc8nmnjbycefNo7+vwWqeV8zKfI43ubO1/BYA5xN3R7hdrLwsHDyJI5st0pjY0tbG29ZIvn0AWVv29rkxgdH4LIXGRp8QXQ5haCyTIrVIXEndztzSxMY+WcCNrnOvYAL008MEkME2NpYQwNla07tcFMgtZ8uKTEyYGtPiRytFPAoFcfiTh7Y50QeGurn35Fej4dw6Ti8bm/+RbFJjm4WSHY96XFzIoDlEz5LnOqnFrLN/hVbhSa5oleOpWnFdkZEzYoRqe76D1PorW+yRkgQeOOjnuLfsFS6eQNcyMiNjtqYADXYnmefVH2P6uscqJ+O7Dx3h02OHSmWgBJQ8wB5mgNh8ULzztkImFZUFyUlJaFeBJQoClVoCEISIKUIQEIUqEgaNhkeGAgE9SaAWluG4ivO53+rdkkLhFE6UF2o7CuV9D9ifkqC5xJJcbO/NTdvpXiOgzEYGAPxpi6tzrIv5aU8WHizPaARGAfNrnG47ctlzbPc/VQl9b/Rs/jvP4Xw9sZPjBzgL0snDtvp6hceYgyHS2mjYAKYz4cBAG793bb0OX/fp2XS4Lw+LiftUcmQ2J8URlYT71e6jNG4xsx4XcOfP41TMeAIq5t7qYo3SvDneUHkGhdnh8PAxwyJspfJll2t55BgHQ+ixZmZjDMJjiLMWV1iXwyKH+vp3S6/6EqrCjDsuvFljiOxe0WQF1MVsLXTMxmudGBXiu/+hSRQzRF78UhumiWOFnSevwK6MEk80bfLG6NpseGKpKU7HPwgGzlzvd5DsvOhzmOLXCnNNEeq9XK6MSk1VHzLznGI2tzDLGzTHJuO19aU6cinxlBkWclRaYWOdaFWhBaakUm0o0rbUIpFJw1SGo0tV0ppPpRpRo0lIpPpRSBqukzGF7g0A+tC6HdNpV8TfChMvvHYb/zr+Ci3BPKrJfb9AbpDNgOf82pU0mIN2VCJMPUIonYczyUqYmF8rWNqydrNIDQweNO2KMBupwYATy6Va7bf6T4keIz4kboi6Fodr1003yF91xY8Z88T5I43ObGNTyOTQma6YRua57xGPM4F1A/9KiKoyzJgyT4jXEP3jnIIIdvyHor+LSyzYGEZzb2trp/iOy5pMk0tnU+R5+JJXVzsTTwiN12YSGkg0D0P3ofVF/QiOD5r45W24ufH+lp31M6t+XNekZD4crJsZ+tsnme0ChRXho5HRSNkZ+phsL2XDcp8d4AYHFxDmi/0giyPiFNmU5fB2xB8DwHgl0hNdlz8sCOOXRQcxhc0m62716Lsw8Pf40cYNEucfgsU8ZY8+Y+f9XrSinHlnz4sjDeKGkbN09viK3+RQ7Gx5ADjyOsj9Nhx58zyPypLPjOZmywRsJLXGmt3odPyFa2CPHjbLMNbiRpHQfL3vx6nkrvJazT4zoQHFzHscSAWn8johTNNJKAHuJA5BCuc+PJWunk8DzccEmLW3uzdYDGWmiCK7r6SWtKw5nDsfJB8WIE/5DYrk5/0X9tevg/jwmkIpdjiHB5ccl8Vvj+4XLLSOi3ncvph1xZfKsBFJ9JUUqlRYWkFqZSqlIrGanAch1PYJ53BxAbYAHI9P4KUs0tO4JoWaP0VZRLtO+IQhLSsUEKi1XpTMYSfKNwppM12lpGkG+vUIu54VL5NA6VkoEZs6gdJOxrutHGeKP4rO2SSGGMtsf2hV/8AVU7Kd7J7O0N03ZOkBxPx5lZtJJoCyUpz/VXqN3CMcl7sihbSGRX1eevyG/0W3DyBNkZeM4PdjMbpaPcY0WDZHK+66PD+FBnB9bpdElOA0N8wcdr9SP2XEmyWYrW4zIf7TacGu97/AGd3NchyCj3Veora2DhxJlHjSmi2gW2O4JG3x59u6s4ZxCQZQe9+7DqYByA94D5b/IpeOYwizGyte58c7Q9pP8+yxYspgyGSAA0aIPUHYj6FPNmjcr6WyRviwyt5He+4pcbiMoZKdLbJJoJuFZhbjtgkc10jHGMi+3IrQYWmKNzqc4XZpT7g/bzHF5XwNYdA1yCiaFUOh7/PZcUuLjbiST1K9XxXEE2BPdaoxqaT6b/i15NV8eYOghCFaX1Yx1yVbmbLU5I4LzMdznyRg3drz/FMBrHmaMUOoXqJG9lzsyLxGEdSE+beanrmWPJuYFS5tnZa5mlkhaehpVMifK+mC+5OwHxK6ZXJZ5xSI9rKujxaaJJ3aGXuDdkfz5q8sji2LtbvQVR/nU/Tqs0lu3JtPS+sUyaS46G0LJvv8uiSirmjdM5m2yqdFZvlmQrHNooDNlc6TiukJywhKrlSVbOF4vtGTbq8OPzOJ5fz/hWVeh4O32bh5ydmOY4EahYeSeXpsjq5FcTafjWd7HithxJTZoh7SOrd627H6n0XlyL59Vpy53ZE7nk2LOn4fzdUI55yH11tdvJLcv8Aphs0l+JCQGmgN7DT8q/C8+eRXe/p+VkvjcOmDdEzS4Emt65fTf5LjzwPhnfBIKex2k/FHMy2Hbs16LBycbh2rIyGlxf4TbA/Q0t5/UL0MEsc0DTE9sjHCw5vKl4ji7/OyLU1xZsa6aQG/TYlW8C4t7DK6LIe72dw7XpP/Fn9d51e+cel4lH4uNKxlAvaWi/UUvAr2PGs7GjwHhs7HvkZ5Gtdub67Lx6fxwdIQhCtL64SkcVll4ljRkgl7iOgYd0Q5TchpLY3trnqFLz8rt2HeVmlqjfJJlOzXu04/hxN6vf5j9FQcRt68iR07+7zsPgOSMn7G1yuJYjDMZnzMZHp/wAt3HsFyZMotHhw0G9wKB9a/c7rs8aaHY/LlvsvOkbro48xyfL+NWNkN7oc+1VSCq+rPThwtXB1hZlIeQl9RKscQmYdlRqKZj6Kf1OVY8Kk7FWueCFU42r5T0txYvGyGMIJbdursurxufwIosJjmnQzzgH3jzP7D0WHC/tRPkeLYW26tjpHY+p/AWWWQyyukdzcbTnmn/x5/wDSKFKhas1mLO/Fyo54zTmOv5db9F1v6ixbzMfIicHePTRvzIqj8wQuIVrdxCWcYkWT5mQP2cP1EbbfZTZd2NOb4yqeIOLsyS+h226Hf91mK28Xi8HiMrWghtjTZ6VX7LEeaOfUO+0IQhAQUIQkb6oXnuUjnb81BKpe6l570rBI6uqzyv2Tudsss76CE2MPESHQP9QvPuC7ec+43LjuC6vhn4uD/RfyV0lIVhCgha4w0lbKKT0opPBpCFCcqK9UYNLv3UsaXvDRzP2RS0Y8XlMrrDd2mu1Wftt80XxFc+aXJdWlgBA0it+Y6LPaeR2uRzyANRuklp8zILdqSVFoJSqixKgoUIVHQzrn4fjZOouLRof0o/wLnLo4X/s4GTjHnGwzNJ3oDc1/Oq5yjnx4Vf6gqFKhUEIQUKTfTXlUPcme5UucuB6hZHVyKxzv9VbK5YZnpyeUVly3+Wu6xEK+Z2p3wVJXbxMjyfn63tWQlKtpKQrY6rRSfSopGHpKUUmKUlPFIK0yyCPhsETGUZS573XzpxAH2WUAucGjmTS0cRk1TNjAYGxNDQG8h1/JSvvF8zxrJahBUKgFCEIUEFBUEpBr4ZKY86NvNsv9p7boOa7aj6f8VWbjuxsqSJzSADbfgeSoXU4qTlYOJxDm5w8KUgH9Q7lTfFX+nKKhCEygKFCFOqfRXlUvOydxVEjvVcOPRtZ5nc1hmdTSVoldvzWWU6jS1452sfl7+vNrOW2kIVxCrcF1vI20hUKSlKDwJHJkptUc5VlKU5CUhC8W4TNU+su0tiGsm/l+6zyu1yOeSTZuyt0cXhcNknLd5PI0nkRy2+/0WGlM92r/AFIRQnIUEJkRClKUHAUpQSoQaUanaS3UdJNkXtaVCVOBChCVqpEoQELO1Uj37is0rldIeyySu2XLHbazynmqO57ppHealHIUun4p+3D/AKet/EhVbhatKQhbVySKS1QQri1KWpapTSghW6VGnZPTUkJSFcWqzGg8XJYytrs8uQRpxu4tKyHgXDOHsYQ/SciQnoTe35+y4dLfnP8AHynvDaGzfpsspalzfCr7UkKCFYQq3KhhCkKZ1UkKDQVCCoU0woUlQUaeBCLQFFqpDBClgsoWV6azl7eU1awyv25rRO/Y7rBI6zSjmNrciALdamkw2AQV0zxHnd37daQhKQrKRVppV6VGkK2kae6WninSgsVwamEYRp4y+HfRdHB4fP7BlZjWAMZGQHEdetfj5qoNFgDmurl8Sbj8Hbw5sdSFgDyKoDr8ylarmPOOjA2KqfsrZZAscknqqm0Fe5UvdaHO3S81cgLfdQU1KCjAVQmKUlTacBUFBKFFq5ApCKVjG2s+qvmeTxMtCvjYhYWunnnw7kr/AFVAFvu7AQhbcRh81sh0IQtnDEhTSEIMdUVaEJKhtgldIGhCEKU+1iORr6DtJuj1WTIyzLI555uNmkIVSQayPkJ6qsklCFrJCtRSKQhV6IpKRxQhZ9VchSVG6ELOrgpSAhCiqSButMTUIWPVbfHPLUwUhCFi6Y//2Q==\"/></div><div class=\"VQBHBM-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    .YMEPRK-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .YMEPRK-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .YMEPRK-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    .YMEPRK-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: 68\n</div>\n<div>\nNon-outliers range: 0.3 to 0.36\n</div>\n<div class=\"YMEPRK-container\">\n    <div class=\"YMEPRK-row\">\n        <h5 class=\"YMEPRK-item\">Mean Green Relative Intensity</h5>\n        <div class=\"YMEPRK-item\">0.16</div><div class=\"YMEPRK-item\">0.2</div><div class=\"YMEPRK-item\">0.21</div><div class=\"YMEPRK-item\">0.38</div><div class=\"YMEPRK-item\">0.42</div><div