vit_small_patch16_224.augreg_in1k
| Property | Value |
|---|---|
| Parameter Count | 22.1M |
| Model Type | Vision Transformer (ViT) |
| License | Apache 2.0 |
| Input Size | 224 x 224 |
| GMACs | 4.3 |
| Paper | How to train your ViT? |
What is vit_small_patch16_224.augreg_in1k?
This is a specialized Vision Transformer model trained on ImageNet-1k with enhanced augmentation and regularization techniques. Originally developed in JAX by the paper authors and later ported to PyTorch by Ross Wightman, it represents a sophisticated approach to image classification using transformer architecture.
Implementation Details
The model employs a patch-based approach, dividing input images into 16x16 patches. With 22.1M parameters and 4.3 GMACs, it strikes a balance between computational efficiency and performance. The model processes 224x224 pixel images and maintains 8.2M activations during operation.
- Supports both classification and embedding extraction
- Implements patch-based image processing
- Features additional augmentation and regularization compared to standard ViT
- Includes pre-trained weights on ImageNet-1k
Core Capabilities
- Image classification with 1000 ImageNet classes
- Feature extraction for downstream tasks
- Efficient processing with 16x16 patch size
- Flexible integration with PyTorch workflows
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its augmentation-enhanced training approach, making it more robust than standard ViT models. It's specifically optimized for 224x224 image inputs while maintaining a relatively small parameter count of 22.1M.
Q: What are the recommended use cases?
The model is ideal for image classification tasks, particularly when working with standard-resolution images. It's also excellent for feature extraction in transfer learning scenarios, especially when computational efficiency is a concern.
