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--- |
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license: other |
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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-b0-finetuned-segments-toolwear |
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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-b0-finetuned-segments-toolwear |
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This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co./nvidia/mit-b0) on the HorcruxNo13/toolwear_segmentsai dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1285 |
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- Mean Iou: 0.3499 |
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- Mean Accuracy: 0.6998 |
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- Overall Accuracy: 0.6998 |
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- Accuracy Unlabeled: nan |
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- Accuracy Tool: nan |
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- Accuracy Wear: 0.6998 |
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- Iou Unlabeled: 0.0 |
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- Iou Tool: nan |
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- Iou Wear: 0.6998 |
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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: 6e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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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: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Tool | Accuracy Wear | Iou Unlabeled | Iou Tool | Iou Wear | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:-------------:|:--------:|:--------:| |
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| 0.8959 | 1.82 | 20 | 0.8677 | 0.4048 | 0.8097 | 0.8097 | nan | nan | 0.8097 | 0.0 | nan | 0.8097 | |
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| 0.6658 | 3.64 | 40 | 0.6010 | 0.3734 | 0.7468 | 0.7468 | nan | nan | 0.7468 | 0.0 | nan | 0.7468 | |
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| 0.4389 | 5.45 | 60 | 0.4941 | 0.3634 | 0.7269 | 0.7269 | nan | nan | 0.7269 | 0.0 | nan | 0.7269 | |
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| 0.3531 | 7.27 | 80 | 0.4390 | 0.3508 | 0.7015 | 0.7015 | nan | nan | 0.7015 | 0.0 | nan | 0.7015 | |
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| 0.3408 | 9.09 | 100 | 0.3753 | 0.3340 | 0.6679 | 0.6679 | nan | nan | 0.6679 | 0.0 | nan | 0.6679 | |
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| 0.3266 | 10.91 | 120 | 0.3769 | 0.3761 | 0.7521 | 0.7521 | nan | nan | 0.7521 | 0.0 | nan | 0.7521 | |
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| 0.2791 | 12.73 | 140 | 0.3491 | 0.3918 | 0.7835 | 0.7835 | nan | nan | 0.7835 | 0.0 | nan | 0.7835 | |
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| 0.2066 | 14.55 | 160 | 0.2705 | 0.3491 | 0.6981 | 0.6981 | nan | nan | 0.6981 | 0.0 | nan | 0.6981 | |
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| 0.161 | 16.36 | 180 | 0.2398 | 0.3283 | 0.6567 | 0.6567 | nan | nan | 0.6567 | 0.0 | nan | 0.6567 | |
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| 0.1558 | 18.18 | 200 | 0.2599 | 0.4021 | 0.8042 | 0.8042 | nan | nan | 0.8042 | 0.0 | nan | 0.8042 | |
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| 0.128 | 20.0 | 220 | 0.2163 | 0.3387 | 0.6775 | 0.6775 | nan | nan | 0.6775 | 0.0 | nan | 0.6775 | |
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| 0.11 | 21.82 | 240 | 0.2019 | 0.3599 | 0.7199 | 0.7199 | nan | nan | 0.7199 | 0.0 | nan | 0.7199 | |
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| 0.1101 | 23.64 | 260 | 0.1905 | 0.3620 | 0.7240 | 0.7240 | nan | nan | 0.7240 | 0.0 | nan | 0.7240 | |
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| 0.0874 | 25.45 | 280 | 0.1708 | 0.3138 | 0.6276 | 0.6276 | nan | nan | 0.6276 | 0.0 | nan | 0.6276 | |
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| 0.0815 | 27.27 | 300 | 0.1505 | 0.3191 | 0.6382 | 0.6382 | nan | nan | 0.6382 | 0.0 | nan | 0.6382 | |
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| 0.082 | 29.09 | 320 | 0.1641 | 0.3520 | 0.7040 | 0.7040 | nan | nan | 0.7040 | 0.0 | nan | 0.7040 | |
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| 0.0694 | 30.91 | 340 | 0.1456 | 0.3322 | 0.6644 | 0.6644 | nan | nan | 0.6644 | 0.0 | nan | 0.6644 | |
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| 0.072 | 32.73 | 360 | 0.1416 | 0.3445 | 0.6889 | 0.6889 | nan | nan | 0.6889 | 0.0 | nan | 0.6889 | |
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| 0.065 | 34.55 | 380 | 0.1348 | 0.3407 | 0.6814 | 0.6814 | nan | nan | 0.6814 | 0.0 | nan | 0.6814 | |
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| 0.0696 | 36.36 | 400 | 0.1372 | 0.3285 | 0.6569 | 0.6569 | nan | nan | 0.6569 | 0.0 | nan | 0.6569 | |
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| 0.0666 | 38.18 | 420 | 0.1430 | 0.3636 | 0.7272 | 0.7272 | nan | nan | 0.7272 | 0.0 | nan | 0.7272 | |
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| 0.0601 | 40.0 | 440 | 0.1222 | 0.3211 | 0.6423 | 0.6423 | nan | nan | 0.6423 | 0.0 | nan | 0.6423 | |
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| 0.0515 | 41.82 | 460 | 0.1225 | 0.3286 | 0.6572 | 0.6572 | nan | nan | 0.6572 | 0.0 | nan | 0.6572 | |
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| 0.0558 | 43.64 | 480 | 0.1229 | 0.3375 | 0.6750 | 0.6750 | nan | nan | 0.6750 | 0.0 | nan | 0.6750 | |
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| 0.07 | 45.45 | 500 | 0.1111 | 0.3057 | 0.6114 | 0.6114 | nan | nan | 0.6114 | 0.0 | nan | 0.6114 | |
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| 0.0606 | 47.27 | 520 | 0.1251 | 0.3391 | 0.6782 | 0.6782 | nan | nan | 0.6782 | 0.0 | nan | 0.6782 | |
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| 0.0561 | 49.09 | 540 | 0.1285 | 0.3499 | 0.6998 | 0.6998 | nan | nan | 0.6998 | 0.0 | nan | 0.6998 | |
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### Framework versions |
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- Transformers 4.28.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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