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:
- 228bbe03b8cacbfd6feb7c6d4b0eee027e00e2b9caae6e6b58a3d555d5e67fd0
- Size of remote file:
- 3.78 kB
- SHA256:
- 3af10caf3d8774aabf2c34b2b7781a6cd6fe5a123b9fd9566d6221c61261b01d
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