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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: 0.2168 |
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- Mean Iou: 0.3007 |
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- Mean Accuracy: 0.6014 |
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- Overall Accuracy: 0.6014 |
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- Accuracy Unlabeled: nan |
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- Accuracy Tool: nan |
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- Accuracy Wear: 0.6014 |
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- Iou Unlabeled: 0.0 |
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- Iou Tool: nan |
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- Iou Wear: 0.6014 |
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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: 25 |
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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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| 0.9147 | 1.82 | 20 | 0.9308 | 0.3010 | 0.9029 | 0.9029 | nan | nan | 0.9029 | 0.0 | 0.0 | 0.9029 | |
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| 0.6788 | 3.64 | 40 | 0.6521 | 0.2589 | 0.7768 | 0.7768 | nan | nan | 0.7768 | 0.0 | 0.0 | 0.7768 | |
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| 0.4559 | 5.45 | 60 | 0.4600 | 0.2663 | 0.7989 | 0.7989 | nan | nan | 0.7989 | 0.0 | 0.0 | 0.7989 | |
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| 0.3799 | 7.27 | 80 | 0.3767 | 0.2061 | 0.6182 | 0.6182 | nan | nan | 0.6182 | 0.0 | 0.0 | 0.6182 | |
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| 0.4438 | 9.09 | 100 | 0.3259 | 0.3479 | 0.6958 | 0.6958 | nan | nan | 0.6958 | 0.0 | nan | 0.6958 | |
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| 0.3534 | 10.91 | 120 | 0.3008 | 0.3057 | 0.6114 | 0.6114 | nan | nan | 0.6114 | 0.0 | nan | 0.6114 | |
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| 0.3332 | 12.73 | 140 | 0.2805 | 0.3631 | 0.7261 | 0.7261 | nan | nan | 0.7261 | 0.0 | nan | 0.7261 | |
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| 0.2543 | 14.55 | 160 | 0.2659 | 0.2927 | 0.5853 | 0.5853 | nan | nan | 0.5853 | 0.0 | nan | 0.5853 | |
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| 0.2746 | 16.36 | 180 | 0.2324 | 0.2724 | 0.5449 | 0.5449 | nan | nan | 0.5449 | 0.0 | nan | 0.5449 | |
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| 0.2532 | 18.18 | 200 | 0.2409 | 0.3597 | 0.7194 | 0.7194 | nan | nan | 0.7194 | 0.0 | nan | 0.7194 | |
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| 0.2353 | 20.0 | 220 | 0.2369 | 0.3070 | 0.6139 | 0.6139 | nan | nan | 0.6139 | 0.0 | nan | 0.6139 | |
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| 0.2192 | 21.82 | 240 | 0.2210 | 0.3041 | 0.6083 | 0.6083 | nan | nan | 0.6083 | 0.0 | nan | 0.6083 | |
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| 0.2469 | 23.64 | 260 | 0.2168 | 0.3007 | 0.6014 | 0.6014 | nan | nan | 0.6014 | 0.0 | nan | 0.6014 | |
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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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