class=\"YMEPRK-item\">0.39</div>\n    </div>\n    <div class=\"YMEPRK-row\">\n        <h5 class=\"YMEPRK-item\">Image</h5>\n        <div class=\"YMEPRK-item\"><img src=\"data:image/png;base64,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0dpVUDVyJxmrggiudNoz2JxzFH8KY78g3evn32pt2X7V3AR1CyASszbAAjlX0iaBJVwRk14z7d2jW9nBc6A6h/DYnsdx+9Vi09hx2nTMex+EkjdSzLhgVbvXoOVeW4ZBJKWMPnCkEr6d69bIuGPvXRDsswdQVM9K7VTFTzrhq1cNAxQ7VKhqUpj18lvHKpMoAUb0tJw5UbKE7Y2571oYwdzypXiV7FZwePISSNgoO7nsK8+M5LSOd40xRrGVZCVO2cZ9Ky7niCPK9rw4PfXIHm8M+RD6tR4bPiPGU8Xi8rW1uCcWkJKs4/8Adv2rbtrS2tLdYLeBI4xsAAB+Zqjy0D4Yr+nnLTh9zdee/YTtjAT/AMaD0HX3NadpE1tc+C2ACNsVpKixggDG++OlKX2oSRT7DQ2D60yyJukTlB1bZaGYQXDwkbHzAn1qt3MkkLKG3AztVr9FeESg5ZeR9DSFogluNLnpnFPGN+QkpNeIWN57ltz033rO+0cVx/02KyikwLydYj/n9q14VS2uZImJAY5VjSUqDiH2qggzqisITKw32dth77b0spb10UxR9vtBmt4o/h7a3IEcaBUHtV2llhgKTqWzyNDlX4W9CkZRTsfStKdVuLQuCCNO1O6VEk222ZNrH4k2CDy610wCJ842zg5p2zjiMUb58y0S8jCyAlfI+xJrOflQVHxsjAG0CsQcjY0ql28L+HMPrR7UnS9tIvmHyn0oc0IlVo3HnX5T3FIqumPbq0OQyBx5TkUh9obaO84JdQzhimnUdPMad9vyoMAliXxYyfL8y1oQXKSqCG81LKHF2h4ZLZ8xs72bxoEjPliYJEAuMqT1717S7j0yHtWivCeHxTePDZQJKDkOq4OaHdRBl5VSOS2XcrMfGKlXcaTVM10rYyZyqmrVw1glDUrpqVjHo+JcTXh8SExtPM5xHAnzN39h60KxsZWkN7xLD3rbKg3WEdl9e5pyKEi9luJCHMgAQ43RQOR9zmmPmAViMkb479a8q66J2cCkbbDB5Dr2q4AIBIJAyCTzx/MVwtnSVG5H69KtjK7kkHBwPT+CgA4wOnBIGRjbqRSfE112zMM7Ybf9ad3XVkgYOc1WWNGj0Nkjl9DTRlTFmrRgmWabyasqAFAFFijmtpUllBCg+Y+nWtGCzjt1UpgHl9arxK5tra0Z7lwgbYZ6554HWul5bdI544n37JfNF8C1zI48NEJLY5YpL7LqTaT3rhUlvJjIe+nHlH5Z/OlAsvGrBbe4DQWYILHOHkx6dBTkMkdszxwACPGFHbA2oKDaoo5qC/oZgtxxJhjWnSin/RyNG39OTl6UvYTgSsJGw7bj60bia67dWA+U86f/AEokU/FyQrGjLqkhO6Nuveuy3ssqNG0ecn8vWgWFwyTgyN5etaNwirNFODkEaW/3oy0wR3HQCJ2khEqDMsXzeopgYuI47hc5B3FdhhSOZmX8Q3FUiUQXDQnZHOpN6m3bKRTSKSKFPjp8p+YVDaBl8WE4bn70VUKFon3Vt19PSg27NBM8DnIO6H0rWx2gkdyCAkmzAb1WbSVPamHgSRSSN6QmDRBkOdJ5GtGmNbRnXSYbI5UtTrq2nDcu9ItsxFdMGVg7RK5XarmnHIalcNSiY9U91Gc5fDE59v5miCRAQzSKoO+PT+GlJbABco3PY560A2d03nAyAdx2zXAscH7OP5Mi9GsskZJXUCQdjnv1H51ctgas7g8vfpWIltNqBIIxsQKkguUcAawScb9/5mt8K9MHzP2jaY+QFQNu/aqgljuwwew61gT8Wa2BV5AxP4Rucj0oP+tvh4ly0kMGdol2LDrmh8JVTtW9IeueOPLc/CcJhE8x+Z2+RfX6d/SrWfCEgmF1xOX4q7ODqb5Y+vloFsrWkYjtYCiDc46+pNSM3MknnBIO306U6x0icvyF1FHJTIZGwcAnOKjwmKFZM5DHGO1WlB1BYzk/4NFmfW8NudjjH5710XRy1YytqskCN8jgZB70q8ziKSGTJYHatSPaNQV5c81m+E0t/KrEVCE+Tdl5x4pUCiTS+lgAHXY+tP25a4tGgZgGBx9aRu0aGFDglkY4xTdmy+OGHKVc/WnybjYuPUqJG7GMSAkMgII71a5Xx4A6Z1J5hV8CK5ZSfJKMqPWuW7aXaM7Y5e1RetoulfZEf4iAMuQw3+tR/vodQADjp2Nc8kFycHyS/oahIjnByCr7UeujBLWYyRb7MuxrsqB1wcGlbgtbTCVF8j/N6Gm1OoZzkEUstbGi70JeBiMq2/Y1j3yGPLAV6N18tZl7DrRhiqQkOjCWYEVcODWddu0ErAjaqR3XrXSmVRrcxUpWGcHG9SnCe5PILjYNgZ7fzFEDeuSD9d/+DWU1+SSgXTnBGf0o0l4kluYy7IzpjKncHuPz/SvNeKRyLLF9hru8trKMvcyrFk5A5lvp71nk8Q4uv3KGztSch3B8R+uVHTrVeG21lFKJpA003NppjqYH6/zatN72IAanzkYOOXrR4yXQ/OC6OWPDLOwj+6TLqdRkYBmzjH059KIY1RyqLkVI7qIpqJx3/agm9gBJDE52IHLahU2LKcXtjaquk6sf2kY55oMxSKEtpAyMb8weg/zSz8UUlQiAFu9KSTS3rFVYYY5GPeqxxy9kZZI1SLW667jUckZ1bVIWL8UIUZQZANOwxeBasHwQN/523pXhkYaSQkcid/Wmc7TYFGmkaYA1ZLFgeeOxrOmynFY3Q/MMEH+dq1SpxyAA5Z7f/vWsu7jX46FvNqGx+lRxdstl0kGvo2e2Zc7jBH03pK0YiLUMkxtt7VrT7xkKuDggZH5Vj2bYnKnYNsfWrY3cGTyKpJj1yrPAskSjUnmX1HWowYxpcR42GffapbsRGQScocY9K7a4jeS3xsvy+1Ix1+yTILiDK4yBkVIgLm3xgZGx9DXYB4cjRkbfhHpVdPw9znkknLfrS36Go6QJUaGTmOZ7mpav5fDPzJsQeeK7IAk6uMjVsf2qkv3UizDGxwwo96B1sabGB3pSZMA5prIKhu9DnXKnvSrsqeV41Zhoy4FeZAZXIr3tzEHQqw2IryPErUwudIO9dEXoeLFknKMBmpSsnzHFSqWOfTjaAopbAbqO5xt/iqJYKSQWJOrmdvcj9K5FfbYK7g5OeWc5onxyksgGVY4A7CuXzOHwZU2ClX8+5AJHUZqPYaVYIc9yeo/goz3iZAAznfPX+c6A9+qKrBc/gY+lMnMVqAMQPjSwIBGrb9K7NaCKMtqx1zVoeI5I8RQE5bde1L3MrXGlFDYXbHXFOuV7EfFrQARanCDJydvetm2tRbRFjjbc+3LeqWlskIBkA1E4/Onzgrkg5Ax67VPLmvSKYsVbYjfuEtyBuxOjPpzrljGRBkEDVv74pW+fx5xEpG/l+talsoEIXTuANzyzQmuMEgwfKbZA2lAMZ9Kz+IEpcwP12yBWiynO+wxy9+VZl84WaI4yQetLi+w+X6mm2GA3JJXn+tYoQxtIw5xPn6VtaiV8uAOY9qzXCNezxlhpddxT4XViZVdBQHF+42CuuQKpcMYbiOYE4yFNVMuIIZgMmNsH25UzdL41oSAOWc0z0zR2iXA1JqTOsDaqyabi2UDOcZ+tSyl8W2VsgEDSarEzRXLRtybzA1OqZS7Ry3fxrch8gjIPpXYz4qNG2+jynPWuNm3uw2Rol225A1ZjonWXA0ts2O9Exy3JBMLE5XkfSiyc8Gl7kNEwmTPl2PqKOjiQBhuDyoP9jRfoSvGCDPrWJxOISRF69Bdxa1K0hPb5iKkbEVSD0FWmeImjAJOeVSn+J2Zh+UczUqhZM92eHoHOD8w2XHrVobSNI1YITg+YN/PSpUrj5yOfhEq1kmpgcsFOdv52NWazjkjKNttu38+tSpW+SRuESJw6GIjB1ahqB6Ht+1Mi2iDBVXGRj98/4qVKznJmUIrpFsg+YA7rnVjr0/npUdyFIAxtkD27fpUqUI7dBk6Rm2C+LO0hXLc8etarEhSQcLn+fvUqVXLuVCYtRsoGDDyknTkHPSszioZfCkGB5unQipUrY/ubJ9DQABAGM/t1pFk08TjZgBqG47451KlHH9mgT+iYNIvNcW7ndTqFGtJNVqVbJxt71KlPPoWH2BQ/6e+kiK4Rt1o9yHKJIqjKbnPapUpJdoePTQS4USw+QDuPShgpNDuuzc8dCKlSh6GrZxW1wtGx8wOk5odmWTVCdzH/AIqVKavQv9GHOob86XmUlQcVKlImWMPiltrjJ7VKlSuhdBP/2Q==\"/></div><div 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FbHsYxamxCERWrKozLgQhEQmyu/4HpllSE79Sw3eyjP8YQhCrH3k9YQiCwODiPrMIQBok5hCBJz2hCEQWEnOO8IRpSDn6yCx6CEIxQDjqZO4QhESM4+kIQgb/9k=\"/></div>\n    </div>\n</div>\n\n<style>\n    .OPFJCU-container {\n        overflow-x: auto;\n        display: flex;\n        flex-direction: column;\n        gap: 10px;\n    }\n    .OPFJCU-row {\n      display: flex;\n      flex-direction: row;\n      align-items: center;\n      gap: 10px;\n    }\n    .OPFJCU-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    .OPFJCU-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: 34\n</div>\n<div>\nNon-outliers range: 0.22 to 0.39\n</div>\n<div class=\"OPFJCU-container\">\n    <div