Instructions to use jaqen79/adv_classifier_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jaqen79/adv_classifier_v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jaqen79/adv_classifier_v3") 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("jaqen79/adv_classifier_v3") model = AutoModelForImageClassification.from_pretrained("jaqen79/adv_classifier_v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jaqen79/adv_classifier_v3: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/jaqen79/adv_classifier_v3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jaqen79/adv_classifier_v3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jaqen79/adv_classifier_v3/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 67e834d5a5e344648d77c749ab4a61e1464269f74eee681e86ab453591e7c71f
- Size of remote file:
- 343 MB
- SHA256:
- 157d0e93aa7a3dc5b1103b5cf4985a53149eaa4e0b497cc55779f093ee967da8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.