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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: 0.5986 |
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- Mean Iou: 0.5110 |
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- Mean Accuracy: 0.8118 |
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- Overall Accuracy: 0.8081 |
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- Accuracy Object1: nan |
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- Accuracy Object2: 0.7529 |
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- Accuracy Object3: 0.8707 |
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- Iou Object1: 0.0 |
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- Iou Object2: 0.7267 |
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- Iou Object3: 0.8065 |
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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 Object1 | Accuracy Object2 | Accuracy Object3 | Iou Object1 | Iou Object2 | Iou Object3 | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------:|:----------------:|:----------------:|:-----------:|:-----------:|:-----------:| |
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| 0.7244 | 1.4286 | 20 | 0.6924 | 0.4978 | 0.8311 | 0.8263 | nan | 0.7536 | 0.9087 | 0.0 | 0.7153 | 0.7782 | |
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| 0.7251 | 2.8571 | 40 | 0.6470 | 0.5105 | 0.8256 | 0.8213 | nan | 0.7572 | 0.8940 | 0.0 | 0.7246 | 0.8069 | |
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| 0.6558 | 4.2857 | 60 | 0.6246 | 0.5148 | 0.8243 | 0.8210 | nan | 0.7714 | 0.8772 | 0.0 | 0.7350 | 0.8095 | |
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| 0.6463 | 5.7143 | 80 | 0.5954 | 0.5100 | 0.8126 | 0.8098 | nan | 0.7680 | 0.8572 | 0.0 | 0.7295 | 0.8004 | |
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| 0.63 | 7.1429 | 100 | 0.5892 | 0.5082 | 0.8073 | 0.8043 | nan | 0.7582 | 0.8564 | 0.0 | 0.7254 | 0.7991 | |
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| 0.5935 | 8.5714 | 120 | 0.5923 | 0.5134 | 0.8155 | 0.8127 | nan | 0.7694 | 0.8616 | 0.0 | 0.7369 | 0.8031 | |
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| 0.6137 | 10.0 | 140 | 0.5986 | 0.5110 | 0.8118 | 0.8081 | nan | 0.7529 | 0.8707 | 0.0 | 0.7267 | 0.8065 | |
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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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