class=\"OPFJCU-row\">\n        <h5 class=\"OPFJCU-item\">Mean Blue Relative Intensity</h5>\n        <div class=\"OPFJCU-item\">0.12</div><div class=\"OPFJCU-item\">0.14</div><div class=\"OPFJCU-item\">0.15</div><div class=\"OPFJCU-item\">0.47</div><div class=\"OPFJCU-item\">0.47</div><div class=\"OPFJCU-item\">0.48</div>\n    </div>\n    <div class=\"OPFJCU-row\">\n        <h5 class=\"OPFJCU-item\">Image</h5>\n        <div class=\"OPFJCU-item\"><img src=\"data:image/png;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA0JCgsKCA0LCgsODg0PEyAVExISEyccHhcgLikxMC4pLSwzOko+MzZGNywtQFdBRkxOUlNSMj5aYVpQYEpRUk//2wBDAQ4ODhMREyYVFSZPNS01T09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT0//wAARCADIAMgDASIAAhEBAxEB/8QAGgAAAwEBAQEAAAAAAAAAAAAAAAECAwQFBv/EADYQAAIBAwMCBAUBBwQDAAAAAAECAAMRIQQSMUFREyJhcQUyQoGRoQYUI1LB0fAzYrHxJEPh/8QAGAEBAQEBAQAAAAAAAAAAAAAAAQACAwT/xAAgEQEBAQACAwEBAQEBAAAAAAAAARECIQMSMUFRMmEi/9oADAMBAAIRAxEAPwDrrh6QDgAXzidekPiMrta8xapvTaQD9pvosN95pr8eL+2lL+NQq2GQVnzAn1H7a1VvQpXFxdp8punn8v16vD/lW4X5kOcnOIDaDB8ic3UlbGAIxf2gox6xtciCHHJgW7RKtvWMj7SKSRxM2p3824iW1l6yd9+MxgpIu3nJhUqMMIJRQsbnEpaa28sdGfkc22o5uSfYTYKAmywAmx2gCyC46jrEWENXrEimVAsJLKgPmGZtS1HlKkk4wLTmeqd5wJdq2NBUG2ygASTUsLDn1mRDOfLx2likbXJlg2/h8jzv9hANbCr9zKCqpvzEXP0rIgUw63Z7EQRB2uZD7yblpKMVvYN7iQ10EbRn9ITmLVWW1zbtCXqvf/j79QVLbr89Z36I+dcTwtR8b0CKb1C5/wBonE37UNSP/i6e9uC5npvKPJ6Wtv22FvidOx5p8T51QD7zbX6/UfEtR42qfc3AAFrTHrOPO7denxyyYsWHSJs8XizfmDHPM5uprKMjPQyW3d4hbMBwZIfNwJjuUfMSTLDAiwFrywbp+W92jWwNwMRbOwz3g4dRYZkmpIBu1rSXrqBZbfaYEm3nP2EYDn5AFv6Zli9v4TVKrm+4KOYrBqYYMb9ZqumZmAIJZjYes7dboE0C0gXDu4NwOh/tNT4xfuV56qxW3mHrKFJFNycmaE3GJOJm1ucRx8pBgSTD7QzAlttzGqgcRgX5kGoRgLeSWVB5gALYmRFVzmyiXgdZCLtYYhINUHhSYSKVUD1lgYxEqm3EsC142iRlbMoe8GGY+RIjeE5iL7+lhGqre5EpiO0gm7YAH3kmkzHzNLDWlYte0tWIFFFzyZaooNziIcYiOcGHdPUU7GxCD8yQGIsf0heO95Iiir1uZW428thM2/EYY2knofBqbVviKYVtgLZ/A/UiZfEq41Gtdl3bV8ov6c/rOvTH9x+D1NVdhVr+VRj1tb9TPJDYxN3qY58e+Vp29YWAECfWT4ij1mHQ1PIOYC9ubzMv0UGCh91jiS1oCL2iYEHj8SgLRgkAggEd+skysxOTaUqD6hmXbqZNwCcw0GBY9oRbhwDmESYsJQ5kbhew5lAk9bCSgqSQRHUAAuCTMRUAvHE2BvJb3kU3u3US2YgjEkYj3G1pI82YSB5tHf0id0FrE39YDzC8sRlsSdwlY+0tNpYKoAJPMsVuMHuRa07NL8OquobUHwKZ6thj7AyTqVpOw0yAlceIRn3tMHq1qzXruzHpuN5rqfXPvl86dHxDVrXqotLy0KS7UX+s41a5sJQQcmMLY3mbdbnHJjLZULeY2EsUx1mtr8wt6YlpwlUDiOwvmAB6G/pKCu9QJTps7noBA9RIzmImwnTp9ItSpUTValNLsxtbLE+00DaKh/o0mqsPrrccfy/3jn9Z9vyORNNqq1NqlOk5pgXLAYtIp0lJ/iMwX/aAT+s662s1FYtvqNtb6QbD8TDn0hbPwzjb9bGpRRbUNNTX/fU87HPrgfYQmWYQ2n1jkRSGvcmaEEC4km54lA2wczbMRZ2GZIpgGxnR4g2nFpgSScYlqyNFWxxKZTcXkqp6mNye8CLFYmBY8xgkwtJEEUG8Ge3ELQsO8lhEuw7CbaGjfUbi/AN/aQR3xOrSWWhXa9mC3U9eD1muN7Y8k/8ALKl4SgnN7m3rJNibmWlFn3ZVdguQxsSPQdZtRSnVoOii1YZXcbhh2AHXj3zKcOXJcvLw4OQjPeAXuZ0qrkbNgJUcBe3eTWp/xtqoQ5GVUZ/Aly8d4zaOHmnO5GG4Ayk3VXCUlZ3P0qLmdY+Gmlo/3zXb1pFzTVFIuWAvnt+PxIOrYUzS09JKNMjIUXJ+5hn9a9t/yE01KkC2rrgMP/VTN2P34H+cS/31wm3TUhQpjkjLH7zlBAORz2jYm2DiXtnxem/VN4dY7rbKl756yCNuJN895LMxOMCZai+kf6zEpu5Y/aMtsWwNoYdalrc4hOMtc8kwj6s+7uWjo6otR1DU36CoMHsJD/D9Sh3Ydf5kNxOY0yRYmb0GqJ8jstsczexiceX5ULklWBxERtadzaoupFemlTtYbbfiZVKemqElKpp9g4x+f+4ffh2z7GIOJFRwDYCXXpVaBN13KPqBuJiGLHzLLLDOUvwwWPtNGa9rxAjb2kFgD3gWg4ndpGalSG0C7c4uWnBvW1xO2lr00a7GTdUGQ6/T2nTxzty896dmuWoulY1TtIA3eW4A4Av3v0nMwWhp6FfTVtxYklgtipHC/jP3kV9dqPiNNt9UbbC44BtJ0ldtPVWjVps1Cpa4GD7idpxm7XlvO5i6JZWWsCd3Nz3nqPpkq0xqdCodi3hulI4Vv5h2En4jp9J8O1b2oVKumdLU0rXDdLk+vpMG+MVgnh6ZUo0wPKEQAg/rLl5OPHo8fFy5dyPW1Oi0yMmu1ZZHABdQ+C4/59p5es+OVKg2aYeCL/MFs5+/SefqdVW1NTfVZiehJJ/5mI9Jx5+Xfj0ePwSf6URuGSfS8Xywi6zi9AIvJtnN5pvUiwXrz1kn3gSsLSXNhwZROLWmZFVuCFEYzSLMeBaQVu2XlGjc3dz9oWpU/W00zdqODYKSe8Ja1FJwtoR1ZFqcSWcJyZn5ibSvCH1Zlh1S1rni8HFz2lIqi1uJdVQBggwOJpHwbMvQ3t6wqBKx/gkCpfKkgX9ogpItxA0trBgbEZBjOX9Y5cf2OcFmbbYgjoZoiG+Zv/qW3C7cX6mBQk3XI7iN/wCKWT6Soo6cZxOIuWJLc9J6CMqPcHzDrb+n/U4q1A03JvdTkN0M34+nHzd9z47PhoUXUsGY5CnvPb+GfD31O6rUqbX043UlNvOf/nSfPaKkHNy+1wQFBvkz2tJXqUyQ606nqrdfQ/0nXcefB8RSpUSnUN2Sqbq7E3VjyD78+885RdwpFs2PpPboFatB6RbFzdH9Z5Wq0p07bTfaflc8ex/vOPPj7dx6PF5PWetYV2Vqh8MWThQe395IbFpkXIOY9wtkzlj0yxopzmFr9Jga4U4zAPUfIFpeq9o1LBcXgaiDreYeGWPmJmi0VXkjiWRbSOouRZOIyzlrhQI9o4Ahta+LR2DKgqTlmx2j2C1wJWxjgn7Sglr+stWIUX4EJptz2EIafVjYk+XmBJS+8yGZgb3t7SGO/FiZuRjWyVRewF7zRsC8xp07WnQR5czNa47+oBv1jJAGTAL2iZQcGDSfFCi1ryLktuAse/WWVtwIvDJOTHpm6YZRycxGoSCq3sfxKWkOZVgBJMwte11LZNsG2ZqKr6U7NPZv5iwvf7S6TKKVTuAOB6zNjc3zc5JmvbI5+u3t36bWirqFD0wCfXPuPWevR2alko6io3hMDyoa3+d589onNLUrUtuC5Ite46/pPRo10DqXbaSNt+Mg2v6CdOPLpx58LL0Xxb4QdMN+mdq+lXO8p51HXHUCeM9CopBfAPykcN7T7amlPUUPDctZrXIax55xx/WfM1dQ++olVvEQsQbgXOcZ+0zzka8Vt6cKUhNNhBt0mrU1dDUoXIFyy8lR/b/PWc4qYxmcrr0yxoyC18YiBB6TPxKpNlEZD/UftBNLXGBLdiwUNby4EyDYsTAVORt/MisjF+sW7pJUk8zSwVMHze0EkDGWzCFxa1oSLAqLzSmoXpCwj3WmtZw9ucAymOIt0hnHeBVA4F5j4giat0sY5R7RsCIEgTnWoSYNUz1jg9mjVLG0zap6ykAbkEiWEUjiXxd1OlqHxgpUlH8jZ6GWqMps/Iwe0YTGJXTMLyU459AA9RfBtNq9SkadLaLMFs46X9PfmYXN4EE9Jb1ivHvXoaD4xV05COpqU+PUf3nHrqnjaurVQFVdiQLcTM7kAI4PWT4lzaO3ME4yXYlSyOGVmB95ulNNSACfDrX5UeVhbAt3/wAzxM7C/MdxxaU5K8dNQy4JgwvmdVHULVpU9Nq1BpITtqDDLf16jrM9Rp2oVCjZHKsOGHcQv9hl/K5uDciXmUFAlKBeZ1rELe+eJe4kkAYMDjtaHHBkTKFRzzCAJ23+mEk5w4IyJHiW6StmIFJvpjazLsTjEChYXJuYiucnErdZbARBJSBwZRpXhT383mxuOIbTJGK0gDm802gQuTx94iCTYGGnDBAkl13cwGmJUuTcA5zGEUcAe8sEtWPeBtbMgtbAGJNi3pDDassAZlUqA9cy0pC9zKNJQcCOwd1ilRythcjoDK2VCc2E3RBa9xFuF+8vZeqQyr8xEPFpk4MzdKZe569JoiLbAl0e1A9bTo0+oC/w66+JRPQ8r6jtMOIDMzuGzZ26aulZKA1FNhUok23DlT0DDoZzgj7zXT16mn3hdrJUFnRhhhLr6aj4P7xp6hdPrU/NT9/T1jm/BufWF/SSL3mbVlGFBMBUY8rDFrQgkEA4hBWW17wk0hsZvIL9BHt/maIAXxNMJsTzkTUKAvEYX8SguOYacZ8QL46ywhLWGSYFdhIIII5EUhQ27zcSwDkmIsBY3i3X9BJLuLZ4mZqJ/NBlBXJN+0PDXosktRcXhj1klgq2tEKtxe2I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class=\"OPFJCU-item\"><img 