Zero-Shot Image Classification
Transformers
PyTorch
Chinese
altclip
Zero-Shot Image Classification
bilingual
en
English
Chinese
Instructions to use BAAI/AltCLIP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BAAI/AltCLIP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="BAAI/AltCLIP") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("BAAI/AltCLIP") model = AutoModelForZeroShotImageClassification.from_pretrained("BAAI/AltCLIP", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from BAAI/AltCLIP: direct link, hf CLI and curl.
- Browser
- Download file 3.46 GB
-
https://huggingface.co/BAAI/AltCLIP/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://BAAI/AltCLIP/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/BAAI/AltCLIP/resolve/main/pytorch_model.bin
3.46 GB
- Xet hash:
- b7447bd2aa864438bfe4011fb2d931a00c4e9ac7a03d0f202b37088dd179431a
- Size of remote file:
- 3.46 GB
- SHA256:
- 0102cd1b26b61103dc89068d15cf02682f7bbd910e1ea99939f2bfc8c6772ebe
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