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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-SixrayKnife8-21-2024_saad5 |
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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-SixrayKnife8-21-2024_saad5 |
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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: 0.2720 |
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- Mean Iou: 0.8171 |
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- Mean Accuracy: 0.9114 |
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- Overall Accuracy: 0.9868 |
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- Accuracy Bkg: 0.9925 |
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- Accuracy Knife: 0.8345 |
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- Accuracy Gun: 0.9072 |
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- Iou Bkg: 0.9878 |
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- Iou Knife: 0.7337 |
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- Iou Gun: 0.7298 |
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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: 20 |
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- eval_batch_size: 20 |
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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: 20 |
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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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| 0.2986 | 5.0 | 20 | 0.2833 | 0.8180 | 0.9092 | 0.9869 | 0.9928 | 0.8290 | 0.9057 | 0.9879 | 0.7362 | 0.7299 | |
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| 0.2801 | 10.0 | 40 | 0.2780 | 0.8141 | 0.9117 | 0.9864 | 0.9921 | 0.8366 | 0.9063 | 0.9875 | 0.7281 | 0.7268 | |
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| 0.2908 | 15.0 | 60 | 0.2739 | 0.8178 | 0.9075 | 0.9869 | 0.9930 | 0.8289 | 0.9005 | 0.9880 | 0.7332 | 0.7321 | |
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| 0.2928 | 20.0 | 80 | 0.2720 | 0.8171 | 0.9114 | 0.9868 | 0.9925 | 0.8345 | 0.9072 | 0.9878 | 0.7337 | 0.7298 | |
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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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