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README.md +105 -195
config.json +78 -0
model.safetensors +3 -0
training_args.bin +3 -0

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 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-## Uses
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
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-## Training Details
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 ---
 library_name: transformers
+license: other
+base_model: nvidia/mit-b0
+tags:
+- vision
+- image-segmentation
+- generated_from_trainer
+model-index:
+- name: segformer-b0-finetuned-batch2-30nov
+  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-b0-finetuned-batch2-30nov
+This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the PushkarA07/batch2-tiles dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0982
+- Mean Iou: 0.6679
+- Mean Accuracy: 0.7012
+- Overall Accuracy: 0.9957
+- Accuracy Abnormality: 0.4034
+- Iou Abnormality: 0.3401
+## 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: 6e-05
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 42
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 10
+### Training results
+| Training Loss | Epoch  | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Abnormality | Iou Abnormality |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------:|:---------------:|
+| 0.2072        | 0.2222 | 10   | 0.2852          | 0.5513   | 0.6876        | 0.9843           | 0.3876               | 0.1183          |
+| 0.242         | 0.4444 | 20   | 0.2535          | 0.5557   | 0.6299        | 0.9896           | 0.2664               | 0.1220          |
+| 0.236         | 0.6667 | 30   | 0.1934          | 0.5640   | 0.6093        | 0.9923           | 0.2220               | 0.1358          |
+| 0.1698        | 0.8889 | 40   | 0.2124          | 0.5858   | 0.6090        | 0.9944           | 0.2193               | 0.1771          |
+| 0.1569        | 1.1111 | 50   | 0.1508          | 0.5692   | 0.6331        | 0.9914           | 0.2709               | 0.1470          |
+| 0.1436        | 1.3333 | 60   | 0.1279          | 0.5781   | 0.6111        | 0.9937           | 0.2243               | 0.1626          |
+| 0.1626        | 1.5556 | 70   | 0.1646          | 0.5661   | 0.5729        | 0.9950           | 0.1461               | 0.1373          |
+| 0.114         | 1.7778 | 80   | 0.1678          | 0.5670   | 0.5803        | 0.9946           | 0.1615               | 0.1395          |
+| 0.1289        | 2.0    | 90   | 0.1249          | 0.5442   | 0.5515        | 0.9946           | 0.1037               | 0.0939          |
+| 0.1467        | 2.2222 | 100  | 0.1260          | 0.5402   | 0.5541        | 0.9937           | 0.1097               | 0.0867          |
+| 0.1887        | 2.4444 | 110  | 0.1186          | 0.5499   | 0.5646        | 0.9940           | 0.1305               | 0.1059          |
+| 0.0918        | 2.6667 | 120  | 0.0961          | 0.5956   | 0.6220        | 0.9945           | 0.2453               | 0.1967          |
+| 0.1173        | 2.8889 | 130  | 0.0911          | 0.6058   | 0.6578        | 0.9938           | 0.3182               | 0.2179          |
+| 0.0988        | 3.1111 | 140  | 0.0960          | 0.6210   | 0.6534        | 0.9949           | 0.3081               | 0.2471          |
+| 0.0753        | 3.3333 | 150  | 0.0937          | 0.6303   | 0.6841        | 0.9944           | 0.3704               | 0.2661          |
+| 0.1008        | 3.5556 | 160  | 0.0940          | 0.6418   | 0.7015        | 0.9946           | 0.4052               | 0.2890          |
+| 0.093         | 3.7778 | 170  | 0.0996          | 0.6523   | 0.6951        | 0.9952           | 0.3916               | 0.3094          |
+| 0.0901        | 4.0    | 180  | 0.1068          | 0.6448   | 0.6702        | 0.9955           | 0.3412               | 0.2941          |
+| 0.0913        | 4.2222 | 190  | 0.0860          | 0.6401   | 0.6591        | 0.9956           | 0.3188               | 0.2845          |
+| 0.0715        | 4.4444 | 200  | 0.0726          | 0.6376   | 0.6588        | 0.9955           | 0.3183               | 0.2796          |
+| 0.0585        | 4.6667 | 210  | 0.1030          | 0.6259   | 0.6418        | 0.9955           | 0.2843               | 0.2563          |
