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"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("BAAI/AltCLIP") model = AutoModelForZeroShotImageClassification.from_pretrained("BAAI/AltCLIP") - Notebooks
- Google Colab
- Kaggle
| __pycache__ | |
| .idea/ | |
| logs/ | |
| test_tokenizer.py | |
| samples_text2image/ | |
| generate_contexts/ | |
| venv/ | |
| *__pycache__ | |
| .DS_Store | |
| .vscode | |
| *.swo | |
| *.swp | |
| *log | |
| build | |
| dist | |
| eazybigmodel.egg-info | |
| flagai.egg-info | |
| test_report | |
| /data/ | |
| /tests/*/data | |
| checkpoints | |
| state_dict | |
| checkpoints* | |
| vocabs | |
| tensorboard* | |
| datasets | |
| qqp | |
| glm_large_qqp_pytorch |