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src=\"data:image/png;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAA0JCgsKCA0LCgsODg0PEyAVExISEyccHhcgLikxMC4pLSwzOko+MzZGNywtQFdBRkxOUlNSMj5aYVpQYEpRUk//2wBDAQ4ODhMREyYVFSZPNS01T09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT09PT0//wAARCADIAMgDASIAAhEBAxEB/8QAGwABAQADAQEBAAAAAAAAAAAAAAEDBAUCBgf/xAA6EAACAgIABAQFAQQJBQEAAAAAAQIDBBEFEiExE0FRYRQicYGRsSMyofAGFTNChMHR4vEWJGJylOH/xAAWAQEBAQAAAAAAAAAAAAAAAAAAAQL/xAAaEQEBAQEBAQEAAAAAAAAAAAAAEQEhMRJB/9oADAMBAAIRAxEAPwD8xBQUTQKAIAAKCAAAAAAIBURFAAAqhQAhoAEAFIAKBoAACjyUgAAAopCgCEKXRB5BdDQEBdEAIpfIhABQUAAAKAAAABFCAAAAeAGCgAAAAIKNkAFA2AGiaKEABSaAAAACjzAAoAARi5yUIJylJ6SS22zex+FZd9cpKMINJyULJcspJd9JgaINvClgwfPmQts69IQ6LWvN7MfxGsedEaquWUtqTgnOK9OYDAAAOpwvg6yqZZGRKUauVuKi9N+/0N2GBiXUN1Y0a1Ja223v6bfT7G7lc2Jg0qPzxilWn28tbZgqtXOqFapOPbS6F3YymJwjh8oKd1Sk4NuUYzktr36/po950sKTSxeH43q34aRlkoeK5zT120jl8XyMyiTXw1Mam/2d0Yttx30311v7Eynrdq4j/wBvyYmFXGUXtxrikvqeo8Vvy8d4teMna3raXVP0PnaeI5dG/CsUN99QX+hFnZKbamk2975Fvf4LT5dqvLyYyuxrK25cri09JxZqW05ELYVZNHNzR3GM/NPzT8jXoszsnmcOTl/vTlCKS+r0dSGSniyjdj12WOOoSjtRgtba3J9fsXv4eOdmcOlVLrVOiU1uMW9xf0fkaF1NlE+S6EoS9Gjp5GfVGvVcY+JrXyyco/l/5Gu8+M6HVbTzptdXLetei8vyTi40QdCE+G2zULKrKoptKcG+q8nJNv8ACME68OMtLItn7xqS/ViFa2wb0cTGlFS+KS2t6bimvqtk+HxE/myZ/blf+ZFaaIdGOPw1RjKebcuZ6cY1JtL30zFK/Gqy+fGx91QluHiPbaXr5fwAw14uTbBTqx7Zxf8AejBtfkzV8MzLMidKokpQ/fbaSgvVvskW/ieXfkTuc1Hne+SK+VfZmtZbbZOU7LJylL95t9X9R0b0uHUUtRyM2rncd6qkny/Vvp+DLP8AqemuMVGdk+b5tNz7Jee4rT9tnJBINiOXbVKSx34UHJyUV1192S/KvyZKV1kpaXKl2SXokuyMCKUAAAAAH1Wdk1PAUN87sltP0X/6c/Fu+Fvc5LncV8sfqYOE5dcrK8fIj0W+Wfp56ZsXX1WcTc4y5tv97XcrOOhXerWu2pLcUZJwV1Xw+ZDmrmu66b917o8zw/BqV1fZ9deh0sXeRw6alBNQe5eqGcTdfN/9NT/aWfG0qmD3zNPfL6vyX5PK/qXB7Oedan+9y/L26dO2u3qfRX008Q4ddRX3cXqPv5P8nx+Jm/BRnXPBxLpN9XfW5Ney69AvrLlcXnaq40U10wglqL1Pr91rX2NC22y6XNZOU2/NvZ1J8fum5OWFhbk05NVPq128/I8T4nj27lbw6l2S6ykmlt+b7FVy2Dfln16XLiwSS5e67enYyLiWHKlVWcLpa2tyT1L8pJ/xJBzUDp/D8JtUY1ZVtU2m9z7Lp2fRfqzXfDrXFSxpwyFrf7Pe++uz6/cQrUBnuw8nHW7qZwWt7a6encwhUAYAFAAgKQgqAQAAAAAAMtGT4EZKNNcnLo5S3vXp3N2mEb4q6iDhrvzS31Ryzu8G8KWBJNNzU3vp661/mVNd7h9iuxIqT5ptaSa6e56xb/h8mTg+Zfuyi+0ka2Mp13Trrb+f5lB9jzU7LZ3XuKVcdKx+Sb7IrLeto8KbyMP93u16Hz/FuGSysh5GMoQsmtzr3rmfqn2+x9PiSjy6fkunsa+dVXVCVsVCMEtzUukX/oanEzXwU4Sg9TjKL9GtEPqOK4tN3DrbeWyVlMU4wXWUd+fvE4ebw27DhXZJxtpsScba9uL35b9TO5G82tMFIRULGUo75ZNb6PT1sADZp4hkU1qEXBxTWuaCbXsn3M1/EK8qyFmTiqU0/manpNeS9XperOeCQdOdWBfzzpca1yrUXdy8r+kk9++me5YXCIx0+L2Ofmo4vMk/qpdTlICDp1VcHjCbuyciyS6x5Y8m/bWn+qFmVwqKUKeGSmkv7Sy+Sk39E9HMAg6VWXwqG/E4TOz/ABcl+iPFmTw2U9w4bOC9Fkt/qjRCEG4r+H9d4Fv/ANP+0yfF8NcZRfCpLr8so5Mt699pp/hHPAg3Hfw5vpgXL/E/7TBZZS8hzqo5at9K5TctdPXozEANjNzJ5sq5Trqg4R5V4cddPL8A1wBDr8Asmp2VxS5dqTf8DkG7wi5VZ8ebtNOPfWn5fz7lxN8fW2zisqppNLw3p+bZ54XiW5XDs+i6Xh2VWxs5V2WuvU93WRjKMmtyS2pLt9jHw7jWLZxKVXNGiiXNXeovm8dv5VNPX/BrGNZ6JQhkt12qcHrbXutrf2aNzLoryMaVL1y2RcXtb1v+dnyvFldwXi+65OceVV2JvpPl0vt05X9Wzq43HsKWLC225V9dSrfWSf0Xde5aR8zhcRtw5crcpxi/lafWL7dPb27Hb4dLhuQ5xlkKELVyypikoS158rW137o+aybXflW3NRi7JuWorSW35GNbXZma1Hc41wKWLvIwoueNrcvm24vf6fyzhs6eBxrIxFGEvngunv8Af1+/5N+zA4dxOuc8OUab9fKov5JP0afWPZma0+cRTLfjXY1nh5FU65ryktGIohD0QAACCFIwBSkRQAAAADyAAoA8lXR7XQAqOvh8VuePdVdOTUa3KEovTi17fXRo8OyFi5tdtkYThzJTUlta317GKm+VPPya3ODg2/JMxlpH0n9I5RycOrLm4ePZq2fL1S30S37pJnzZ0oWSyOFXR+eVkeXb5ujUe310v0Rzibt0zIgBQIeoTlCSlCTi15p6IAO9XxbG4hjxxOLRceqULYLah/5e3ul0fsczL4bfjRdkXC6ldfFqe1r1foahvcM4jPBuXMvEpknGcH16PvoknitAHTyuHxureZwyMrMfq51pfNTr1Xp7/wDL5oEAZCgNApAAAAAAAAgKCAACkKggAB1f6P1OeXZOa/Y11uVrWt69Pv1Ro/DSeF8XGUHX4jhyp/NHptNryX+hs4qtp4XlX8qddyVPX6pt/bp+TDh5tmI7IxSnVdHktqk3yzXvr9SK1SnucYx5eWyM+aKb5d/L7Pa7ngqAAAAIoVs8PzruH5UbqX/7R30kjocQ4fXl48+J8Ki5Ur+2qS61v6en89u3FM+Jl5GFcrca2Vcl6Po/qvMkGAjR3Fj4/G07aLK8fOeueuT1Gb67kv5+vfZysnEyMSahlUTqk+qU1rf09RRrg9aJoohSaBBQCAUAAAAAABUAipADoURs/qHKk0/D8avlb7PvvX8N/Y550eHyjbgZmHJrmlHxak5a3KPdenbb99HOJigASKgUaIAKQoVAUBBbR0sfjFygqc2uOZj9nC3uvpLuuxziEV7vlVK+cqK5V1t/LCUuZpfXzPGgCogACpoFGiCAAAAAKACgAAgAABUEAAA8wAAAhQUAQpAAAAgACgAIAKAPILoAAwCgAAAL5BAUhQETRdAACLuUAEAABCkCgAAnmAAHmB5gANghBWAAAAKAAAFAAoACIAAA2ABdjzAAnUjACmxsAgAAoFAIIAAAAA//2Q==\"/></div>\n    </div>\n</div>\n</div>"}}, "3d364ea68cfa46dd925687d1caf5fb47": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "842a92d5d85649a0bc3d3eb0003fd3d2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3a12606ce77a446081d823e284af33db": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_65663e4031d348bca6a5787eb1fcf7b6", "style": "IPY_MODEL_286d8c9f84104e0e9e3f79bb770e482c", "value": "<div><h5><b>Properties With No Outliers Found</h5></b></div>"}}, "65663e4031d348bca6a5787eb1fcf7b6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "286d8c9f84104e0e9e3f79bb770e482c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e9bfa6da5b2d407798ce1d2fc3301b16": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0a490daf9a6e4828957b9be866d41644", "style": "IPY_MODEL_1bd4b53dc60e4f749a8c34bf3fc4b506", "value": "<style type=\"text/css\">\n#T_fc9b6 table {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fc9b6 thead {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fc9b6 tbody {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fc9b6 th {\n  text-align: left;\n  white-space: pre-wrap;\n}\n#T_fc9b6 td {\n  text-align: left;\n  white-space: pre-wrap;\n}\n</style>\n<table id=\"T_fc9b6\">\n  <thead>\n    <tr>\n      <th id=\"T_fc9b6_level0_col0\" class=\"col_heading level0 col0\" >More Info</th>\n      <th id=\"T_fc9b6_level0_col1\" class=\"col_heading level0 col1\" >Properties</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_fc9b6_row0_col0\" class=\"data row0 col0\" >No outliers found.