Video Classification
Transformers
PyTorch
Safetensors
English
xclip
feature-extraction
vision
Eval Results (legacy)
Instructions to use microsoft/xclip-base-patch32-16-frames with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/xclip-base-patch32-16-frames with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="microsoft/xclip-base-patch32-16-frames")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/xclip-base-patch32-16-frames") model = AutoModel.from_pretrained("microsoft/xclip-base-patch32-16-frames", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Adding `safetensors` variant of this model (#1)
Verified signature- Adding `safetensors` variant of this model (0f95fcd4ae71910513f25e2da564e2a0acda36ac) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>663a1e6
-
Upload README.md with huggingface_hub
9c89543
-
Upload tokenizer
d1005fb -
Upload processor
041335e -
Upload model
6e6fe6d -
initial commit
d386c3e