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
Download .gitignore from EdwarV/computer_vision_example: direct link, hf CLI and curl.
- Browser
- Download file 13 Bytes
-
https://huggingface.co/EdwarV/computer_vision_example/resolve/main/.gitignore
- Command line
-
hf download hf://EdwarV/computer_vision_example/.gitignore
-
curl -L -o .gitignore https://huggingface.co/EdwarV/computer_vision_example/resolve/main/.gitignore
13 Bytes
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