Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use sayakpaul/vit-base-patch16-224-in21k-finetuned-lora-food101 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sayakpaul/vit-base-patch16-224-in21k-finetuned-lora-food101 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sayakpaul/vit-base-patch16-224-in21k-finetuned-lora-food101") 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("sayakpaul/vit-base-patch16-224-in21k-finetuned-lora-food101") model = AutoModelForImageClassification.from_pretrained("sayakpaul/vit-base-patch16-224-in21k-finetuned-lora-food101") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b383dd58d6b040e9626113931db2533af9c39d38176051d4a4b8cd2a50071296
- Size of remote file:
- 2.69 MB
- SHA256:
- 18c24ece6561dec886b00e307e3df0a43a3919d7f6cf32edd01e06f4fb5c473b
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