End of training

README.md

@@ -1,199 +1,90 @@
 ---
-library_name: transformers
-tags: []
+license: other
+base_model: nvidia/mit-b5
+tags:
+- vision
+- image-segmentation
+- generated_from_trainer
+model-index:
+- name: SegFormer_mit-b5_Clean-Set3-Grayscale_Augmented_Medium_16
+  results: []
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
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-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
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-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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+<!-- 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_Medium_16
+
+This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the Hasano20/Clean-Set3 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0121
+- Mean Iou: 0.9823
+- Mean Accuracy: 0.9920
+- Overall Accuracy: 0.9954
+- Accuracy Background: 0.9974
+- Accuracy Melt: 0.9828
+- Accuracy Substrate: 0.9958
+- Iou Background: 0.9943
+- Iou Melt: 0.9594
+- Iou Substrate: 0.9932
+
+## 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: 16
+- eval_batch_size: 16
+- 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: 20
+
+### 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 |
+|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
+| 0.1196        | 0.7937  | 50   | 0.1076          | 0.8582   | 0.8965        | 0.9626           | 0.9674              | 0.7265        | 0.9955             | 0.9625         | 0.6696   | 0.9424        |
+| 0.2728        | 1.5873  | 100  | 0.0878          | 0.8762   | 0.9239        | 0.9665           | 0.9622              | 0.8161        | 0.9935             | 0.9611         | 0.7150   | 0.9525        |
+| 0.2668        | 2.3810  | 150  | 0.1131          | 0.8710   | 0.9238        | 0.9639           | 0.9971              | 0.8140        | 0.9602             | 0.9620         | 0.7076   | 0.9432        |
+| 0.0337        | 3.1746  | 200  | 0.0610          | 0.9173   | 0.9613        | 0.9778           | 0.9709              | 0.9208        | 0.9923             | 0.9685         | 0.8110   | 0.9723        |
+| 0.0443        | 3.9683  | 250  | 0.0295          | 0.9527   | 0.9665        | 0.9885           | 0.9924              | 0.9095        | 0.9977             | 0.9902         | 0.8867   | 0.9812        |
+| 0.0283        | 4.7619  | 300  | 0.0220          | 0.9652   | 0.9781        | 0.9915           | 0.9965              | 0.9429        | 0.9950             | 0.9910         | 0.9175   | 0.9871        |
+| 0.0166        | 5.5556  | 350  | 0.0193          | 0.9683   | 0.9837        | 0.9922           | 0.9972              | 0.9609        | 0.9929             | 0.9925         | 0.9249   | 0.9876        |
+| 0.0218        | 6.3492  | 400  | 0.0190          | 0.9691   | 0.9871        | 0.9922           | 0.9975              | 0.9730        | 0.9909             | 0.9919         | 0.9277   | 0.9879        |
+| 0.0178        | 7.1429  | 450  | 0.0157          | 0.9752   | 0.9853        | 0.9938           | 0.9981              | 0.9626        | 0.9951             | 0.9925         | 0.9424   | 0.9909        |
+| 0.0165        | 7.9365  | 500  | 0.0151          | 0.9771   | 0.9878        | 0.9941           | 0.9966              | 0.9711        | 0.9957             | 0.9931         | 0.9470   | 0.9911        |
+| 0.0136        | 8.7302  | 550  | 0.0137          | 0.9785   | 0.9902        | 0.9945           | 0.9955              | 0.9792        | 0.9959             | 0.9930         | 0.9508   | 0.9918        |
+| 0.0127        | 9.5238  | 600  | 0.0128          | 0.9798   | 0.9896        | 0.9948           | 0.9977              | 0.9758        | 0.9955             | 0.9937         | 0.9536   | 0.9923        |
+| 0.0117        | 10.3175 | 650  | 0.0123          | 0.9809   | 0.9895        | 0.9951           | 0.9974              | 0.9747        | 0.9964             | 0.9939         | 0.9561   | 0.9927        |
+| 0.011         | 11.1111 | 700  | 0.0125          | 0.9805   | 0.9923        | 0.9950           | 0.9974              | 0.9848        | 0.9946             | 0.9938         | 0.9552   | 0.9925        |
+| 0.0108        | 11.9048 | 750  | 0.0123          | 0.9809   | 0.9915        | 0.9951           | 0.9975              | 0.9818        | 0.9952             | 0.9940         | 0.9561   | 0.9926        |
+| 0.0135        | 12.6984 | 800  | 0.0126          | 0.9808   | 0.9920        | 0.9950           | 0.9979              | 0.9834        | 0.9946             | 0.9941         | 0.9558   | 0.9924        |
+| 0.0089        | 13.4921 | 850  | 0.0123          | 0.9814   | 0.9923        | 0.9952           | 0.9968              | 0.9844        | 0.9957             | 0.9940         | 0.9574   | 0.9929        |
+| 0.0077        | 14.2857 | 900  | 0.0119          | 0.9819   | 0.9911        | 0.9953           | 0.9976              | 0.9797        | 0.9959             | 0.9942         | 0.9586   | 0.9930        |
+| 0.0069        | 15.0794 | 950  | 0.0122          | 0.9822   | 0.9914        | 0.9954           | 0.9973              | 0.9807        | 0.9961             | 0.9943         | 0.9591   | 0.9931        |
+| 0.0069        | 15.8730 | 1000 | 0.0120          | 0.9822   | 0.9920        | 0.9954           | 0.9975              | 0.9828        | 0.9957             | 0.9944         | 0.9592   | 0.9931        |
+| 0.0089        | 16.6667 | 1050 | 0.0120          | 0.9824   | 0.9914        | 0.9955           | 0.9976              | 0.9807        | 0.9961             | 0.9943         | 0.9595   | 0.9932        |
+| 0.0072        | 17.4603 | 1100 | 0.0121          | 0.9823   | 0.9920        | 0.9954           | 0.9974              | 0.9828        | 0.9958             | 0.9943         | 0.9594   | 0.9932        |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.0.1+cu117
+- Datasets 2.19.2
+- Tokenizers 0.19.1

config.json

@@ -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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