</td>\n      <td id=\"T_fc9b6_row0_col1\" class=\"data row0 col1\" >Aspect Ratio, Area</td>\n    </tr>\n  </tbody>\n</table>\n"}}, "0a490daf9a6e4828957b9be866d41644": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "1bd4b53dc60e4f749a8c34bf3fc4b506": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d0fdda61aa6148d5a1aba553543f399c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_86593cccf6fc4d7295f7c1dfbdea9e49", "style": "IPY_MODEL_36808124330e4eef919b88d81fcc2c20", "value": "<br><a href=\"#summary_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\" style=\"font-size: 14px;\">Go to top</a>"}}, "86593cccf6fc4d7295f7c1dfbdea9e49": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "36808124330e4eef919b88d81fcc2c20": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "1bf73d9d75894a8c862ade39c840962d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fe88706870694920abfac8746ce326d4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7d3cd174be9948d78e841c2f8a55efbc": {"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_d26a7118d1b2499d80dce94854356eca", "IPY_MODEL_9b2627deb6da445aa7272fb2fb715513", "IPY_MODEL_2c0bc469d5e04748838adb117e9cedfb"], "layout": "IPY_MODEL_fe3bf148beb54afb9b7dc9c8097a35a8"}}, "d26a7118d1b2499d80dce94854356eca": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e9d2f26a0fe34feba60ae6e13692102c", "style": "IPY_MODEL_9a731bf604264eacaaeb90c9e7600355", "value": "<h4 id=\"PropertyLabelCorrelation_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\"><b>Property Label Correlation</b></h4>"}}, "e9d2f26a0fe34feba60ae6e13692102c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9a731bf604264eacaaeb90c9e7600355": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9b2627deb6da445aa7272fb2fb715513": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_14d5740e03734305b3f83016fec81d37", "style": "IPY_MODEL_5ab95b7f9aac43c89a5b0a8db99e59bf", "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>"}}, "14d5740e03734305b3f83016fec81d37": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5ab95b7f9aac43c89a5b0a8db99e59bf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "2c0bc469d5e04748838adb117e9cedfb": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_dde1a8f2087e4fbe8bfba7862f8ed549", "IPY_MODEL_b4bf6d35e4624d76b365c6f3b2755382", "IPY_MODEL_836cbd0193af44abab5a93723a065c7d", "IPY_MODEL_cd1bf58dbfc64ad295817137bb704aad", "IPY_MODEL_a13e9c8ca42f4edcbeb82194d1a7ab10", "IPY_MODEL_966521fad2924c01911e2d56a9b22dca", "IPY_MODEL_466614cedb174ec5a5f747e00d5017cd"], "layout": "IPY_MODEL_954dc450168043839fba2c3f33ddbf65"}}, "dde1a8f2087e4fbe8bfba7862f8ed549": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3a4bf8a0a1ea4d82a50691ea60305827", "style": "IPY_MODEL_fd4fc6bbbb694fb3b4a8aae70b822e7c", "value": "<h5><b>Additional Outputs</b></h5>"}}, "3a4bf8a0a1ea4d82a50691ea60305827": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fd4fc6bbbb694fb3b4a8aae70b822e7c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "b4bf6d35e4624d76b365c6f3b2755382": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "#69b3a2"}, "name": "Test", "text": ["<b>0.02</b>", "<b>0.0</b>", "<b>0.0</b>"], "textposition": "outside", "x": ["RMS Contrast", "Mean Green Relative Intensity", "Aspect Ratio"], "y": {"dtype": "float64", "shape": [3]}, "type": "bar", "uid": "d83d6fa0-c6a2-47eb-9135-8f6f03194b4f"}], "_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, 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"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": "ZZtt1cObkT9zQXuG9YhEPwAAAAAAAAAA"}]}, "836cbd0193af44abab5a93723a065c7d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_eb1dfc2f33734e0dafccd5c4f2a67b95", "style": "IPY_MODEL_7989cfb6804a4cbba560cfb930299608", "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>"}}, "eb1dfc2f33734e0dafccd5c4f2a67b95": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "7989cfb6804a4cbba560cfb930299608": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "cd1bf58dbfc64ad295817137bb704aad": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_22a94d45a9114215a0683a5487e7ab53", "style": "IPY_MODEL_fc6686170eae485094aa06f91d26b569", "value": "<div><u>In the graph above</u>, we should suspect we have problems in our data if:</div>"}}, "22a94d45a9114215a0683a5487e7ab53": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fc6686170eae485094aa06f91d26b569": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a13e9c8ca42f4edcbeb82194d1a7ab10": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_408374796d864a0d8e6fafd152ba2df3", "style": "IPY_MODEL_b73da276f37b4e9bac1754aa1b3d1704", "value": "<div><b>Train