+| 0.0875        | 4.8889 | 220  | 0.1419          | 0.6402   | 0.6581        | 0.9957           | 0.3168               | 0.2847          |
+| 0.053         | 5.1111 | 230  | 0.0792          | 0.6224   | 0.6368        | 0.9955           | 0.2742               | 0.2492          |
+| 0.0741        | 5.3333 | 240  | 0.1018          | 0.6277   | 0.6438        | 0.9955           | 0.2882               | 0.2599          |
+| 0.0667        | 5.5556 | 250  | 0.1554          | 0.6427   | 0.6698        | 0.9955           | 0.3405               | 0.2900          |
+| 0.0482        | 5.7778 | 260  | 0.1308          | 0.6366   | 0.6578        | 0.9955           | 0.3164               | 0.2777          |
+| 0.0574        | 6.0    | 270  | 0.1338          | 0.6499   | 0.6763        | 0.9956           | 0.3535               | 0.3043          |
+| 0.0575        | 6.2222 | 280  | 0.1473          | 0.6657   | 0.7068        | 0.9955           | 0.4149               | 0.3359          |
+| 0.0556        | 6.4444 | 290  | 0.1115          | 0.6607   | 0.6935        | 0.9956           | 0.3879               | 0.3257          |
+| 0.0535        | 6.6667 | 300  | 0.0924          | 0.6608   | 0.6930        | 0.9956           | 0.3870               | 0.3259          |
+| 0.0474        | 6.8889 | 310  | 0.0916          | 0.6625   | 0.6989        | 0.9956           | 0.3989               | 0.3295          |
+| 0.066         | 7.1111 | 320  | 0.1368          | 0.6720   | 0.7167        | 0.9956           | 0.4347               | 0.3484          |
+| 0.0546        | 7.3333 | 330  | 0.1216          | 0.6676   | 0.7048        | 0.9956           | 0.4107               | 0.3395          |
+| 0.0608        | 7.5556 | 340  | 0.1136          | 0.6653   | 0.6982        | 0.9957           | 0.3975               | 0.3349          |
+| 0.0572        | 7.7778 | 350  | 0.1108          | 0.6678   | 0.7034        | 0.9957           | 0.4079               | 0.3400          |
+| 0.0548        | 8.0    | 360  | 0.1140          | 0.6705   | 0.7105        | 0.9956           | 0.4223               | 0.3453          |
+| 0.0495        | 8.2222 | 370  | 0.0903          | 0.6665   | 0.7059        | 0.9956           | 0.4131               | 0.3374          |
+| 0.07          | 8.4444 | 380  | 0.1136          | 0.6712   | 0.7110        | 0.9957           | 0.4232               | 0.3468          |
+| 0.0492        | 8.6667 | 390  | 0.1237          | 0.6692   | 0.7044        | 0.9957           | 0.4099               | 0.3427          |
+| 0.0462        | 8.8889 | 400  | 0.1226          | 0.6739   | 0.7133        | 0.9957           | 0.4278               | 0.3522          |
+| 0.0429        | 9.1111 | 410  | 0.0969          | 0.6652   | 0.6956        | 0.9958           | 0.3921               | 0.3347          |
+| 0.0431        | 9.3333 | 420  | 0.0875          | 0.6634   | 0.6925        | 0.9958           | 0.3858               | 0.3311          |
+| 0.0416        | 9.5556 | 430  | 0.0927          | 0.6640   | 0.6944        | 0.9957           | 0.3898               | 0.3323          |
+| 0.0439        | 9.7778 | 440  | 0.0921          | 0.6660   | 0.6990        | 0.9957           | 0.3990               | 0.3362          |
+| 0.0594        | 10.0   | 450  | 0.0982          | 0.6679   | 0.7012        | 0.9957           | 0.4034               | 0.3401          |
+### Framework versions
+- Transformers 4.46.2
+- Pytorch 2.5.1+cu121
+- Datasets 3.1.0
+- Tokenizers 0.20.3

config.json ADDED Viewed

	@@ -0,0 +1,78 @@

+{
+  "_name_or_path": "nvidia/mit-b0",
+  "architectures": [
+    "SegformerForSemanticSegmentation"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "classifier_dropout_prob": 0.1,
+  "decoder_hidden_size": 256,
+  "depths": [
+    2,
+    2,
+    2,
+    2
+  ],
+  "downsampling_rates": [
+    1,
+    4,
+    8,
+    16
+  ],
+  "drop_path_rate": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_sizes": [
+    32,
+    64,
+    160,
+    256
+  ],
+  "id2label": {
+    "0": "normal",
+    "1": "abnormality"
+  },
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "label2id": {
+    "abnormality": 1,
+    "normal": 0
+  },
+  "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": "4.46.2"
+}

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