Hasano20
/

SegFormer_Clean_Set1_95images_mit-b5_RGB

@@ -1,199 +1,109 @@
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-library_name: transformers
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 ---
+license: other
+base_model: nvidia/mit-b5
+tags:
+- vision
+- image-segmentation
+- generated_from_trainer
+model-index:
+- name: SegFormer_Clean_Set1_95images_mit-b5_RGB
+  results: []
 ---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# SegFormer_Clean_Set1_95images_mit-b5_RGB
+This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the Hasano20/Clean_Set1_95images dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0210
+- Mean Iou: 0.9721
+- Mean Accuracy: 0.9816
+- Overall Accuracy: 0.9941
+- Accuracy Background: 0.9974
+- Accuracy Melt: 0.9506
+- Accuracy Substrate: 0.9969
+- Iou Background: 0.9954
+- Iou Melt: 0.9316
+- Iou Substrate: 0.9891
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 0.0001
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 50
+### Training results
+| Training Loss | Epoch   | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Melt | Accuracy Substrate | Iou Background | Iou Melt | Iou Substrate |
+|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
+| 0.2459        | 1.1765  | 20   | 0.4048          | 0.5613   | 0.6310        | 0.8812           | 0.9733              | 0.0102        | 0.9096             | 0.8391         | 0.0100   | 0.8349        |
+| 0.2421        | 2.3529  | 40   | 0.1840          | 0.6645   | 0.7118        | 0.9292           | 0.9969              | 0.1720        | 0.9666             | 0.9574         | 0.1475   | 0.8886        |
+| 0.1511        | 3.5294  | 60   | 0.1347          | 0.6751   | 0.7154        | 0.9392           | 0.9909              | 0.1590        | 0.9963             | 0.9639         | 0.1570   | 0.9045        |
+| 0.1449        | 4.7059  | 80   | 0.1350          | 0.7359   | 0.7793        | 0.9471           | 0.9937              | 0.3623        | 0.9819             | 0.9642         | 0.3221   | 0.9213        |
+| 0.1276        | 5.8824  | 100  | 0.1006          | 0.8194   | 0.9138        | 0.9551           | 0.9823              | 0.8117        | 0.9474             | 0.9707         | 0.5605   | 0.9271        |
+| 0.0638        | 7.0588  | 120  | 0.0916          | 0.8139   | 0.8438        | 0.9646           | 0.9964              | 0.5438        | 0.9913             | 0.9779         | 0.5208   | 0.9431        |
+| 0.0535        | 8.2353  | 140  | 0.0695          | 0.8572   | 0.8769        | 0.9735           | 0.9969              | 0.6367        | 0.9971             | 0.9804         | 0.6316   | 0.9597        |
+| 0.0346        | 9.4118  | 160  | 0.0435          | 0.9224   | 0.9384        | 0.9848           | 0.9962              | 0.8230        | 0.9959             | 0.9888         | 0.8039   | 0.9745        |
+| 0.0393        | 10.5882 | 180  | 0.0376          | 0.9352   | 0.9642        | 0.9867           | 0.9970              | 0.9082        | 0.9873             | 0.9882         | 0.8376   | 0.9798        |
+| 0.0294        | 11.7647 | 200  | 0.0448          | 0.9298   | 0.9746        | 0.9851           | 0.9932              | 0.9487        | 0.9818             | 0.9916         | 0.8253   | 0.9725        |
+| 0.0387        | 12.9412 | 220  | 0.0409          | 0.9270   | 0.9488        | 0.9855           | 0.9970              | 0.8575        | 0.9918             | 0.9830         | 0.8157   | 0.9823        |
+| 0.0435        | 14.1176 | 240  | 0.0353          | 0.9482   | 0.9685        | 0.9886           | 0.9891              | 0.9185        | 0.9980             | 0.9881         | 0.8749   | 0.9816        |
+| 0.022         | 15.2941 | 260  | 0.0246          | 0.9587   | 0.9696        | 0.9915           | 0.9970              | 0.9152        | 0.9967             | 0.9931         | 0.8979   | 0.9853        |
+| 0.0203        | 16.4706 | 280  | 0.0191          | 0.9698   | 0.9826        | 0.9934           | 0.9953              | 0.9557        | 0.9967             | 0.9935         | 0.9272   | 0.9887        |
+| 0.0212        | 17.6471 | 300  | 0.0256          | 0.9604   | 0.9724        | 0.9917           | 0.9953              | 0.9243        | 0.9975             | 0.9933         | 0.9028   | 0.9851        |
+| 0.0123        | 18.8235 | 320  | 0.0223          | 0.9638   | 0.9763        | 0.9924           | 0.9954              | 0.9363        | 0.9972             | 0.9938         | 0.9112   | 0.9864        |
+| 0.0137        | 20.0    | 340  | 0.0292          | 0.9543   | 0.9720        | 0.9906           | 0.9933              | 0.9256        | 0.9969             | 0.9919         | 0.8867   | 0.9844        |
+| 0.0092        | 21.1765 | 360  | 0.0171          | 0.9719   | 0.9797        | 0.9941           | 0.9977              | 0.9439        | 0.9974             | 0.9942         | 0.9312   | 0.9902        |
