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--- |
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library_name: transformers |
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license: other |
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base_model: nvidia/mit-b0 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: segmentation_model_50ep_2 |
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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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# segmentation_model_50ep_2 |
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This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0151 |
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- Mean Iou: 0.4992 |
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- Mean Accuracy: 0.5002 |
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- Overall Accuracy: 0.9980 |
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- Per Category Iou: [0.9979567074182948, 0.0004395926441497546] |
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- Per Category Accuracy: [0.9999017103951866, 0.00046175157765122367] |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy | |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------------------------------:|:--------------------------------------------:| |
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| 0.0176 | 12.1951 | 1000 | 0.0153 | 0.4991 | 0.5001 | 0.9978 | [0.9978437819175541, 0.00041657987919183504] | [0.9997885648043022, 0.00046175157765122367] | |
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| 0.0173 | 24.3902 | 2000 | 0.0153 | 0.4991 | 0.5001 | 0.9978 | [0.9978095148690534, 0.0004100657472081357] | [0.999754230969827, 0.00046175157765122367] | |
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| 0.0144 | 36.5854 | 3000 | 0.0146 | 0.4991 | 0.5001 | 0.9980 | [0.9979986133831826, 0.00026932399676811203] | [0.9999440574585123, 0.00027705094659073417] | |
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| 0.0208 | 48.7805 | 4000 | 0.0151 | 0.4992 | 0.5002 | 0.9980 | [0.9979567074182948, 0.0004395926441497546] | [0.9999017103951866, 0.00046175157765122367] | |
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### Framework versions |
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- Transformers 4.46.3 |
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- Pytorch 2.2.0 |
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- Datasets 2.4.0 |
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- Tokenizers 0.20.3 |
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