Hasano20
/

segformer_mixed-set2-788img-rgb_mit-b5_17epochs

@@ -1,199 +1,91 @@
 ---
-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_Mixed_Set2_788images_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_Mixed_Set2_788images_mit-b5_RGB
+This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the Hasano20/Mixed_Set2_788images dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0179
+- Mean Iou: 0.9757
+- Mean Accuracy: 0.9872
+- Overall Accuracy: 0.9938
+- Accuracy Background: 0.9959
+- Accuracy Melt: 0.9697
+- Accuracy Substrate: 0.9959
+- Iou Background: 0.9922
+- Iou Melt: 0.9437
+- Iou Substrate: 0.9911
+## 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: 8
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 100
+- 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 |
+|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
+| 0.1292        | 0.7042  | 50   | 0.1861          | 0.7698   | 0.8223        | 0.9387           | 0.9844              | 0.5153        | 0.9673             | 0.9318         | 0.4625   | 0.9152        |
+| 0.1161        | 1.4085  | 100  | 0.1307          | 0.8463   | 0.9335        | 0.9543           | 0.9851              | 0.8721        | 0.9433             | 0.9596         | 0.6514   | 0.9279        |
+| 0.072         | 2.1127  | 150  | 0.0675          | 0.9075   | 0.9607        | 0.9762           | 0.9887              | 0.9179        | 0.9755             | 0.9821         | 0.7779   | 0.9625        |
+| 0.0425        | 2.8169  | 200  | 0.0622          | 0.9078   | 0.9322        | 0.9781           | 0.9868              | 0.8138        | 0.9959             | 0.9838         | 0.7746   | 0.9652        |
+| 0.0214        | 3.5211  | 250  | 0.0372          | 0.9458   | 0.9688        | 0.9868           | 0.9905              | 0.9223        | 0.9935             | 0.9870         | 0.8702   | 0.9802        |
+| 0.0397        | 4.2254  | 300  | 0.0373          | 0.9428   | 0.9802        | 0.9858           | 0.9948              | 0.9635        | 0.9824             | 0.9892         | 0.8617   | 0.9774        |
+| 0.0515        | 4.9296  | 350  | 0.0411          | 0.9399   | 0.9735        | 0.9846           | 0.9902              | 0.9438        | 0.9864             | 0.9865         | 0.8583   | 0.9750        |
+| 0.0171        | 5.6338  | 400  | 0.0267          | 0.9587   | 0.9782        | 0.9898           | 0.9937              | 0.9477        | 0.9931             | 0.9900         | 0.9017   | 0.9843        |
+| 0.0274        | 6.3380  | 450  | 0.0262          | 0.9621   | 0.9780        | 0.9906           | 0.9935              | 0.9454        | 0.9951             | 0.9900         | 0.9107   | 0.9857        |
+| 0.0105        | 7.0423  | 500  | 0.0272          | 0.9597   | 0.9844        | 0.9900           | 0.9924              | 0.9695        | 0.9913             | 0.9898         | 0.9041   | 0.9852        |
+| 0.0143        | 7.7465  | 550  | 0.0250          | 0.9638   | 0.9824        | 0.9911           | 0.9946              | 0.9593        | 0.9931             | 0.9907         | 0.9142   | 0.9865        |
+| 0.0153        | 8.4507  | 600  | 0.0226          | 0.9670   | 0.9826        | 0.9918           | 0.9947              | 0.9585        | 0.9946             | 0.9909         | 0.9223   | 0.9878        |
+| 0.011         | 9.1549  | 650  | 0.0201          | 0.9711   | 0.9841        | 0.9926           | 0.9936              | 0.9622        | 0.9965             | 0.9908         | 0.9330   | 0.9893        |
+| 0.009         | 9.8592  | 700  | 0.0199          | 0.9707   | 0.9858        | 0.9926           | 0.9962              | 0.9676        | 0.9936             | 0.9913         | 0.9315   | 0.9891        |
+| 0.017         | 10.5634 | 750  | 0.0206          | 0.9692   | 0.9869        | 0.9923           | 0.9964              | 0.9723        | 0.9921             | 0.9911         | 0.9279   | 0.9886        |
+| 0.0095        | 11.2676 | 800  | 0.0184          | 0.9733   | 0.9870        | 0.9933           | 0.9954              | 0.9704        | 0.9950             | 0.9917         | 0.9379   | 0.9902        |
+| 0.0142        | 11.9718 | 850  | 0.0179          | 0.9740   | 0.9862        | 0.9935           | 0.9957              | 0.9671        | 0.9957             | 0.9919         | 0.9395   | 0.9905        |
+| 0.0134        | 12.6761 | 900  | 0.0180          | 0.9739   | 0.9882        | 0.9934           | 0.9948              | 0.9747        | 0.9951             | 0.9919         | 0.9394   | 0.9903        |
+| 0.0096        | 13.3803 | 950  | 0.0179          | 0.9744   | 0.9864        | 0.9936           | 0.9960              | 0.9675        | 0.9956             | 0.9922         | 0.9406   | 0.9905        |
+| 0.0089        | 14.0845 | 1000 | 0.0174          | 0.9744   | 0.9881        | 0.9936           | 0.9958              | 0.9737        | 0.9949             | 0.9922         | 0.9404   | 0.9908        |
+| 0.0094        | 14.7887 | 1050 | 0.0174          | 0.9754   | 0.9864        | 0.9938           | 0.9962              | 0.9671        | 0.9960             | 0.9924         | 0.9428   | 0.9911        |
+| 0.0089        | 15.4930 | 1100 | 0.0192          | 0.9748   | 0.9860        | 0.9935           | 0.9945              | 0.9666        | 0.9968             | 0.9918         | 0.9421   | 0.9905        |
+| 0.0087        | 16.1972 | 1150 | 0.0179          | 0.9757   | 0.9872        | 0.9938           | 0.9959              | 0.9697        | 0.9959             | 0.9922         | 0.9437   | 0.9911        |
+### 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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