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 tokenizer.json from BAAI/AltCLIP: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/BAAI/AltCLIP/resolve/main/tokenizer.json
- Command line
-
hf download hf://BAAI/AltCLIP/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/BAAI/AltCLIP/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 26d2251a0cc8624d53826ce61b262d382bc7c92ea49ae7dd998f44ed8fcf6656
- Size of remote file:
- 17.1 MB
- SHA256:
- de3788bb2f349135f2ad2e2b10dff3cee7f23fab906a5fbdadf20bc963b05b4c
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