Instructions to use chanelcolgate/vit-base-patch16-224-chest-x-ray with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chanelcolgate/vit-base-patch16-224-chest-x-ray with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="chanelcolgate/vit-base-patch16-224-chest-x-ray") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("chanelcolgate/vit-base-patch16-224-chest-x-ray") model = AutoModelForImageClassification.from_pretrained("chanelcolgate/vit-base-patch16-224-chest-x-ray") - Notebooks
- Google Colab
- Kaggle
vit-base-patch16-224-chest-x-ray
This model is a fine-tuned version of google/vit-base-patch16-224 on the chest-xray-classification dataset.
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
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