Instructions to use yaboidimsum/image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yaboidimsum/image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="yaboidimsum/image_classification") 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("yaboidimsum/image_classification") model = AutoModelForImageClassification.from_pretrained("yaboidimsum/image_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from yaboidimsum/image_classification: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/yaboidimsum/image_classification/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://yaboidimsum/image_classification@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/yaboidimsum/image_classification/resolve/refs%2Fpr%2F1/pytorch_model.bin
343 MB
- Xet hash:
- 38e4e92478e31b0feaa05f50988d5fb781603856f1928ade0487172b43ab758e
- Size of remote file:
- 343 MB
- SHA256:
- b915555aa93b83f2dcda94bc4ce21e419778dc5487e733c0d9704ab3f539a78d
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