Transformers
TensorBoard
Safetensors
vit_mae
pretraining
masked-auto-encoding
Generated from Trainer
Instructions to use crncskn/radiovers16v with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use crncskn/radiovers16v with Transformers:
# Load model directly from transformers import AutoImageProcessor, AutoModelForPreTraining processor = AutoImageProcessor.from_pretrained("crncskn/radiovers16v") model = AutoModelForPreTraining.from_pretrained("crncskn/radiovers16v") - Notebooks
- Google Colab
- Kaggle
radiovers16v
This model is a fine-tuned version of on the /kaggle/radioai/radiology_ai dataset. It achieves the following results on the evaluation set:
- Loss: 0.4036
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: 3.125e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40.0
Training results
Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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