Instructions to use Zekrom997/image_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Zekrom997/image_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Zekrom997/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("Zekrom997/image_classification") model = AutoModelForImageClassification.from_pretrained("Zekrom997/image_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Zekrom997/image_classification: direct link, hf CLI and curl.
- Browser
- Download file 344 MB
-
https://huggingface.co/Zekrom997/image_classification/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Zekrom997/image_classification/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Zekrom997/image_classification/resolve/main/pytorch_model.bin
344 MB
- Xet hash:
- c32ca5d6dedc05de0cf360d4ed27b87389b6d7f6a9f5b6f95149c41b9b02092c
- Size of remote file:
- 344 MB
- SHA256:
- 9da6197e183f0f951d2f8b3bcadbf1ad039d18806db1c3219bb6369021610aeb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.