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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-b1 |
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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-b01-finetuned-wrinkle |
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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-b01-finetuned-wrinkle |
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This model is a fine-tuned version of [nvidia/mit-b1](https://huggingface.co/nvidia/mit-b1) on the face-wrinkles dataset. |
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It achieves the following results on the evaluation set: |
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- eval_loss: 0.0002 |
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- eval_mean_iou: 0.0 |
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- eval_mean_accuracy: nan |
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- eval_overall_accuracy: nan |
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- eval_accuracy_unlabeled: nan |
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- eval_accuracy_wrinkle: nan |
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- eval_iou_unlabeled: 0.0 |
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- eval_iou_wrinkle: 0.0 |
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- eval_runtime: 13.0305 |
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- eval_samples_per_second: 10.053 |
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- eval_steps_per_second: 5.065 |
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- epoch: 2.3978 |
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- step: 880 |
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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: 2 |
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- eval_batch_size: 2 |
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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: 5 |
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### Framework versions |
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- Transformers 4.46.2 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
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