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_On-the-fly-Augmented_batch8_lr0.0002
+  results: []
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
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-
-
-## 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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-## 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_On-the-fly-Augmented_batch8_lr0.0002
+
+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: 1.6332
+- Mean Iou: 0.2042
+- Mean Accuracy: 0.3547
+- Overall Accuracy: 0.5005
+- Accuracy Background: 0.8894
+- Accuracy Melt: 0.0
+- Accuracy Substrate: 0.1746
+- Iou Background: 0.4549
+- Iou Melt: 0.0
+- Iou Substrate: 0.1576
+
+## 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
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 100
+- num_epochs: 25
+- 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 |
+|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
+| 0.0948        | 1.7699  | 50   | 3.2172          | 0.1562   | 0.3333        | 0.4687           | 1.0                 | 0.0           | 0.0                | 0.4687         | 0.0      | 0.0           |
+| 0.0735        | 3.5398  | 100  | 3.5520          | 0.1562   | 0.3333        | 0.4687           | 1.0                 | 0.0           | 0.0                | 0.4687         | 0.0      | 0.0           |
+| 0.064         | 5.3097  | 150  | 3.4135          | 0.1562   | 0.3333        | 0.4687           | 1.0                 | 0.0           | 0.0                | 0.4687         | 0.0      | 0.0           |
+| 0.062         | 7.0796  | 200  | 3.2473          | 0.1594   | 0.3297        | 0.4638           | 0.9688              | 0.0           | 0.0203             | 0.4585         | 0.0      | 0.0197        |
+| 0.0672        | 8.8496  | 250  | 1.3897          | 0.1861   | 0.3555        | 0.5006           | 0.9884              | 0.0           | 0.0780             | 0.4812         | 0.0      | 0.0771        |
+| 0.0423        | 10.6195 | 300  | 1.3204          | 0.1603   | 0.2938        | 0.4144           | 0.7495              | 0.0           | 0.1318             | 0.3750         | 0.0      | 0.1058        |
+| 0.044         | 12.3894 | 350  | 1.2021          | 0.2482   | 0.3864        | 0.5467           | 0.8290              | 0.0           | 0.3303             | 0.4616         | 0.0      | 0.2829        |
+| 0.0322        | 14.1593 | 400  | 1.5121          | 0.2118   | 0.3578        | 0.5052           | 0.8625              | 0.0           | 0.2108             | 0.4497         | 0.0      | 0.1858        |
+| 0.0291        | 15.9292 | 450  | 1.6387          | 0.1855   | 0.3411        | 0.4808           | 0.9079              | 0.0           | 0.1155             | 0.4504         | 0.0      | 0.1059        |
+| 0.0235        | 17.6991 | 500  | 1.6660          | 0.1874   | 0.3481        | 0.4906           | 0.9404              | 0.0           | 0.1040             | 0.4639         | 0.0      | 0.0982        |
+| 0.0243        | 19.4690 | 550  | 1.5501          | 0.2051   | 0.3521        | 0.4970           | 0.8649              | 0.0           | 0.1913             | 0.4463         | 0.0      | 0.1690        |
+| 0.0225        | 21.2389 | 600  | 1.7049          | 0.1982   | 0.3497        | 0.4934           | 0.8914              | 0.0           | 0.1578             | 0.4520         | 0.0      | 0.1427        |
+| 0.0265        | 23.0088 | 650  | 1.6788          | 0.2008   | 0.3531        | 0.4982           | 0.8989              | 0.0           | 0.1606             | 0.4564         | 0.0      | 0.1461        |
+| 0.0214        | 24.7788 | 700  | 1.6332          | 0.2042   | 0.3547        | 0.5005           | 0.8894              | 0.0           | 0.1746             | 0.4549         | 0.0      | 0.1576        |
+
+
+### 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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