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--- |
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license: other |
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base_model: nvidia/mit-b4 |
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tags: |
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- vision |
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- image-segmentation |
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- generated_from_trainer |
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model-index: |
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- name: segformer_Clean_Set1_95images |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# segformer_Clean_Set1_95images |
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This model is a fine-tuned version of [nvidia/mit-b4](https://huggingface.co/nvidia/mit-b4) on the Hasano20/Clean_Set1_95images dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0223 |
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- Mean Iou: 0.6447 |
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- Mean Accuracy: 0.9824 |
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- Overall Accuracy: 0.9886 |
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- Accuracy Background: nan |
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- Accuracy Melt: 0.9724 |
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- Accuracy Substrate: 0.9923 |
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- Iou Background: 0.0 |
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- Iou Melt: 0.9458 |
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- Iou Substrate: 0.9882 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Background | Accuracy Melt | Accuracy Substrate | Iou Background | Iou Melt | Iou Substrate | |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:-------------:|:------------------:|:--------------:|:--------:|:-------------:| |
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| 0.2051 | 1.1765 | 20 | 0.3764 | 0.3339 | 0.5766 | 0.8354 | nan | 0.1639 | 0.9894 | 0.0 | 0.1612 | 0.8404 | |
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| 0.3486 | 2.3529 | 40 | 0.1932 | 0.4595 | 0.7687 | 0.8745 | nan | 0.6000 | 0.9375 | 0.0 | 0.4928 | 0.8858 | |
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| 0.0831 | 3.5294 | 60 | 0.2016 | 0.4101 | 0.6782 | 0.8792 | nan | 0.3576 | 0.9988 | 0.0 | 0.3570 | 0.8732 | |
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| 0.0809 | 4.7059 | 80 | 0.0763 | 0.5787 | 0.9243 | 0.9507 | nan | 0.8822 | 0.9664 | 0.0 | 0.7830 | 0.9531 | |
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| 0.0325 | 5.8824 | 100 | 0.0694 | 0.6028 | 0.9436 | 0.9618 | nan | 0.9146 | 0.9727 | 0.0 | 0.8479 | 0.9606 | |
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| 0.0279 | 7.0588 | 120 | 0.0460 | 0.6142 | 0.9520 | 0.9712 | nan | 0.9213 | 0.9826 | 0.0 | 0.8739 | 0.9686 | |
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| 0.0493 | 8.2353 | 140 | 0.0353 | 0.6297 | 0.9648 | 0.9802 | nan | 0.9404 | 0.9893 | 0.0 | 0.9092 | 0.9797 | |
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| 0.0286 | 9.4118 | 160 | 0.0366 | 0.6261 | 0.9643 | 0.9765 | nan | 0.9449 | 0.9837 | 0.0 | 0.8997 | 0.9787 | |
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| 0.0463 | 10.5882 | 180 | 0.0258 | 0.6425 | 0.9798 | 0.9879 | nan | 0.9669 | 0.9927 | 0.0 | 0.9414 | 0.9862 | |
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| 0.0145 | 11.7647 | 200 | 0.0302 | 0.6324 | 0.9652 | 0.9821 | nan | 0.9382 | 0.9922 | 0.0 | 0.9162 | 0.9810 | |
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| 0.0221 | 12.9412 | 220 | 0.0262 | 0.6379 | 0.9733 | 0.9850 | nan | 0.9547 | 0.9919 | 0.0 | 0.9289 | 0.9848 | |
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| 0.0109 | 14.1176 | 240 | 0.0236 | 0.6417 | 0.9764 | 0.9869 | nan | 0.9595 | 0.9932 | 0.0 | 0.9379 | 0.9871 | |
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| 0.0122 | 15.2941 | 260 | 0.0252 | 0.6407 | 0.9812 | 0.9866 | nan | 0.9725 | 0.9898 | 0.0 | 0.9358 | 0.9864 | |
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| 0.0101 | 16.4706 | 280 | 0.0239 | 0.6417 | 0.9799 | 0.9869 | nan | 0.9686 | 0.9911 | 0.0 | 0.9382 | 0.9870 | |
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| 0.0113 | 17.6471 | 300 | 0.0231 | 0.6425 | 0.9798 | 0.9874 | nan | 0.9675 | 0.9920 | 0.0 | 0.9399 | 0.9875 | |
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| 0.0086 | 18.8235 | 320 | 0.0225 | 0.6444 | 0.9826 | 0.9885 | nan | 0.9733 | 0.9919 | 0.0 | 0.9451 | 0.9882 | |
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| 0.0086 | 20.0 | 340 | 0.0223 | 0.6447 | 0.9824 | 0.9886 | nan | 0.9724 | 0.9923 | 0.0 | 0.9458 | 0.9882 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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