+| 0.0094        | 22.3529 | 380  | 0.0178          | 0.9730   | 0.9829        | 0.9941           | 0.9984              | 0.9550        | 0.9952             | 0.9938         | 0.9352   | 0.9901        |
+| 0.016         | 23.5294 | 400  | 0.0163          | 0.9760   | 0.9881        | 0.9946           | 0.9954              | 0.9721        | 0.9969             | 0.9944         | 0.9430   | 0.9907        |
+| 0.0083        | 24.7059 | 420  | 0.0151          | 0.9784   | 0.9882        | 0.9952           | 0.9973              | 0.9707        | 0.9965             | 0.9952         | 0.9483   | 0.9916        |
+| 0.0094        | 25.8824 | 440  | 0.0259          | 0.9626   | 0.9731        | 0.9925           | 0.9971              | 0.9248        | 0.9972             | 0.9952         | 0.9067   | 0.9858        |
+| 0.0144        | 27.0588 | 460  | 0.0171          | 0.9743   | 0.9860        | 0.9945           | 0.9980              | 0.9648        | 0.9951             | 0.9948         | 0.9376   | 0.9905        |
+| 0.0075        | 28.2353 | 480  | 0.0168          | 0.9733   | 0.9824        | 0.9943           | 0.9972              | 0.9528        | 0.9972             | 0.9949         | 0.9351   | 0.9900        |
+| 0.0076        | 29.4118 | 500  | 0.0171          | 0.9756   | 0.9842        | 0.9947           | 0.9979              | 0.9580        | 0.9966             | 0.9951         | 0.9409   | 0.9907        |
+| 0.0075        | 30.5882 | 520  | 0.0170          | 0.9748   | 0.9835        | 0.9946           | 0.9974              | 0.9560        | 0.9971             | 0.9954         | 0.9388   | 0.9901        |
+| 0.0084        | 31.7647 | 540  | 0.0154          | 0.9783   | 0.9899        | 0.9952           | 0.9976              | 0.9770        | 0.9953             | 0.9954         | 0.9480   | 0.9914        |
+| 0.0055        | 32.9412 | 560  | 0.0156          | 0.9777   | 0.9888        | 0.9951           | 0.9971              | 0.9730        | 0.9962             | 0.9953         | 0.9465   | 0.9913        |
+| 0.009         | 34.1176 | 580  | 0.0166          | 0.9752   | 0.9856        | 0.9947           | 0.9972              | 0.9630        | 0.9965             | 0.9953         | 0.9400   | 0.9904        |
+| 0.0055        | 35.2941 | 600  | 0.0176          | 0.9745   | 0.9835        | 0.9946           | 0.9972              | 0.9560        | 0.9974             | 0.9954         | 0.9378   | 0.9902        |
+| 0.0069        | 36.4706 | 620  | 0.0180          | 0.9748   | 0.9832        | 0.9946           | 0.9974              | 0.9547        | 0.9974             | 0.9955         | 0.9388   | 0.9902        |
+| 0.0051        | 37.6471 | 640  | 0.0181          | 0.9752   | 0.9843        | 0.9947           | 0.9975              | 0.9585        | 0.9968             | 0.9955         | 0.9397   | 0.9903        |
+| 0.0071        | 38.8235 | 660  | 0.0201          | 0.9729   | 0.9847        | 0.9943           | 0.9968              | 0.9610        | 0.9963             | 0.9953         | 0.9337   | 0.9896        |
+| 0.0058        | 40.0    | 680  | 0.0208          | 0.9720   | 0.9826        | 0.9941           | 0.9971              | 0.9540        | 0.9968             | 0.9954         | 0.9315   | 0.9892        |
+| 0.0061        | 41.1765 | 700  | 0.0222          | 0.9699   | 0.9802        | 0.9937           | 0.9973              | 0.9467        | 0.9967             | 0.9954         | 0.9260   | 0.9883        |
+| 0.0062        | 42.3529 | 720  | 0.0205          | 0.9720   | 0.9819        | 0.9941           | 0.9975              | 0.9516        | 0.9966             | 0.9953         | 0.9315   | 0.9891        |
+| 0.004         | 43.5294 | 740  | 0.0193          | 0.9741   | 0.9835        | 0.9945           | 0.9973              | 0.9561        | 0.9969             | 0.9954         | 0.9371   | 0.9898        |
+| 0.0065        | 44.7059 | 760  | 0.0195          | 0.9738   | 0.9842        | 0.9944           | 0.9971              | 0.9588        | 0.9967             | 0.9953         | 0.9363   | 0.9898        |
+| 0.0044        | 45.8824 | 780  | 0.0201          | 0.9731   | 0.9830        | 0.9943           | 0.9971              | 0.9550        | 0.9969             | 0.9954         | 0.9344   | 0.9895        |
+| 0.0073        | 47.0588 | 800  | 0.0210          | 0.9723   | 0.9818        | 0.9941           | 0.9972              | 0.9512        | 0.9971             | 0.9954         | 0.9323   | 0.9891        |
+| 0.0049        | 48.2353 | 820  | 0.0209          | 0.9723   | 0.9822        | 0.9941           | 0.9974              | 0.9527        | 0.9966             | 0.9954         | 0.9322   | 0.9892        |
+| 0.0069        | 49.4118 | 840  | 0.0210          | 0.9721   | 0.9816        | 0.9941           | 0.9974              | 0.9506        | 0.9969             | 0.9954         | 0.9316   | 0.9891        |
+### Framework versions
+- Transformers 4.41.2
+- Pytorch 2.0.1+cu117
+- Datasets 2.19.2
+- Tokenizers 0.19.1

