FastViT T8
| Property | Value |
|---|---|
| Parameters | 4.0M |
| GMACs | 0.7 |
| Activations | 8.6M |
| Image Size | 256 x 256 |
| Paper | FastViT: A Fast Hybrid Vision Transformer |
What is fastvit_t8.apple_dist_in1k?
FastViT T8 is a highly efficient vision transformer model developed by Apple, designed to balance speed and accuracy in image classification tasks. It employs structural reparameterization techniques and was trained on ImageNet-1k using knowledge distillation to achieve optimal performance with minimal computational overhead.
Implementation Details
The model represents a significant advancement in efficient vision transformer architecture, featuring a hybrid design that combines the best aspects of traditional CNNs and vision transformers. With only 4.0M parameters and 0.7 GMACs, it achieves impressive efficiency while maintaining strong performance on standard image classification tasks.
- Lightweight architecture with only 4.0M parameters
- Optimized for 256x256 image inputs
- Uses structural reparameterization for improved efficiency
- Trained with knowledge distillation on ImageNet-1k
Core Capabilities
- Image classification with state-of-the-art efficiency
- Feature extraction with multiple resolution outputs
- Image embedding generation
- Support for both classification and backbone usage
Frequently Asked Questions
Q: What makes this model unique?
FastViT T8 stands out for its exceptional efficiency-to-performance ratio, achieved through structural reparameterization and knowledge distillation. It's particularly notable for maintaining high accuracy while requiring minimal computational resources.
Q: What are the recommended use cases?
The model is ideal for applications requiring efficient image classification, feature extraction, or as a backbone for more complex computer vision tasks. It's particularly well-suited for deployment in resource-constrained environments or where processing speed is crucial.
