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README.md +95 -196
config.json +80 -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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 ---
+license: other
+base_model: nvidia/mit-b5
+tags:
+- vision
+- image-segmentation
+- generated_from_trainer
+model-index:
+- name: SegFormer_mit-b5_Clean-Set3-Grayscale_Augmented_Hard
+  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_mit-b5_Clean-Set3-Grayscale_Augmented_Hard
+This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the Hasano20/Clean-Set3-Grayscale dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0143
+- Mean Iou: 0.9789
+- Mean Accuracy: 0.9908
+- Overall Accuracy: 0.9945
+- Accuracy Background: 0.9964
+- Accuracy Melt: 0.9810
+- Accuracy Substrate: 0.9951
+- Iou Background: 0.9930
+- Iou Melt: 0.9518
+- Iou Substrate: 0.9919
+## 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: 0.0002
+- 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: 25
+### 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 |
+|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
+| 0.3678        | 0.3030 | 50   | 0.1206          | 0.8584   | 0.9180        | 0.9591           | 0.9811              | 0.8082        | 0.9648             | 0.9560         | 0.6832   | 0.9361        |
+| 0.1315        | 0.6061 | 100  | 0.0573          | 0.9293   | 0.9609        | 0.9808           | 0.9953              | 0.9068        | 0.9805             | 0.9764         | 0.8404   | 0.9710        |
+| 0.0983        | 0.9091 | 150  | 0.0426          | 0.9427   | 0.9712        | 0.9855           | 0.9927              | 0.9330        | 0.9879             | 0.9865         | 0.8645   | 0.9772        |
+| 0.0302        | 1.2121 | 200  | 0.0397          | 0.9420   | 0.9562        | 0.9860           | 0.9937              | 0.8783        | 0.9965             | 0.9870         | 0.8609   | 0.9781        |
+| 0.0378        | 1.5152 | 250  | 0.0366          | 0.9447   | 0.9804        | 0.9856           | 0.9916              | 0.9655        | 0.9840             | 0.9872         | 0.8704   | 0.9765        |
+| 0.232         | 1.8182 | 300  | 0.0278          | 0.9582   | 0.9810        | 0.9893           | 0.9894              | 0.9599        | 0.9938             | 0.9875         | 0.9026   | 0.9844        |
+| 0.023         | 2.1212 | 350  | 0.0252          | 0.9630   | 0.9821        | 0.9905           | 0.9958              | 0.9595        | 0.9910             | 0.9895         | 0.9141   | 0.9852        |
+| 0.0254        | 2.4242 | 400  | 0.0263          | 0.9626   | 0.9841        | 0.9901           | 0.9964              | 0.9675        | 0.9885             | 0.9887         | 0.9146   | 0.9846        |
+| 0.0153        | 2.7273 | 450  | 0.0299          | 0.9613   | 0.9735        | 0.9906           | 0.9952              | 0.9290        | 0.9963             | 0.9904         | 0.9080   | 0.9855        |
+| 0.0172        | 3.0303 | 500  | 0.0230          | 0.9645   | 0.9776        | 0.9913           | 0.9956              | 0.9417        | 0.9956             | 0.9917         | 0.9153   | 0.9864        |
+| 0.0338        | 3.3333 | 550  | 0.0185          | 0.9723   | 0.9875        | 0.9928           | 0.9972              | 0.9733        | 0.9922             | 0.9913         | 0.9368   | 0.9889        |
+| 0.0168        | 3.6364 | 600  | 0.0231          | 0.9679   | 0.9788        | 0.9922           | 0.9969              | 0.9438        | 0.9958             | 0.9921         | 0.9237   | 0.9878        |
+| 0.0253        | 3.9394 | 650  | 0.0245          | 0.9664   | 0.9772        | 0.9918           | 0.9965              | 0.9388        | 0.9962             | 0.9920         | 0.9202   | 0.9869        |
