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README.md +93 -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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-## How to Get Started with the Model
-Use the code below to get started with the model.
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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_RGB
+  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_RGB
+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: 0.0207
+- Mean Iou: 0.9744
+- Mean Accuracy: 0.9865
+- Overall Accuracy: 0.9940
+- Accuracy Background: 0.9965
+- Accuracy Melt: 0.9672
+- Accuracy Substrate: 0.9957
+- Iou Background: 0.9938
+- Iou Melt: 0.9389
+- Iou Substrate: 0.9905
+## 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: 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: 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 |
+|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:|
+| 0.3016        | 0.9434  | 50   | 0.2259          | 0.6885   | 0.7339        | 0.9268           | 0.9683              | 0.2451        | 0.9882             | 0.9455         | 0.2365   | 0.8834        |
+| 0.1267        | 1.8868  | 100  | 0.1062          | 0.8505   | 0.9168        | 0.9620           | 0.9849              | 0.7996        | 0.9660             | 0.9706         | 0.6411   | 0.9398        |
+| 0.0982        | 2.8302  | 150  | 0.0765          | 0.8725   | 0.9003        | 0.9718           | 0.9905              | 0.7183        | 0.9920             | 0.9803         | 0.6829   | 0.9544        |
+| 0.0626        | 3.7736  | 200  | 0.0596          | 0.9124   | 0.9496        | 0.9793           | 0.9921              | 0.8731        | 0.9836             | 0.9824         | 0.7879   | 0.9668        |
+| 0.0601        | 4.7170  | 250  | 0.0776          | 0.8931   | 0.9394        | 0.9733           | 0.9814              | 0.8536        | 0.9834             | 0.9762         | 0.7466   | 0.9566        |
+| 0.0662        | 5.6604  | 300  | 0.0548          | 0.9176   | 0.9660        | 0.9803           | 0.9919              | 0.9280        | 0.9781             | 0.9875         | 0.7993   | 0.9662        |
+| 0.0297        | 6.6038  | 350  | 0.0353          | 0.9452   | 0.9791        | 0.9872           | 0.9918              | 0.9581        | 0.9875             | 0.9895         | 0.8670   | 0.9792        |
+| 0.0197        | 7.5472  | 400  | 0.0422          | 0.9332   | 0.9520        | 0.9853           | 0.9949              | 0.8670        | 0.9940             | 0.9899         | 0.8343   | 0.9753        |
+| 0.0274        | 8.4906  | 450  | 0.0281          | 0.9589   | 0.9783        | 0.9904           | 0.9944              | 0.9475        | 0.9932             | 0.9913         | 0.9012   | 0.9843        |
+| 0.0197        | 9.4340  | 500  | 0.0280          | 0.9569   | 0.9792        | 0.9901           | 0.9965              | 0.9507        | 0.9904             | 0.9920         | 0.8950   | 0.9836        |
+| 0.0185        | 10.3774 | 550  | 0.0230          | 0.9644   | 0.9819        | 0.9918           | 0.9961              | 0.9564        | 0.9931             | 0.9923         | 0.9142   | 0.9867        |
+| 0.0131        | 11.3208 | 600  | 0.0248          | 0.9663   | 0.9788        | 0.9922           | 0.9951              | 0.9449        | 0.9964             | 0.9922         | 0.9192   | 0.9874        |
+| 0.0123        | 12.2642 | 650  | 0.0229          | 0.9682   | 0.9784        | 0.9926           | 0.9957              | 0.9424        | 0.9972             | 0.9931         | 0.9236   | 0.9879        |
+| 0.0094        | 13.2075 | 700  | 0.0220          | 0.9673   | 0.9811        | 0.9925           | 0.9962              | 0.9519        | 0.9951             | 0.9930         | 0.9209   | 0.9878        |
+| 0.0092        | 14.1509 | 750  | 0.0198          | 0.9721   | 0.9845        | 0.9935           | 0.9962              | 0.9617        | 0.9956             | 0.9933         | 0.9334   | 0.9895        |
+| 0.0119        | 15.0943 | 800  | 0.0210          | 0.9688   | 0.9828        | 0.9928           | 0.9971              | 0.9571        | 0.9943             | 0.9932         | 0.9250   | 0.9883        |
+| 0.0092        | 16.0377 | 850  | 0.0220          | 0.9688   | 0.9819        | 0.9928           | 0.9959              | 0.9543        | 0.9957             | 0.9929         | 0.9249   | 0.9885        |
+| 0.0092        | 16.9811 | 900  | 0.0186          | 0.9718   | 0.9859        | 0.9934           | 0.9965              | 0.9666        | 0.9947             | 0.9936         | 0.9324   | 0.9894        |
+| 0.0069        | 17.9245 | 950  | 0.0201          | 0.9725   | 0.9831        | 0.9936           | 0.9963              | 0.9564        | 0.9967             | 0.9937         | 0.9341   | 0.9898        |
+| 0.011         | 18.8679 | 1000 | 0.0190          | 0.9742   | 0.9851        | 0.9939           | 0.9962              | 0.9628        | 0.9964             | 0.9937         | 0.9388   | 0.9903        |
+| 0.009         | 19.8113 | 1050 | 0.0219          | 0.9714   | 0.9855        | 0.9933           | 0.9972              | 0.9652        | 0.9940             | 0.9936         | 0.9314   | 0.9891        |
+| 0.0086        | 20.7547 | 1100 | 0.0199          | 0.9737   | 0.9872        | 0.9938           | 0.9961              | 0.9702        | 0.9953             | 0.9937         | 0.9373   | 0.9901        |
+| 0.0086        | 21.6981 | 1150 | 0.0206          | 0.9737   | 0.9850        | 0.9938           | 0.9957              | 0.9625        | 0.9967             | 0.9936         | 0.9372   | 0.9902        |
+| 0.0052        | 22.6415 | 1200 | 0.0205          | 0.9737   | 0.9866        | 0.9939           | 0.9960              | 0.9682        | 0.9957             | 0.9936         | 0.9372   | 0.9903        |
+| 0.0079        | 23.5849 | 1250 | 0.0205          | 0.9745   | 0.9861        | 0.9940           | 0.9962              | 0.9658        | 0.9962             | 0.9937         | 0.9393   | 0.9905        |
+| 0.0057        | 24.5283 | 1300 | 0.0210          | 0.9746   | 0.9849        | 0.9940           | 0.9961              | 0.9618        | 0.9968             | 0.9938         | 0.9397   | 0.9904        |
+| 0.007         | 25.4717 | 1350 | 0.0212          | 0.9735   | 0.9858        | 0.9938           | 0.9963              | 0.9652        | 0.9957             | 0.9936         | 0.9369   | 0.9901        |
+| 0.0059        | 26.4151 | 1400 | 0.0207          | 0.9744   | 0.9865        | 0.9940           | 0.9965              | 0.9672        | 0.9957             | 0.9938         | 0.9389   | 0.9905        |
+### 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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