ViT Base Patch8 224 AugReg2
| Property | Value |
|---|---|
| Parameter Count | 86.6M |
| Model Type | Vision Transformer (ViT) |
| License | Apache-2.0 |
| Image Size | 224x224 |
| GMACs | 66.9 |
| Paper | How to train your ViT? |
What is vit_base_patch8_224.augreg2_in21k_ft_in1k?
This is an advanced Vision Transformer (ViT) model that implements a sophisticated image classification architecture. Initially trained on ImageNet-21k and fine-tuned on ImageNet-1k, it represents a state-of-the-art approach to computer vision tasks using transformer architecture with 8x8 pixel patches.
Implementation Details
The model utilizes a patch-based approach to image processing, dividing input images into 8x8 pixel patches. With 86.6M parameters and 66.9 GMACs, it balances computational efficiency with high performance. The model features 65.7M activations and is optimized for 224x224 pixel images.
- Pre-trained on ImageNet-21k for robust feature learning
- Fine-tuned on ImageNet-1k with additional augmentation
- Implements advanced regularization techniques
- Supports both classification and embedding extraction
Core Capabilities
- High-accuracy image classification
- Feature extraction for downstream tasks
- Flexible usage with timm library integration
- Support for batch processing and real-time inference
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its use of smaller 8x8 patches (compared to traditional 16x16) and its implementation of advanced augmentation and regularization techniques from the AugReg paper. It provides a good balance between accuracy and computational efficiency.
Q: What are the recommended use cases?
The model is ideal for high-precision image classification tasks, feature extraction for transfer learning, and as a backbone for complex computer vision applications. It's particularly suitable for scenarios requiring detailed image analysis due to its smaller patch size.
