Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use sasha/vit-base-butterflies with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sasha/vit-base-butterflies with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sasha/vit-base-butterflies") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("sasha/vit-base-butterflies") model = AutoModelForImageClassification.from_pretrained("sasha/vit-base-butterflies", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from sasha/vit-base-butterflies: direct link, hf CLI and curl.
- Browser
- Download file 5.91 kB
-
https://huggingface.co/sasha/vit-base-butterflies/resolve/main/training_args.bin
- Command line
-
hf download hf://sasha/vit-base-butterflies/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/sasha/vit-base-butterflies/resolve/main/training_args.bin
5.91 kB
- Xet hash:
- a68e1d40eb1976c5d65956ceb094e34797baffd539bb9c8d12708c6423751c93
- Size of remote file:
- 5.91 kB
- SHA256:
- 83c53c7d0aabbcb0a2b5b92bad717582c86772feb11816b40d24acf1efb9602f
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