Vision Transformer Small (ViT-Small/32)
| Property | Value |
|---|---|
| Parameter Count | 22.9M |
| License | Apache 2.0 |
| Image Size | 224x224 |
| GMACs | 1.1 |
| Paper | How to train your ViT? |
What is vit_small_patch32_224.augreg_in21k_ft_in1k?
This is a Vision Transformer (ViT) model specifically designed for image classification tasks. Originally trained on ImageNet-21k and fine-tuned on ImageNet-1k, it implements additional augmentation and regularization techniques to enhance performance. The model was initially developed in JAX by the original authors and later ported to PyTorch by Ross Wightman.
Implementation Details
The model operates on 224x224 pixel images, processing them in patches of size 32x32. With 22.9M parameters and 1.1 GMACs, it offers an efficient balance between computational cost and performance. The architecture utilizes transformer blocks to process image patches as sequences, similar to how transformers process text tokens.
- Pre-trained on ImageNet-21k for robust feature learning
- Fine-tuned on ImageNet-1k with advanced augmentation
- Uses patch size of 32x32 for efficient processing
- Supports both classification and feature extraction modes
Core Capabilities
- Image Classification with 1000 classes
- Feature Extraction for downstream tasks
- Batch processing support
- Compatible with timm's transformation pipeline
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient architecture combining the benefits of Vision Transformers with additional augmentation and regularization techniques. It's particularly notable for its two-stage training approach (pre-training on ImageNet-21k and fine-tuning on ImageNet-1k).
Q: What are the recommended use cases?
The model is well-suited for general image classification tasks, feature extraction for transfer learning, and as a backbone for more complex computer vision tasks. It's particularly effective when working with standard resolution images (224x224) and when computational efficiency is a consideration.