dataset PPS values are high</b>:</div>"}}, "408374796d864a0d8e6fafd152ba2df3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b73da276f37b4e9bac1754aa1b3d1704": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "966521fad2924c01911e2d56a9b22dca": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_16c7927333724fbe8f2e272f5b5bf121", "style": "IPY_MODEL_3da97c702ff24ea7902feafde6455653", "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>"}}, "16c7927333724fbe8f2e272f5b5bf121": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3da97c702ff24ea7902feafde6455653": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "466614cedb174ec5a5f747e00d5017cd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_b1572c129dc949238b1745885579a0fe", "style": "IPY_MODEL_59e4a74d3c884ea5ad05af1f76cb460c", "value": "<br><a href=\"#summary_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\" style=\"font-size: 14px;\">Go to top</a>"}}, "b1572c129dc949238b1745885579a0fe": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "59e4a74d3c884ea5ad05af1f76cb460c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "954dc450168043839fba2c3f33ddbf65": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fe3bf148beb54afb9b7dc9c8097a35a8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "58a98f54f6f24d5c8a140a3d4a7c089a": {"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_1c4a1d6c5df44980a1b21e24975a0dca", "IPY_MODEL_3970e46e64af413f888ba6cfd24e5d8c", "IPY_MODEL_ce02178603c7466aa189f1b0b7daac0a"], "layout": "IPY_MODEL_37cff74575a943c4a4cfae48551710c1"}}, "1c4a1d6c5df44980a1b21e24975a0dca": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bbdbe638afc6450a8f3eaca4aad3c36c", "style": "IPY_MODEL_b47a32e79fc74ff88e0a92d77f9b68f9", "value": "<h4 id=\"PropertyLabelCorrelation_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\"><b>Property Label Correlation</b></h4>"}}, "bbdbe638afc6450a8f3eaca4aad3c36c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "b47a32e79fc74ff88e0a92d77f9b68f9": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "3970e46e64af413f888ba6cfd24e5d8c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_4be1246b73934422b48e5eca4cabc631", "style": "IPY_MODEL_48454471ef324ebc97d620538a9a0461", "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>"}}, "4be1246b73934422b48e5eca4cabc631": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "48454471ef324ebc97d620538a9a0461": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "ce02178603c7466aa189f1b0b7daac0a": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_af95b2180d854a91b13957bf67e94160", "IPY_MODEL_efaf67ae7ab447f88ba9a51810c2638f", "IPY_MODEL_6cf1445f61604b2fb039c1e05dc80089", "IPY_MODEL_26bd5a9fc2254f478589939dcff9205a", "IPY_MODEL_c74b7df73ff5456ba03802b19ba8e7c0", "IPY_MODEL_a9cfefdc4bd545c0af0e9661eb33525f", "IPY_MODEL_38d034afc20e4e9fa2010bf52ca36f96"], "layout": "IPY_MODEL_d7d2ba2761b2401b923f18df03d45c64"}}, "af95b2180d854a91b13957bf67e94160": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_67b0881d59a0407aa0810caabe08570a", "style": "IPY_MODEL_33bc8e6b750f4a8f96dbfc718b90c44a", "value": "<h5><b>Additional Outputs</b></h5>"}}, "67b0881d59a0407aa0810caabe08570a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "33bc8e6b750f4a8f96dbfc718b90c44a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "efaf67ae7ab447f88ba9a51810c2638f": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"marker": {"color": "#00008b"}, "name": "Train", "text": ["<b>0.04</b>", "<b>0.0</b>", "<b>0.0</b>"], "textposition": "outside", "x": ["Mean Green Relative Intensity", "Aspect Ratio", "Area"], "y": {"dtype": "float64", "shape": [3]}, "type": "bar", "uid": "5b4dfbf1-9f27-44ba-aa3a-d7a0205f7884"}], "_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"], 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"#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": "ME0SbVNOoj8AAAAAAAAAAAAAAAAAAAAA"}]}, "6cf1445f61604b2fb039c1e05dc80089": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_0214266142d64a3fbe84d8b9cbba0e41", "style": "IPY_MODEL_c9aad5efa85545059cece6e17efc8dde", "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>"}}, "0214266142d64a3fbe84d8b9cbba0e41": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "c9aad5efa85545059cece6e17efc8dde": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "26bd5a9fc2254f478589939dcff9205a": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_79b3147b610945539ba11bfb9a19b8d1", "style": "IPY_MODEL_ef996fd8e764497a8bdf33d1cb04bada", "value": "<div><u>In the graph above</u>, we should suspect we have problems in our data if:</div>"}}, "79b3147b610945539ba11bfb9a19b8d1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ef996fd8e764497a8bdf33d1cb04bada": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c74b7df73ff5456ba03802b19ba8e7c0": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_ae7e6c0ec2bf45be81a5d71d2b9a8d67", "style": "IPY_MODEL_950b6f0dd8984323b2c340efc46fcf26", "value": "<div><b>Train dataset PPS values are