Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use EdwarV/computer_vision_example with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EdwarV/computer_vision_example with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="EdwarV/computer_vision_example") 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("EdwarV/computer_vision_example") model = AutoModelForImageClassification.from_pretrained("EdwarV/computer_vision_example", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 95785586f2dc1d8235c95bf82dd5aacc47881b107b391e9ceb928d51ff22f0ba
- Size of remote file:
- 343 MB
- SHA256:
- d66027d6e4a50436b38e2230450b32c35516825e5ec60fb00093022e87a5ee22
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