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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: segformerSAAD |
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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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# segformerSAAD |
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This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the saad7489/SixGUN dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7611 |
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- Mean Iou: 0.5823 |
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- Mean Accuracy: 0.8994 |
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- Overall Accuracy: 0.9474 |
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- Accuracy Bkg: 0.9505 |
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- Accuracy Knife: 0.8767 |
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- Accuracy Gun: 0.8711 |
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- Iou Bkg: 0.9471 |
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- Iou Knife: 0.4452 |
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- Iou Gun: 0.3544 |
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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: 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: 30 |
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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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| 0.9115 | 10.0 | 20 | 1.0252 | 0.5076 | 0.9065 | 0.9181 | 0.9188 | 0.8625 | 0.9382 | 0.9176 | 0.3218 | 0.2833 | |
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| 0.766 | 20.0 | 40 | 0.8278 | 0.5811 | 0.8914 | 0.9486 | 0.9523 | 0.8691 | 0.8527 | 0.9485 | 0.4288 | 0.3661 | |
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| 0.7862 | 30.0 | 60 | 0.7611 | 0.5823 | 0.8994 | 0.9474 | 0.9505 | 0.8767 | 0.8711 | 0.9471 | 0.4452 | 0.3544 | |
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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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