ViViT-b-16x2
| Property | Value |
|---|---|
| License | MIT |
| Author | |
| Paper | ViViT: A Video Vision Transformer |
| Downloads | 57,395 |
What is vivit-b-16x2?
ViViT-b-16x2 is a specialized Video Vision Transformer model that extends the capabilities of the Vision Transformer (ViT) architecture to process video content. Developed by Google Research, this model represents a significant advancement in video understanding using transformer-based architectures.
Implementation Details
The model is implemented using PyTorch and is designed specifically for video classification tasks. It utilizes a transformer-based architecture that processes both spatial and temporal dimensions of video data, making it particularly effective for understanding motion and temporal relationships in video sequences.
- Built on transformer architecture for video processing
- Supports video classification tasks
- PyTorch-based implementation
- Includes inference endpoints support
Core Capabilities
- Video classification and analysis
- Temporal relationship understanding
- Spatial-temporal feature extraction
- Fine-tuning capabilities for specific video tasks
Frequently Asked Questions
Q: What makes this model unique?
ViViT is unique in its approach to video understanding by extending the Vision Transformer architecture to handle temporal information in videos, making it particularly effective for video classification tasks while maintaining the benefits of transformer-based architectures.
Q: What are the recommended use cases?
The model is primarily intended for fine-tuning on downstream video classification tasks. It's particularly useful for researchers and developers working on video understanding applications, action recognition, and temporal analysis of video content.
