| Property | Value |
|---|---|
| Parameter Count | 343M |
| License | CC-BY-NC-4.0 |
| Paper | VideoMAE Paper |
| Framework | PyTorch |
What is videomae-large?
VideoMAE-large is an advanced self-supervised learning model designed for video understanding tasks. It extends the Masked Autoencoder (MAE) approach to video processing, utilizing a large-scale architecture with 343M parameters. Pre-trained on the Kinetics-400 dataset for 1600 epochs, it represents a significant advancement in video representation learning.
Implementation Details
The model processes videos as sequences of 16x16 fixed-size patches, incorporating a Vision Transformer (ViT) architecture with additional decoder capabilities. It utilizes a [CLS] token for classification tasks and employs sinus/cosinus position embeddings.
- Large-scale architecture with 343M parameters
- Self-supervised pre-training on Kinetics-400
- 16x16 patch-based video processing
- Transformer-based encoding with specialized decoder
Core Capabilities
- Masked video patch prediction
- Feature extraction for downstream tasks
- Video representation learning
- Transfer learning potential for various video tasks
Frequently Asked Questions
Q: What makes this model unique?
VideoMAE-large stands out for its self-supervised learning approach that doesn't require labeled data for pre-training, making it highly efficient for video understanding tasks. Its large parameter count and specialized architecture enable robust feature learning from masked video content.
Q: What are the recommended use cases?
The model is primarily designed for video understanding tasks and can be fine-tuned for specific applications like action recognition, video classification, and feature extraction. It's particularly useful when working with large video datasets that require sophisticated feature learning.