vit_base_patch32_224.augreg_in21k
| Property | Value |
|---|---|
| Parameter Count | 104.3M |
| Model Type | Vision Transformer (ViT) |
| License | Apache-2.0 |
| Training Dataset | ImageNet-21k |
| Image Size | 224 x 224 |
| GMACs | 4.4 |
What is vit_base_patch32_224.augreg_in21k?
This is a Vision Transformer (ViT) model specifically designed for image classification tasks. Originally trained by Google Research and ported to PyTorch by Ross Wightman, it implements advanced augmentation and regularization techniques for enhanced performance. The model processes images by dividing them into 32x32 patches and employs transformer architecture for feature extraction.
Implementation Details
The model architecture follows the Vision Transformer paradigm with several key technical specifications: it operates on 224x224 pixel images, uses a patch size of 32, and contains approximately 104.3M parameters. The implementation includes both classification and embedding extraction capabilities, making it versatile for various computer vision tasks.
- Trained on ImageNet-21k with enhanced augmentation
- Supports both classification and feature extraction modes
- Efficient processing with 4.4 GMACs computation requirement
- Includes model-specific transforms for preprocessing
Core Capabilities
- Image Classification with 21k classes support
- Feature Embedding Generation
- Flexible deployment with PyTorch integration
- Pre-trained weights available for immediate use
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its implementation of additional augmentation and regularization techniques during training on ImageNet-21k, as detailed in the "How to train your ViT?" paper. The patch size of 32 offers a good balance between computational efficiency and performance.
Q: What are the recommended use cases?
The model is particularly well-suited for image classification tasks requiring broad category recognition (thanks to ImageNet-21k training), feature extraction for downstream tasks, and scenarios where a balance between computational resources and accuracy is needed.