high</b>:</div>"}}, "ae7e6c0ec2bf45be81a5d71d2b9a8d67": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "950b6f0dd8984323b2c340efc46fcf26": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "a9cfefdc4bd545c0af0e9661eb33525f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_3619d1c24f13464ebc79f039b4e8709a", "style": "IPY_MODEL_fb630b724a3f4c11bf0aa5230d1e4396", "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>"}}, "3619d1c24f13464ebc79f039b4e8709a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fb630b724a3f4c11bf0aa5230d1e4396": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "38d034afc20e4e9fa2010bf52ca36f96": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_60e15154f72f4d3bb3b5b1cf3322baa4", "style": "IPY_MODEL_edb97b810a294347bd75498478c1c466", "value": "<br><a href=\"#summary_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\" style=\"font-size: 14px;\">Go to top</a>"}}, "60e15154f72f4d3bb3b5b1cf3322baa4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "edb97b810a294347bd75498478c1c466": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d7d2ba2761b2401b923f18df03d45c64": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "37cff74575a943c4a4cfae48551710c1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "2e2e347151ac475b96e3589cfea76826": {"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_54cc6ac944aa424da3cdddb4fdd5acbf", "IPY_MODEL_76d57f29b2c4479eb24a2ca1fa899223", "IPY_MODEL_6aa2cbcc98f5486d9f2847e8a117e931"], "layout": "IPY_MODEL_6eebdcf21e7c41b09db4aad710735bdb"}}, "54cc6ac944aa424da3cdddb4fdd5acbf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e1a5df654a314377bc812bb0b9c4ddb6", "style": "IPY_MODEL_ec3882421a6241438888bc3215dbcf6b", "value": "<h4 id=\"ConfusionMatrix_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\"><b>Confusion Matrix</b></h4>"}}, "e1a5df654a314377bc812bb0b9c4ddb6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "ec3882421a6241438888bc3215dbcf6b": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "76d57f29b2c4479eb24a2ca1fa899223": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_e7d253ed210346c69d22f33e3a2db120", "style": "IPY_MODEL_e5f99ea618b843e7bbb22904bf4db6b4", "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>"}}, "e7d253ed210346c69d22f33e3a2db120": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "e5f99ea618b843e7bbb22904bf4db6b4": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "6aa2cbcc98f5486d9f2847e8a117e931": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_fb4e0a3e89f941e08c5dfecdacb16343", "IPY_MODEL_9d98fe8b8e6540e38c61ab8a04696a11", "IPY_MODEL_70990d6003704216874774e2937081ca", "IPY_MODEL_d336c40cb294410cb1b24df3fd5a9069", "IPY_MODEL_ea7b531de6b74fd894baddf46696b6b9"], "layout": "IPY_MODEL_16199a4c28534ad783e120045d1259bf"}}, "fb4e0a3e89f941e08c5dfecdacb16343": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_602000bfd9204c518f674c014eae6517", "style": "IPY_MODEL_985226de46f940529d4e95d63ad2023c", "value": "<h5><b>Additional Outputs</b></h5>"}}, "602000bfd9204c518f674c014eae6517": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "985226de46f940529d4e95d63ad2023c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "9d98fe8b8e6540e38c61ab8a04696a11": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_d235caf27da9470b9f9aa45f660a7d29", "style": "IPY_MODEL_66aac5a1cf57452590550d3a2c566c27", "value": "<div>Showing 10 of 3 classes:</div>"}}, "d235caf27da9470b9f9aa45f660a7d29": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "66aac5a1cf57452590550d3a2c566c27": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "70990d6003704216874774e2937081ca": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_5ba8f5d2a023466b918aed9718dd9d38", "style": "IPY_MODEL_179fc26f971042448fc678252c74f9d3", "value": "<div>The overall accuracy of your model is: 55.36%.<br>Best accuracy achieved on samples with <b>culex_quinquefasciatus</b> label (86.08%).<br>Worst accuracy achieved on samples with <b>aedes_aegypti</b> label (28.05%).</div>"}}, "5ba8f5d2a023466b918aed9718dd9d38": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "179fc26f971042448fc678252c74f9d3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "d336c40cb294410cb1b24df3fd5a9069": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"customdata": [[[9.66, 28.05, 63.89], [7.98, 23.17, 52.78], [16.81, 48.78, 111.11]], [[2.94, 9.09, 10.94], [16.81, 51.95, 62.5], [12.61, 38.96, 46.88]], [[2.52, 7.59, 4.35], [2.1, 6.33, 3.62], [28.57, 86.08, 49.28]]], "hovertemplate": "% out of all data: 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[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": "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}}, "ea7b531de6b74fd894baddf46696b6b9": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_2c3f857798724b278e30274197e97106", "style": "IPY_MODEL_9041a2b4d6d341c2a23279f4a6a02d9f", "value": "<br><a href=\"#summary_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\" style=\"font-size: 14px;\">Go to