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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-base-1029
This model is trained from scratch using [VideoMAE](https://huggingface.co/docs/transformers/en/model_doc/videomae) on over 4.7k 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: 32
- 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: 30.0
### Training results
{
"_runtime": 54805.860011105,
"_step": 4351,
"eval/runtime": 17.8428,
"eval/samples_per_second": 2.578,
"eval/steps_per_second": 2.578,
"total_flos": 3.8084565648770335e+21,
"train/epoch": 30,
"train/global_step": 4350,
"train/grad_norm": 0.0735374316573143,
"train/learning_rate": 0,
"train/loss": 0.5736,
"train_loss": 0.5022664608695041,
"train_runtime": 54785.1298,
"train_samples_per_second": 2.527,
"train_steps_per_second": 0.079
}
### 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-base,
trust_remote_code=True,
)
embedding = model.videomae(batch["image"])
```
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