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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: 0.1291 |
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- Mean Iou: 0.4322 |
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- Mean Accuracy: 0.8644 |
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- Overall Accuracy: 0.8644 |
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- Accuracy Unlabeled: nan |
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- Accuracy Tool: nan |
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- Accuracy Wear: 0.8644 |
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- Iou Unlabeled: 0.0 |
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- Iou Tool: nan |
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- Iou Wear: 0.8644 |
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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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| 0.8371 | 1.82 | 20 | 0.9482 | 0.3285 | 0.9854 | 0.9854 | nan | nan | 0.9854 | 0.0 | 0.0 | 0.9854 | |
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| 0.6335 | 3.64 | 40 | 0.7489 | 0.4996 | 0.9992 | 0.9992 | nan | nan | 0.9992 | 0.0 | nan | 0.9992 | |
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| 0.5053 | 5.45 | 60 | 0.5400 | 0.4975 | 0.9949 | 0.9949 | nan | nan | 0.9949 | 0.0 | nan | 0.9949 | |
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| 0.3924 | 7.27 | 80 | 0.4544 | 0.4905 | 0.9810 | 0.9810 | nan | nan | 0.9810 | 0.0 | nan | 0.9810 | |
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| 0.3419 | 9.09 | 100 | 0.3840 | 0.4727 | 0.9455 | 0.9455 | nan | nan | 0.9455 | 0.0 | nan | 0.9455 | |
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| 0.3379 | 10.91 | 120 | 0.3407 | 0.4648 | 0.9296 | 0.9296 | nan | nan | 0.9296 | 0.0 | nan | 0.9296 | |
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| 0.2639 | 12.73 | 140 | 0.3495 | 0.4780 | 0.9559 | 0.9559 | nan | nan | 0.9559 | 0.0 | nan | 0.9559 | |
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| 0.224 | 14.55 | 160 | 0.2815 | 0.4541 | 0.9081 | 0.9081 | nan | nan | 0.9081 | 0.0 | nan | 0.9081 | |
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| 0.1725 | 16.36 | 180 | 0.2896 | 0.4599 | 0.9199 | 0.9199 | nan | nan | 0.9199 | 0.0 | nan | 0.9199 | |
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| 0.1623 | 18.18 | 200 | 0.2540 | 0.4679 | 0.9359 | 0.9359 | nan | nan | 0.9359 | 0.0 | nan | 0.9359 | |
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| 0.1724 | 20.0 | 220 | 0.2567 | 0.4702 | 0.9404 | 0.9404 | nan | nan | 0.9404 | 0.0 | nan | 0.9404 | |
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| 0.1503 | 21.82 | 240 | 0.1967 | 0.4459 | 0.8919 | 0.8919 | nan | nan | 0.8919 | 0.0 | nan | 0.8919 | |
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| 0.1189 | 23.64 | 260 | 0.2153 | 0.4617 | 0.9234 | 0.9234 | nan | nan | 0.9234 | 0.0 | nan | 0.9234 | |
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| 0.1007 | 25.45 | 280 | 0.1695 | 0.4324 | 0.8648 | 0.8648 | nan | nan | 0.8648 | 0.0 | nan | 0.8648 | |
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| 0.0921 | 27.27 | 300 | 0.1540 | 0.4346 | 0.8691 | 0.8691 | nan | nan | 0.8691 | 0.0 | nan | 0.8691 | |
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| 0.0897 | 29.09 | 320 | 0.1657 | 0.4538 | 0.9077 | 0.9077 | nan | nan | 0.9077 | 0.0 | nan | 0.9077 | |
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| 0.0814 | 30.91 | 340 | 0.1519 | 0.4374 | 0.8749 | 0.8749 | nan | nan | 0.8749 | 0.0 | nan | 0.8749 | |
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| 0.0729 | 32.73 | 360 | 0.1444 | 0.4430 | 0.8861 | 0.8861 | nan | nan | 0.8861 | 0.0 | nan | 0.8861 | |
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| 0.0892 | 34.55 | 380 | 0.1283 | 0.4106 | 0.8213 | 0.8213 | nan | nan | 0.8213 | 0.0 | nan | 0.8213 | |
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| 0.07 | 36.36 | 400 | 0.1442 | 0.4374 | 0.8748 | 0.8748 | nan | nan | 0.8748 | 0.0 | nan | 0.8748 | |
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| 0.0619 | 38.18 | 420 | 0.1391 | 0.4296 | 0.8592 | 0.8592 | nan | nan | 0.8592 | 0.0 | nan | 0.8592 | |
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| 0.0563 | 40.0 | 440 | 0.1283 | 0.4402 | 0.8804 | 0.8804 | nan | nan | 0.8804 | 0.0 | nan | 0.8804 | |
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| 0.0582 | 41.82 | 460 | 0.1275 | 0.4297 | 0.8595 | 0.8595 | nan | nan | 0.8595 | 0.0 | nan | 0.8595 | |
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| 0.0575 | 43.64 | 480 | 0.1341 | 0.4362 | 0.8724 | 0.8724 | nan | nan | 0.8724 | 0.0 | nan | 0.8724 | |
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| 0.068 | 45.45 | 500 | 0.1132 | 0.4181 | 0.8362 | 0.8362 | nan | nan | 0.8362 | 0.0 | nan | 0.8362 | |
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| 0.0595 | 47.27 | 520 | 0.1285 | 0.4316 | 0.8632 | 0.8632 | nan | nan | 0.8632 | 0.0 | nan | 0.8632 | |
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| 0.0558 | 49.09 | 540 | 0.1291 | 0.4322 | 0.8644 | 0.8644 | nan | nan | 0.8644 | 0.0 | nan | 0.8644 | |
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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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