Vision Transformer Tiny (ViT-Tiny)
| Property | Value |
|---|---|
| Parameter Count | 5.7M |
| Model Type | Vision Transformer |
| License | Apache-2.0 |
| Image Size | 224x224 |
| GMACs | 1.1 |
| Paper | How to train your ViT? |
What is vit_tiny_patch16_224.augreg_in21k_ft_in1k?
This is a compact Vision Transformer model designed for efficient image classification. Originally trained on ImageNet-21k and fine-tuned on ImageNet-1k, it represents a lightweight implementation of the ViT architecture with additional augmentation and regularization techniques. The model processes images by dividing them into 16x16 patches and leverages transformer architecture for feature extraction.
Implementation Details
The model implements a tiny variant of the Vision Transformer architecture with specific optimizations:
- Patch size of 16x16 pixels
- Two-stage training: pretraining on ImageNet-21k followed by fine-tuning on ImageNet-1k
- Utilizes advanced augmentation and regularization techniques
- Optimized for 224x224 input images
- Efficient architecture with only 5.7M parameters
Core Capabilities
- Image classification with 1000 classes (ImageNet-1k)
- Feature extraction for downstream tasks
- Efficient inference with relatively low computational requirements (1.1 GMACs)
- Support for both classification and embedding extraction
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient design, combining the power of transformer architecture with a compact parameter count. It's particularly notable for its use of advanced training techniques detailed in the "How to train your ViT?" paper, resulting in strong performance despite its small size.
Q: What are the recommended use cases?
The model is ideal for applications requiring efficient image classification or feature extraction, particularly in resource-constrained environments. It's well-suited for mobile applications, edge devices, or scenarios where a balance between accuracy and computational efficiency is crucial.
