TimeSformer Base Model (Kinetics-400)
| Property | Value |
|---|---|
| License | CC-BY-NC-4.0 |
| Framework | PyTorch |
| Paper | arXiv:2102.05095 |
| Downloads | 77,234 |
What is timesformer-base-finetuned-k400?
TimeSformer is a revolutionary video understanding model that leverages transformer architecture to process spatial and temporal information in videos. This particular version is the base model fine-tuned on the Kinetics-400 dataset, capable of classifying videos into 400 different categories.
Implementation Details
The model implements a space-time attention mechanism that processes video frames using transformer architectures. It's built using PyTorch and can be easily integrated using the Hugging Face transformers library. The model processes video frames of size 224x224 pixels and can handle sequences of frames for classification tasks.
- Utilizes space-time attention mechanisms
- Pre-trained on Kinetics-400 dataset
- Supports batch processing of video frames
- Implements transformer-based architecture for video understanding
Core Capabilities
- Video classification across 400 Kinetics categories
- Efficient processing of spatial and temporal information
- Support for standard video input formats
- Easy integration with PyTorch workflows
Frequently Asked Questions
Q: What makes this model unique?
TimeSformer is the first model to demonstrate that a pure transformer-based architecture can be effective for video understanding tasks, eliminating the need for conventional CNN-based approaches.
Q: What are the recommended use cases?
The model is ideal for video classification tasks, particularly in applications requiring recognition of human actions, activities, and events within video content. It's particularly suited for research and non-commercial applications due to its licensing.
