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--- |
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library_name: transformers |
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tags: |
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- masked-image-modeling |
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- generated_from_trainer |
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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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# smb-vision-large-1202 |
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This model is trained from scratch using [VideoMAE](https://huggingface.co/docs/transformers/en/model_doc/videomae) on over 55k CT volumes. |
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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: 3e-04 |
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- train_batch_size: 16 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.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: cosine |
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- num_epochs: 10.0 |
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### Training results |
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{ |
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"_runtime": 2641.091489502, |
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"_step": 399, |
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"_timestamp": 1733187755.3146417, |
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"_wandb.runtime": 2660, |
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"train/epoch": 8.425414364640885, |
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"train/global_step": 18300, |
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"train/grad_norm": 0.04110511764883995, |
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"train/learning_rate": 0.0001624558726951691, |
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"train/loss": 0.4292 |
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} |
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### Framework versions |
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- Transformers 4.46.0 |
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- Pytorch 2.5.0 |
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- Datasets 3.0.2 |
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- Tokenizers 0.20.1 |
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### How to use |
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```python |
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# load data using `dataload.py` |
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model = VideoMAEForPreTraining.from_pretrained( |
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standardmodelbio/smb-vision-large, |
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trust_remote_code=True, |
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) |
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embedding = model.videomae(batch["image"]) |
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``` |
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