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--- |
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license: other |
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base_model: nvidia/mit-b5 |
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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_Grayscale_mit-b5_Grayscale |
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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_Grayscale_mit-b5_Grayscale |
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This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the Hasano20/Clean_Set1_Grayscale dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0178 |
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- Mean Iou: 0.9760 |
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- Mean Accuracy: 0.9847 |
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- Overall Accuracy: 0.9949 |
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- Accuracy Background: 0.9976 |
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- Accuracy Melt: 0.9586 |
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- Accuracy Substrate: 0.9978 |
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- Iou Background: 0.9959 |
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- Iou Melt: 0.9408 |
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- Iou Substrate: 0.9912 |
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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: 8 |
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- eval_batch_size: 8 |
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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: cosine |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 50 |
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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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| 0.0882 | 5.5556 | 50 | 0.1733 | 0.7543 | 0.7985 | 0.9397 | 0.9594 | 0.4375 | 0.9986 | 0.9562 | 0.4189 | 0.8880 | |
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| 0.0295 | 11.1111 | 100 | 0.0270 | 0.9580 | 0.9736 | 0.9907 | 0.9965 | 0.9302 | 0.9940 | 0.9918 | 0.8978 | 0.9843 | |
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| 0.0143 | 16.6667 | 150 | 0.0260 | 0.9561 | 0.9798 | 0.9901 | 0.9970 | 0.9541 | 0.9884 | 0.9910 | 0.8938 | 0.9836 | |
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| 0.0095 | 22.2222 | 200 | 0.0224 | 0.9645 | 0.9747 | 0.9926 | 0.9985 | 0.9293 | 0.9962 | 0.9944 | 0.9119 | 0.9872 | |
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| 0.0083 | 27.7778 | 250 | 0.0180 | 0.9742 | 0.9819 | 0.9945 | 0.9982 | 0.9498 | 0.9977 | 0.9955 | 0.9366 | 0.9905 | |
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| 0.0072 | 33.3333 | 300 | 0.0175 | 0.9751 | 0.9838 | 0.9947 | 0.9984 | 0.9563 | 0.9968 | 0.9957 | 0.9388 | 0.9909 | |
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| 0.0073 | 38.8889 | 350 | 0.0177 | 0.9758 | 0.9854 | 0.9948 | 0.9970 | 0.9613 | 0.9978 | 0.9957 | 0.9406 | 0.9912 | |
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| 0.0054 | 44.4444 | 400 | 0.0179 | 0.9758 | 0.9844 | 0.9949 | 0.9978 | 0.9579 | 0.9976 | 0.9959 | 0.9404 | 0.9911 | |
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| 0.0052 | 50.0 | 450 | 0.0178 | 0.9760 | 0.9847 | 0.9949 | 0.9976 | 0.9586 | 0.9978 | 0.9959 | 0.9408 | 0.9912 | |
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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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