End of training

README.md

@@ -1,199 +1,85 @@
 ---
-library_name: transformers
-tags: []
+license: other
+base_model: nvidia/mit-b5
+tags:
+- vision
+- image-segmentation
+- generated_from_trainer
+model-index:
+- name: SegFormer_mit-b5_Final-Set4-Grayscale_Not-Augmented_4_lr0.0001
+  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. -->
-
-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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+<!-- 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_Final-Set4-Grayscale_Not-Augmented_4_lr0.0001
+
+This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the Hasano20/Final-Set4-Grayscale dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.0217
+- Mean Iou: 0.9708
+- Mean Accuracy: 0.9835
+- Overall Accuracy: 0.9941
+- Accuracy Background: 0.9965
+- Accuracy Melt: 0.9584
+- Accuracy Substrate: 0.9957
+- Iou Background: 0.9940
+- Iou Melt: 0.9288
+- Iou Substrate: 0.9895
+
+## 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
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 100
+- num_epochs: 20
+- mixed_precision_training: Native AMP
+
+### 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 |
+|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
+| 0.1107        | 0.8850  | 50   | 0.1152          | 0.8138   | 0.8439        | 0.9627           | 0.9781              | 0.5623        | 0.9914             | 0.9677         | 0.5412   | 0.9325        |
+| 0.0564        | 1.7699  | 100  | 0.0520          | 0.9163   | 0.9432        | 0.9829           | 0.9967              | 0.8488        | 0.9841             | 0.9806         | 0.7963   | 0.9721        |
+| 0.0296        | 2.6549  | 150  | 0.0270          | 0.9557   | 0.9821        | 0.9906           | 0.9916              | 0.9621        | 0.9928             | 0.9893         | 0.8939   | 0.9839        |
+| 0.042         | 3.5398  | 200  | 0.0226          | 0.9619   | 0.9763        | 0.9922           | 0.9934              | 0.9384        | 0.9969             | 0.9917         | 0.9077   | 0.9862        |
+| 0.0166        | 4.4248  | 250  | 0.0300          | 0.9616   | 0.9768        | 0.9904           | 0.9957              | 0.9446        | 0.9903             | 0.9872         | 0.9153   | 0.9823        |
+| 0.0159        | 5.3097  | 300  | 0.0203          | 0.9658   | 0.9863        | 0.9931           | 0.9946              | 0.9701        | 0.9941             | 0.9923         | 0.9169   | 0.9883        |
+| 0.0121        | 6.1947  | 350  | 0.0221          | 0.9645   | 0.9795        | 0.9928           | 0.9937              | 0.9480        | 0.9968             | 0.9923         | 0.9141   | 0.9872        |
+| 0.0149        | 7.0796  | 400  | 0.0220          | 0.9648   | 0.9821        | 0.9930           | 0.9949              | 0.9565        | 0.9951             | 0.9930         | 0.9138   | 0.9874        |
+| 0.0352        | 7.9646  | 450  | 0.0215          | 0.9658   | 0.9764        | 0.9933           | 0.9959              | 0.9361        | 0.9971             | 0.9935         | 0.9158   | 0.9880        |
+| 0.0106        | 8.8496  | 500  | 0.0201          | 0.9696   | 0.9820        | 0.9939           | 0.9961              | 0.9535        | 0.9962             | 0.9938         | 0.9256   | 0.9892        |
+| 0.0095        | 9.7345  | 550  | 0.0216          | 0.9674   | 0.9796        | 0.9936           | 0.9955              | 0.9463        | 0.9969             | 0.9936         | 0.9202   | 0.9886        |
+| 0.009         | 10.6195 | 600  | 0.0209          | 0.9702   | 0.9821        | 0.9941           | 0.9966              | 0.9539        | 0.9960             | 0.9940         | 0.9273   | 0.9894        |
+| 0.0106        | 11.5044 | 650  | 0.0211          | 0.9700   | 0.9830        | 0.9940           | 0.9964              | 0.9568        | 0.9958             | 0.9940         | 0.9266   | 0.9893        |
+| 0.0099        | 12.3894 | 700  | 0.0217          | 0.9708   | 0.9835        | 0.9941           | 0.9965              | 0.9584        | 0.9957             | 0.9940         | 0.9288   | 0.9895        |
+
+
+### 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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