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---
library_name: transformers
tags:
- masked-image-modeling
- generated_from_trainer
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smb-vision-large-1202
This model is trained from scratch using [VideoMAE](https://huggingface.co/docs/transformers/en/model_doc/videomae) on over 55k CT volumes.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-04
- train_batch_size: 16
- eval_batch_size: 1
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 10.0
### Training results
{
"_runtime": 2641.091489502,
"_step": 399,
"_timestamp": 1733187755.3146417,
"_wandb.runtime": 2660,
"train/epoch": 8.425414364640885,
"train/global_step": 18300,
"train/grad_norm": 0.04110511764883995,
"train/learning_rate": 0.0001624558726951691,
"train/loss": 0.4292
}
### Framework versions
- Transformers 4.46.0
- Pytorch 2.5.0
- Datasets 3.0.2
- Tokenizers 0.20.1
### How to use
```python
# load data using `dataload.py`
model = VideoMAEForPreTraining.from_pretrained(
standardmodelbio/smb-vision-large,
trust_remote_code=True,
)
embedding = model.videomae(batch["image"])
```
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