+| 0.0163        | 4.2424 | 700  | 0.0191          | 0.9689   | 0.9832        | 0.9923           | 0.9961              | 0.9592        | 0.9943             | 0.9917         | 0.9270   | 0.9881        |
+| 0.0133        | 4.5455 | 750  | 0.0173          | 0.9745   | 0.9877        | 0.9932           | 0.9976              | 0.9728        | 0.9928             | 0.9913         | 0.9428   | 0.9895        |
+| 0.0133        | 4.8485 | 800  | 0.0171          | 0.9742   | 0.9876        | 0.9934           | 0.9965              | 0.9721        | 0.9942             | 0.9921         | 0.9405   | 0.9901        |
+| 0.0362        | 5.1515 | 850  | 0.0178          | 0.9725   | 0.9866        | 0.9931           | 0.9973              | 0.9692        | 0.9934             | 0.9918         | 0.9360   | 0.9897        |
+| 0.0142        | 5.4545 | 900  | 0.0208          | 0.9679   | 0.9888        | 0.9919           | 0.9961              | 0.9797        | 0.9904             | 0.9919         | 0.9244   | 0.9874        |
+| 0.0111        | 5.7576 | 950  | 0.0149          | 0.9772   | 0.9882        | 0.9941           | 0.9964              | 0.9727        | 0.9956             | 0.9924         | 0.9478   | 0.9915        |
+| 0.0184        | 6.0606 | 1000 | 0.0165          | 0.9737   | 0.9822        | 0.9934           | 0.9977              | 0.9525        | 0.9963             | 0.9915         | 0.9388   | 0.9909        |
+| 0.0181        | 6.3636 | 1050 | 0.0157          | 0.9759   | 0.9853        | 0.9938           | 0.9973              | 0.9628        | 0.9959             | 0.9924         | 0.9443   | 0.9909        |
+| 0.0138        | 6.6667 | 1100 | 0.0143          | 0.9781   | 0.9907        | 0.9943           | 0.9966              | 0.9811        | 0.9945             | 0.9926         | 0.9501   | 0.9917        |
+| 0.0287        | 6.9697 | 1150 | 0.0161          | 0.9747   | 0.9875        | 0.9934           | 0.9976              | 0.9714        | 0.9935             | 0.9920         | 0.9420   | 0.9900        |
+| 0.0144        | 7.2727 | 1200 | 0.0149          | 0.9774   | 0.9894        | 0.9940           | 0.9974              | 0.9771        | 0.9938             | 0.9920         | 0.9493   | 0.9909        |
+| 0.012         | 7.5758 | 1250 | 0.0139          | 0.9783   | 0.9906        | 0.9943           | 0.9971              | 0.9805        | 0.9942             | 0.9929         | 0.9506   | 0.9915        |
+| 0.0098        | 7.8788 | 1300 | 0.0134          | 0.9793   | 0.9901        | 0.9945           | 0.9976              | 0.9782        | 0.9945             | 0.9927         | 0.9533   | 0.9918        |
+| 0.0105        | 8.1818 | 1350 | 0.0182          | 0.9780   | 0.9895        | 0.9942           | 0.9971              | 0.9768        | 0.9946             | 0.9926         | 0.9500   | 0.9913        |
+| 0.014         | 8.4848 | 1400 | 0.0141          | 0.9784   | 0.9896        | 0.9943           | 0.9969              | 0.9769        | 0.9948             | 0.9924         | 0.9512   | 0.9916        |
+| 0.0117        | 8.7879 | 1450 | 0.0154          | 0.9767   | 0.9911        | 0.9938           | 0.9968              | 0.9834        | 0.9930             | 0.9917         | 0.9477   | 0.9908        |
+| 0.0153        | 9.0909 | 1500 | 0.0143          | 0.9789   | 0.9908        | 0.9945           | 0.9964              | 0.9810        | 0.9951             | 0.9930         | 0.9518   | 0.9919        |
+### 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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