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--- |
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base_model: nvidia/mit-b0 |
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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-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: 0.6355 |
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- Mean Iou: 0.5008 |
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- Mean Accuracy: 0.7954 |
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- Overall Accuracy: 0.7906 |
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- Accuracy Bkg: nan |
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- Accuracy Knife: 0.7186 |
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- Accuracy Gun: 0.8722 |
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- Iou Bkg: 0.0 |
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- Iou Knife: 0.6915 |
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- Iou Gun: 0.8110 |
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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 Knife | Accuracy Gun | Iou Bkg | Iou Knife | Iou Gun | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------:|:--------------:|:------------:|:-------:|:---------:|:-------:| |
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| 0.7945 | 1.4286 | 20 | 0.7932 | 0.5023 | 0.8446 | 0.8389 | nan | 0.7531 | 0.9361 | 0.0 | 0.7186 | 0.7883 | |
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| 0.7385 | 2.8571 | 40 | 0.7324 | 0.5150 | 0.8445 | 0.8404 | nan | 0.7787 | 0.9103 | 0.0 | 0.7375 | 0.8074 | |
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| 0.7139 | 4.2857 | 60 | 0.7152 | 0.5033 | 0.8256 | 0.8200 | nan | 0.7358 | 0.9155 | 0.0 | 0.7072 | 0.8027 | |
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| 0.7405 | 5.7143 | 80 | 0.6747 | 0.4953 | 0.7972 | 0.7917 | nan | 0.7078 | 0.8866 | 0.0 | 0.6785 | 0.8075 | |
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| 0.6666 | 7.1429 | 100 | 0.6442 | 0.4937 | 0.7919 | 0.7860 | nan | 0.6964 | 0.8874 | 0.0 | 0.6723 | 0.8089 | |
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| 0.6357 | 8.5714 | 120 | 0.6210 | 0.4957 | 0.7874 | 0.7823 | nan | 0.7059 | 0.8688 | 0.0 | 0.6794 | 0.8076 | |
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| 0.6548 | 10.0 | 140 | 0.6355 | 0.5008 | 0.7954 | 0.7906 | nan | 0.7186 | 0.8722 | 0.0 | 0.6915 | 0.8110 | |
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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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