End of training

README.md

@@ -1,199 +1,88 @@
 ---
-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
+  results: []
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
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-
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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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+<!-- 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
+
+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: 0.0156
+- Mean Iou: 0.9776
+- Mean Accuracy: 0.9882
+- Overall Accuracy: 0.9952
+- Accuracy Background: 0.9974
+- Accuracy Melt: 0.9708
+- Accuracy Substrate: 0.9963
+- Iou Background: 0.9942
+- Iou Melt: 0.9458
+- Iou Substrate: 0.9927
+
+## 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: 200
+- 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 |
+|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
+| 0.1206        | 1.8519  | 50   | 0.0898          | 0.8826   | 0.9277        | 0.9727           | 0.9809              | 0.8182        | 0.9840             | 0.9697         | 0.7209   | 0.9571        |
+| 0.0687        | 3.7037  | 100  | 0.0445          | 0.9291   | 0.9568        | 0.9845           | 0.9920              | 0.8888        | 0.9895             | 0.9833         | 0.8286   | 0.9754        |
+| 0.0457        | 5.5556  | 150  | 0.0413          | 0.9284   | 0.9428        | 0.9859           | 0.9938              | 0.8381        | 0.9966             | 0.9877         | 0.8204   | 0.9770        |
+| 0.0281        | 7.4074  | 200  | 0.0240          | 0.9592   | 0.9706        | 0.9914           | 0.9971              | 0.9198        | 0.9949             | 0.9900         | 0.9011   | 0.9865        |
+| 0.0234        | 9.2593  | 250  | 0.0179          | 0.9672   | 0.9810        | 0.9932           | 0.9960              | 0.9513        | 0.9957             | 0.9926         | 0.9195   | 0.9893        |
+| 0.0147        | 11.1111 | 300  | 0.0180          | 0.9672   | 0.9785        | 0.9932           | 0.9955              | 0.9429        | 0.9972             | 0.9925         | 0.9197   | 0.9893        |
+| 0.012         | 12.9630 | 350  | 0.0139          | 0.9748   | 0.9864        | 0.9946           | 0.9967              | 0.9664        | 0.9962             | 0.9936         | 0.9390   | 0.9918        |
+| 0.0104        | 14.8148 | 400  | 0.0138          | 0.9756   | 0.9890        | 0.9947           | 0.9972              | 0.9748        | 0.9949             | 0.9935         | 0.9413   | 0.9919        |
+| 0.0094        | 16.6667 | 450  | 0.0136          | 0.9767   | 0.9862        | 0.9950           | 0.9965              | 0.9646        | 0.9974             | 0.9940         | 0.9436   | 0.9924        |
+| 0.0101        | 18.5185 | 500  | 0.0135          | 0.9767   | 0.9867        | 0.9950           | 0.9974              | 0.9663        | 0.9964             | 0.9940         | 0.9438   | 0.9924        |
+| 0.0087        | 20.3704 | 550  | 0.0144          | 0.9764   | 0.9887        | 0.9949           | 0.9954              | 0.9736        | 0.9970             | 0.9935         | 0.9435   | 0.9923        |
+| 0.0078        | 22.2222 | 600  | 0.0145          | 0.9760   | 0.9885        | 0.9949           | 0.9967              | 0.9727        | 0.9960             | 0.9938         | 0.9417   | 0.9924        |
+| 0.0095        | 24.0741 | 650  | 0.0145          | 0.9753   | 0.9855        | 0.9948           | 0.9971              | 0.9626        | 0.9967             | 0.9939         | 0.9398   | 0.9921        |
+| 0.0073        | 25.9259 | 700  | 0.0145          | 0.9761   | 0.9892        | 0.9949           | 0.9965              | 0.9752        | 0.9960             | 0.9938         | 0.9419   | 0.9925        |
+| 0.009         | 27.7778 | 750  | 0.0143          | 0.9772   | 0.9891        | 0.9951           | 0.9958              | 0.9745        | 0.9970             | 0.9938         | 0.9451   | 0.9929        |
+| 0.0049        | 29.6296 | 800  | 0.0143          | 0.9782   | 0.9883        | 0.9953           | 0.9966              | 0.9713        | 0.9971             | 0.9942         | 0.9474   | 0.9929        |
+| 0.0075        | 31.4815 | 850  | 0.0153          | 0.9767   | 0.9886        | 0.9951           | 0.9967              | 0.9727        | 0.9963             | 0.9941         | 0.9434   | 0.9925        |
+| 0.008         | 33.3333 | 900  | 0.0155          | 0.9772   | 0.9876        | 0.9952           | 0.9970              | 0.9690        | 0.9968             | 0.9943         | 0.9447   | 0.9927        |
+| 0.0061        | 35.1852 | 950  | 0.0150          | 0.9777   | 0.9877        | 0.9953           | 0.9973              | 0.9691        | 0.9967             | 0.9943         | 0.9461   | 0.9928        |
+| 0.0053        | 37.0370 | 1000 | 0.0156          | 0.9776   | 0.9882        | 0.9952           | 0.9974              | 0.9708        | 0.9963             | 0.9942         | 0.9458   | 0.9927        |
+
+
+### 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"
+}

model.safetensors

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+size 338531516

training_args.bin

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