Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use justinsiow/vit_101 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use justinsiow/vit_101 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="justinsiow/vit_101") 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("justinsiow/vit_101") model = AutoModelForImageClassification.from_pretrained("justinsiow/vit_101", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 05f562b38aa8c437dddb6f86f94cb675c5ff5928ef50e84e01609ae629caa863
- Size of remote file:
- 344 MB
- SHA256:
- 3c55423a12df9aa3986028f894b8d87652d50b1fce7515660d28e9d9d676780e
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