Instructions to use Jagannath/artDetection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jagannath/artDetection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Jagannath/artDetection") 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("Jagannath/artDetection") model = AutoModelForImageClassification.from_pretrained("Jagannath/artDetection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from Jagannath/artDetection: direct link, hf CLI and curl.
- Browser
- Download file 687 MB
-
https://huggingface.co/Jagannath/artDetection/resolve/refs%2Fpr%2F2/optimizer.pt
- Command line
-
hf download hf://Jagannath/artDetection@refs/pr/2/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/Jagannath/artDetection/resolve/refs%2Fpr%2F2/optimizer.pt
687 MB
- Xet hash:
- 1a94f30203a67da1cc879dba94f5b6a17f35a389522c3194b531c408eb8dfda8
- Size of remote file:
- 687 MB
- SHA256:
- e7eadf35aa4ac8e39bb054e1191d827c4e8dcff589e8be0e1a17133b0d9e1a64
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.