top</a>"}}, "2c3f857798724b278e30274197e97106": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9041a2b4d6d341c2a23279f4a6a02d9f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "16199a4c28534ad783e120045d1259bf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "6eebdcf21e7c41b09db4aad710735bdb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "508864d3fffb45c2b0725aabbc7b5153": {"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_1025590cbc5d4be0ac4db13e0609a4fb", "IPY_MODEL_814d0c61412f40d69114da49113ca780", "IPY_MODEL_c467e999026e4b98a98ef8053742a47a"], "layout": "IPY_MODEL_fcd913c4308640848281b7a7ed6ef2c4"}}, "1025590cbc5d4be0ac4db13e0609a4fb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_44d8faff9586435b9d492185df00434b", "style": "IPY_MODEL_3aebb8c49da94698930f0445180b0835", "value": "<h4 id=\"ConfusionMatrix_TAXP0WFOY4DD1VN8LFJDTVV2Y-section-RERVT\"><b>Confusion Matrix</b></h4>"}}, "44d8faff9586435b9d492185df00434b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "3aebb8c49da94698930f0445180b0835": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "814d0c61412f40d69114da49113ca780": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_07ca41c180134a49b5c70e7ef3970207", "style": "IPY_MODEL_9c96f772a6be4da7a948efc44b131647", "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>"}}, "07ca41c180134a49b5c70e7ef3970207": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "9c96f772a6be4da7a948efc44b131647": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "c467e999026e4b98a98ef8053742a47a": {"model_name": "VBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "children": ["IPY_MODEL_cb7d3c11dc4449fca9df6f1e256e06e2", "IPY_MODEL_e9160cf4b1514f8cb2ae479a6c14a0a6", "IPY_MODEL_0cc36ff40ed444598c04eeb2afd62bac", "IPY_MODEL_89f0d2cd9cf346a9bfeae30324d1243e", "IPY_MODEL_e1dfa7af289046fcba197ddcb9792628"], "layout": "IPY_MODEL_451fc67a734643e4a5ace13ad4e1fc2d"}}, "cb7d3c11dc4449fca9df6f1e256e06e2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_61841947e8a14122953e58d83b7e48ce", "style": "IPY_MODEL_fb8ac7206f204c9fa3166d1854177845", "value": "<h5><b>Additional Outputs</b></h5>"}}, "61841947e8a14122953e58d83b7e48ce": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "fb8ac7206f204c9fa3166d1854177845": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "e9160cf4b1514f8cb2ae479a6c14a0a6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_c5c023bb60da452fb955ab22870664bf", "style": "IPY_MODEL_f8c1d8c2604b4a7cae8361050b502a21", "value": "<div>Showing 10 of 3 classes:</div>"}}, "c5c023bb60da452fb955ab22870664bf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "f8c1d8c2604b4a7cae8361050b502a21": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "0cc36ff40ed444598c04eeb2afd62bac": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "layout": "IPY_MODEL_bf14de669b7c45d8990680b52117e5fa", "style": "IPY_MODEL_5585134d5e6c4dd5aec790017b151595", "value": "<div>The overall accuracy of your model is: 99.52%.<br>Best accuracy achieved on samples with <b>culex_quinquefasciatus</b> label (100.0%).<br>Worst accuracy achieved on samples with <b>aedes_aegypti</b> label (99.04%).</div>"}}, "bf14de669b7c45d8990680b52117e5fa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {}}, "5585134d5e6c4dd5aec790017b151595": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {}}, "89f0d2cd9cf346a9bfeae30324d1243e": {"model_name": "FigureModel", "model_module": "jupyterlab-plotly", "model_module_version": "^5.18.0", "state": {"_config": {"plotlyServerURL": "https://plot.ly"}, "_data": [{"customdata": [[[33.01, 99.04, 100.0], [0.0, 0.0, 0.0], [0.32, 0.96, 0.97]], [[0.0, 0.0, 0.0], [33.17, 99.52, 100.0], [0.16, 0.48, 0.48]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [33.33, 100.0, 98.58]]], "hovertemplate": "% out of all data: <b>%{customdata[0]}%</b><br>% out of row: <b>%{customdata[1]}%</b><br>% out of column: <b>%{customdata[2]}%</b><extra></extra>", "showscale": false, "text": [[206.0, 0.0, 2.0], [0.0, 207.0, 1.0], [0.0, 0.0, 208.0]], "texttemplate": "%{text}", "x": ["aedes_aegypti", "aedes_albopictus", "culex_quinquefasciatus"], "xgap": 1, "y": ["aedes_aegypti", "aedes_albopictus", "culex_quinquefasciatus"], "ygap": 1, "z": [["99.04", "0", "0.96"], ["0", "99.52", "0.48"], ["0", "0", "100"]], "type": "heatmap", "uid": "0e3538fc-13f4-433f-98a9-32705f334ea8"}], "_dom_classes": [], "_js2py_layoutDelta": {}, "_js2py_pointsCallback": {}, "_js2py_relayout": {}, "_js2py_restyle": {}, "_js2py_traceDeltas": {}, "_js2py_update": {}, "_layout": {"height": 600, "template": {"data": {"barpolar": [{"marker": {"line": {"color": "#E5ECF6", "width": 0.5}, "pattern": {"fillmode": "overlay", "size": 10, 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>Reason</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td id=\"T_0970c_row0_col0\" class=\"data row0 col0\" >Mean Average Precision Report</td>\n      <td id=\"T_0970c_row0_col1\" class=\"data row0 col1\" >Check is irrelevant for task of type TaskType.CLASSIFICATION</td>\n    </tr>\n    <tr>\n      <td id=\"T_0970c_row1_col0\" class=\"data row1 col0\" >Mean Average Precision Report</td>\n      <td id=\"T_0970c_row1_col1\" class=\"data row1 col1\" >Check is irrelevant for task of type TaskType.CLASSIFICATION</td>\n    </tr>\n    <tr>\n      <td id=\"T_0970c_row2_col0\" class=\"data row2 col0\" >Label Property Outliers</td>\n      <td id=\"T_0970c_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_0970c_row3_col0\" class=\"data row3 col0\" >Label Property Outliers</td>\n      <td id=\"T_0970c_row3_col1\" class=\"data row3 col1\" 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