config.json ADDED Viewed

	@@ -0,0 +1,80 @@

+{
+  "_name_or_path": "nvidia/mit-b5",
+  "architectures": [
+    "SegformerForSemanticSegmentation"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "classifier_dropout_prob": 0.1,
+  "decoder_hidden_size": 768,
+  "depths": [
+    3,
+    6,
+    40,
+    3
+  ],
+  "downsampling_rates": [
+    1,
+    4,
+    8,
+    16
+  ],
+  "drop_path_rate": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_sizes": [
+    64,
+    128,
+    320,
+    512
+  ],
+  "id2label": {
+    "0": "background",
+    "1": "melt",
+    "2": "substrate"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    "background": 0,
+    "melt": 1,
+    "substrate": 2
+  },
+  "layer_norm_eps": 1e-06,
+  "mlp_ratios": [
+    4,
+    4,
+    4,
+    4
+  ],
+  "model_type": "segformer",
+  "num_attention_heads": [
+    1,
+    2,
+    5,
+    8
+  ],
+  "num_channels": 3,
+  "num_encoder_blocks": 4,
+  "patch_sizes": [
+    7,
+    3,
+    3,
+    3
+  ],
+  "reshape_last_stage": true,
+  "semantic_loss_ignore_index": 255,
+  "sr_ratios": [
+    8,
+    4,
+    2,
+    1
+  ],
+  "strides": [
+    4,
+    2,
+    2,
+    2
+  ],
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2"
+}

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