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--- |
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license: other |
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base_model: nvidia/mit-b0 |
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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-SixrayKnife8-19-2024 |
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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-SixrayKnife8-19-2024 |
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This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the saad7489/SixraygunTest dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9038 |
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- Mean Iou: 0.4486 |
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- Mean Accuracy: 0.8693 |
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- Overall Accuracy: 0.8708 |
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- Accuracy Bkg: 0.8709 |
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- Accuracy Gun: 0.8680 |
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- Accuracy Knife: 0.8689 |
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- Iou Bkg: 0.8700 |
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- Iou Gun: 0.1670 |
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- Iou Knife: 0.3088 |
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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: 1e-05 |
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- train_batch_size: 5 |
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- eval_batch_size: 5 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Bkg | Accuracy Gun | Accuracy Knife | Iou Bkg | Iou Gun | Iou Knife | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------:|:------------:|:--------------:|:-------:|:-------:|:---------:| |
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| 1.0919 | 1.4286 | 20 | 1.1033 | 0.1925 | 0.6509 | 0.3960 | 0.3783 | 0.8281 | 0.7462 | 0.3783 | 0.0314 | 0.1677 | |
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| 1.0576 | 2.8571 | 40 | 1.0473 | 0.3242 | 0.7738 | 0.6681 | 0.6608 | 0.8826 | 0.7781 | 0.6607 | 0.0600 | 0.2518 | |
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| 0.9844 | 4.2857 | 60 | 0.9913 | 0.3823 | 0.8265 | 0.7783 | 0.7750 | 0.8873 | 0.8171 | 0.7748 | 0.0928 | 0.2793 | |
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| 0.9604 | 5.7143 | 80 | 0.9385 | 0.4206 | 0.8552 | 0.8396 | 0.8386 | 0.8813 | 0.8457 | 0.8381 | 0.1334 | 0.2902 | |
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| 0.9418 | 7.1429 | 100 | 0.9073 | 0.4389 | 0.8651 | 0.8592 | 0.8588 | 0.8795 | 0.8570 | 0.8581 | 0.1520 | 0.3065 | |
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| 0.9029 | 8.5714 | 120 | 0.8989 | 0.4474 | 0.8672 | 0.8683 | 0.8684 | 0.8712 | 0.8620 | 0.8677 | 0.1621 | 0.3123 | |
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| 0.9277 | 10.0 | 140 | 0.9038 | 0.4486 | 0.8693 | 0.8708 | 0.8709 | 0.8680 | 0.8689 | 0.8700 | 0.1670 | 0.3088 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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