Instructions to use google/vivit-b-16x2-kinetics400 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/vivit-b-16x2-kinetics400 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="google/vivit-b-16x2-kinetics400")# Load model directly from transformers import AutoImageProcessor, ViViTForVideoClassification processor = AutoImageProcessor.from_pretrained("google/vivit-b-16x2-kinetics400") model = ViViTForVideoClassification.from_pretrained("google/vivit-b-16x2-kinetics400") - Notebooks
- Google Colab
- Kaggle
QORA-Vision (Video) - Native Rust Video Classifier based on ViViT
#5
by drdraq - opened
Pure Rust video action classification engine based on ViViT. Classifies video clips into 400 action categories from Kinetics-400. No Python runtime, no CUDA, no external dependencies.
Try: https://huggingface.co/qoranet/QORA-Vision-Video
Classify from frame directory (fast, from binary)
qora-vision.exe vivit --load model.qora-vision --frames ./my_frames/
Classify from video file (requires ffmpeg)
qora-vision.exe vivit --load model.qora-vision --video clip.mp4
Load from safetensors (slow, first time)
qora-vision.exe vivit --frames ./my_frames/ --model-path ../ViViT/
Save binary for fast loading
qora-vision.exe vivit --model-path ../ViViT/ --save model.qora-vision