ViViT-B-16x2-Kinetics400
| Property | Value |
|---|---|
| License | MIT |
| Author | |
| Paper | ViViT: A Video Vision Transformer |
| Downloads | 416,310 |
What is vivit-b-16x2-kinetics400?
ViViT-B-16x2-Kinetics400 is a Video Vision Transformer model specifically designed for video classification tasks. It represents an innovative extension of the Vision Transformer (ViT) architecture, adapted to handle video data by incorporating temporal information processing capabilities. This model has been trained on the Kinetics-400 dataset, making it particularly effective for action recognition and video understanding tasks.
Implementation Details
The model implements a transformer-based architecture that processes video frames through a combination of spatial and temporal attention mechanisms. It utilizes a 16x2 architecture pattern, referring to the patch size and temporal sampling strategy.
- Built on PyTorch framework for efficient deep learning computations
- Implements the transformer architecture for video processing
- Supports inference endpoints for practical deployment
- Utilizes patch-based processing of video frames
Core Capabilities
- Video classification and action recognition
- Temporal feature extraction from video sequences
- Efficient processing of both spatial and temporal information
- Support for transfer learning and fine-tuning on custom video datasets
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its innovative approach to video processing using transformer architecture, extending the success of ViT to video understanding tasks. It's particularly notable for its ability to capture both spatial and temporal relationships in video data efficiently.
Q: What are the recommended use cases?
The model is primarily designed for video classification tasks and is best suited for action recognition, video understanding, and similar applications. It can be fine-tuned on specific video classification tasks for optimal performance in particular domains.
