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--- |
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license: other |
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tags: |
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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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3547 |
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- Mean Iou: 0.3725 |
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- Mean Accuracy: 0.7265 |
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- Overall Accuracy: 0.8226 |
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- Accuracy Unlabeled: nan |
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- Accuracy Tool: 0.6195 |
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- Accuracy Wear: 0.8334 |
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- Iou Unlabeled: 0.0 |
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- Iou Tool: 0.2973 |
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- Iou Wear: 0.8202 |
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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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| 0.7196 | 1.82 | 20 | 0.9873 | 0.2927 | 0.4996 | 0.6806 | nan | 0.2982 | 0.7009 | 0.0 | 0.2025 | 0.6757 | |
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| 0.6004 | 3.64 | 40 | 0.7373 | 0.3312 | 0.6517 | 0.7107 | nan | 0.5861 | 0.7173 | 0.0 | 0.2916 | 0.7019 | |
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| 0.5155 | 5.45 | 60 | 0.6634 | 0.3376 | 0.5621 | 0.6378 | nan | 0.4778 | 0.6463 | 0.0 | 0.3840 | 0.6289 | |
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| 0.4228 | 7.27 | 80 | 0.5380 | 0.3612 | 0.6707 | 0.7661 | nan | 0.5646 | 0.7768 | 0.0 | 0.3241 | 0.7595 | |
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| 0.3216 | 9.09 | 100 | 0.5102 | 0.3466 | 0.6845 | 0.7281 | nan | 0.6361 | 0.7330 | 0.0 | 0.3188 | 0.7209 | |
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| 0.3752 | 10.91 | 120 | 0.4615 | 0.3902 | 0.7013 | 0.8268 | nan | 0.5616 | 0.8409 | 0.0 | 0.3476 | 0.8229 | |
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| 0.3014 | 12.73 | 140 | 0.4504 | 0.4075 | 0.7007 | 0.8311 | nan | 0.5558 | 0.8457 | 0.0 | 0.3949 | 0.8275 | |
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| 0.2183 | 14.55 | 160 | 0.4241 | 0.3708 | 0.7363 | 0.8002 | nan | 0.6653 | 0.8073 | 0.0 | 0.3165 | 0.7959 | |
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| 0.1674 | 16.36 | 180 | 0.4173 | 0.4020 | 0.7433 | 0.8684 | nan | 0.6041 | 0.8824 | 0.0 | 0.3397 | 0.8664 | |
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| 0.2385 | 18.18 | 200 | 0.4716 | 0.3450 | 0.6543 | 0.7462 | nan | 0.5520 | 0.7566 | 0.0 | 0.2941 | 0.7410 | |
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| 0.1588 | 20.0 | 220 | 0.3742 | 0.3820 | 0.7108 | 0.8179 | nan | 0.5917 | 0.8299 | 0.0 | 0.3311 | 0.8149 | |
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| 0.1553 | 21.82 | 240 | 0.3677 | 0.3811 | 0.7312 | 0.8313 | nan | 0.6199 | 0.8426 | 0.0 | 0.3144 | 0.8291 | |
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| 0.1765 | 23.64 | 260 | 0.4131 | 0.3689 | 0.7032 | 0.8024 | nan | 0.5929 | 0.8135 | 0.0 | 0.3082 | 0.7985 | |
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| 0.2516 | 25.45 | 280 | 0.3632 | 0.4142 | 0.7158 | 0.8856 | nan | 0.5270 | 0.9047 | 0.0 | 0.3585 | 0.8841 | |
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| 0.1534 | 27.27 | 300 | 0.3979 | 0.3813 | 0.7191 | 0.8236 | nan | 0.6029 | 0.8354 | 0.0 | 0.3231 | 0.8209 | |
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| 0.1104 | 29.09 | 320 | 0.3787 | 0.3640 | 0.7439 | 0.8044 | nan | 0.6765 | 0.8112 | 0.0 | 0.2911 | 0.8007 | |
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| 0.1799 | 30.91 | 340 | 0.3654 | 0.3868 | 0.7217 | 0.8257 | nan | 0.6060 | 0.8374 | 0.0 | 0.3378 | 0.8227 | |
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| 0.1069 | 32.73 | 360 | 0.3928 | 0.3524 | 0.7171 | 0.7606 | nan | 0.6687 | 0.7655 | 0.0 | 0.3018 | 0.7554 | |
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| 0.1178 | 34.55 | 380 | 0.3703 | 0.3622 | 0.7259 | 0.8079 | nan | 0.6345 | 0.8172 | 0.0 | 0.2814 | 0.8052 | |
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| 0.1191 | 36.36 | 400 | 0.3636 | 0.3766 | 0.7396 | 0.8264 | nan | 0.6431 | 0.8361 | 0.0 | 0.3069 | 0.8230 | |
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| 0.2008 | 38.18 | 420 | 0.3836 | 0.3685 | 0.7249 | 0.7907 | nan | 0.6516 | 0.7981 | 0.0 | 0.3194 | 0.7860 | |
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| 0.0846 | 40.0 | 440 | 0.3602 | 0.3738 | 0.7285 | 0.8244 | nan | 0.6218 | 0.8352 | 0.0 | 0.2994 | 0.8219 | |
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| 0.1178 | 41.82 | 460 | 0.3631 | 0.3751 | 0.7224 | 0.8311 | nan | 0.6015 | 0.8433 | 0.0 | 0.2964 | 0.8288 | |
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| 0.0806 | 43.64 | 480 | 0.3631 | 0.3678 | 0.7233 | 0.8074 | nan | 0.6297 | 0.8169 | 0.0 | 0.2988 | 0.8045 | |
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| 0.1102 | 45.45 | 500 | 0.3731 | 0.3686 | 0.7113 | 0.8067 | nan | 0.6053 | 0.8174 | 0.0 | 0.3025 | 0.8032 | |
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| 0.0751 | 47.27 | 520 | 0.3671 | 0.3682 | 0.7249 | 0.8117 | nan | 0.6283 | 0.8215 | 0.0 | 0.2959 | 0.8085 | |
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| 0.1272 | 49.09 | 540 | 0.3547 | 0.3725 | 0.7265 | 0.8226 | nan | 0.6195 | 0.8334 | 0.0 | 0.2973 | 0.8202 | |
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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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