Video Classification
Transformers
PyTorch
Safetensors
English
xclip
feature-extraction
vision
Eval Results (legacy)
Instructions to use aurelio-ai/xclip-base-patch16-zero-shot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aurelio-ai/xclip-base-patch16-zero-shot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="aurelio-ai/xclip-base-patch16-zero-shot")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("aurelio-ai/xclip-base-patch16-zero-shot") model = AutoModel.from_pretrained("aurelio-ai/xclip-base-patch16-zero-shot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download requirements.txt from aurelio-ai/xclip-base-patch16-zero-shot: direct link, hf CLI and curl.
- Browser
- Download file 461 Bytes
-
https://huggingface.co/aurelio-ai/xclip-base-patch16-zero-shot/resolve/main/requirements.txt
- Command line
-
hf download hf://aurelio-ai/xclip-base-patch16-zero-shot/requirements.txt
-
curl -L -o requirements.txt https://huggingface.co/aurelio-ai/xclip-base-patch16-zero-shot/resolve/main/requirements.txt
461 Bytes
| certifi==2023.7.22 | |
| charset-normalizer==3.3.2 | |
| colorama==0.4.6 | |
| filelock==3.13.1 | |
| fsspec==2023.10.0 | |
| huggingface-hub==0.17.3 | |
| idna==3.4 | |
| Jinja2==3.1.2 | |
| MarkupSafe==2.1.3 | |
| mpmath==1.3.0 | |
| networkx==3.2.1 | |
| numpy==1.24.4 | |
| opencv-python==4.8.1.78 | |
| packaging==23.2 | |
| Pillow==10.1.0 | |
| PyYAML==6.0.1 | |
| regex==2023.10.3 | |
| requests==2.31.0 | |
| safetensors==0.4.0 | |
| sympy==1.12 | |
| tokenizers==0.13.3 | |
| tqdm==4.66.1 | |
| transformers==4.27.2 | |
| typing_extensions==4.8.0 | |
| urllib3==2.0.7 | |
| decord==0.6.0 | |
| easyocr==1.7.1 |