Instructions to use thelabel/image-labeling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thelabel/image-labeling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="thelabel/image-labeling") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("thelabel/image-labeling") model = AutoModelForZeroShotImageClassification.from_pretrained("thelabel/image-labeling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from thelabel/image-labeling: direct link, hf CLI and curl.
- Browser
- Download file 605 MB
-
https://huggingface.co/thelabel/image-labeling/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://thelabel/image-labeling/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/thelabel/image-labeling/resolve/main/pytorch_model.bin
605 MB
- Xet hash:
- 5a5e1ef2bc50accf13047a62cb88783e10955187b2abe628aaa05d5e896ead73
- Size of remote file:
- 605 MB
- SHA256:
- 83d9df2bf012fc6143ff7aeaa67e71f7339a53b913cd88a0d5a3da9677a15d40
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