Vision Transformer Base Patch16 224 (AugReg2)
| Property | Value |
|---|---|
| Parameter Count | 86.6M |
| License | Apache 2.0 |
| Image Size | 224x224 |
| GMACs | 16.9 |
| Training Data | ImageNet-21k + ImageNet-1k |
| Paper | How to train your ViT? |
What is vit_base_patch16_224.augreg2_in21k_ft_in1k?
This is an advanced Vision Transformer (ViT) model that implements state-of-the-art image classification capabilities. Initially trained on ImageNet-21k and fine-tuned on ImageNet-1k, it incorporates enhanced augmentation and regularization techniques developed by Ross Wightman. The model processes images by dividing them into 16x16 patches and employs transformer architecture for feature extraction.
Implementation Details
The model features a sophisticated architecture optimized for 224x224 pixel images, utilizing 86.6M parameters and requiring 16.9 GMACs for inference. It employs a patch-based approach where images are divided into 16x16 patches, processed through transformer layers with additional augmentation strategies.
- Pretrained on ImageNet-21k for robust feature learning
- Fine-tuned on ImageNet-1k with advanced augmentation
- Optimized for both classification and feature extraction tasks
- Supports both F32 tensor operations
Core Capabilities
- Image Classification with state-of-the-art accuracy
- Feature extraction for downstream tasks
- Efficient processing of 224x224 images
- Robust performance through advanced training techniques
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its advanced training regime combining ImageNet-21k pretraining with sophisticated augmentation and regularization techniques during fine-tuning on ImageNet-1k. This approach results in superior performance compared to standard ViT models.
Q: What are the recommended use cases?
The model is ideal for high-accuracy image classification tasks, feature extraction for transfer learning, and as a backbone for complex computer vision applications. It's particularly well-suited for scenarios requiring robust image understanding at 224x224 resolution.
