vit_base_patch16_224.augreg_in21k
| Property | Value |
|---|---|
| Parameter Count | 103M |
| Model Type | Vision Transformer (ViT) |
| Training Dataset | ImageNet-21k |
| License | Apache-2.0 |
| Paper | How to train your ViT? |
What is vit_base_patch16_224.augreg_in21k?
This is a Vision Transformer (ViT) model specifically designed for image classification tasks. Originally trained in JAX by the paper authors and later ported to PyTorch by Ross Wightman, it represents a sophisticated approach to computer vision using transformer architecture. The model processes 224x224 pixel images by dividing them into 16x16 patches and applies enhanced augmentation and regularization techniques during training.
Implementation Details
The model architecture features 102.6M parameters and requires 16.9 GMACs for inference. It processes images by converting them into a sequence of 16x16 patches, which are then processed through a transformer architecture. The model outputs feature vectors of dimension 768 and can be used both for classification and embedding generation.
- Image input size: 224 x 224 pixels
- Patch size: 16x16 pixels
- Activation size: 16.5M
- Feature dimension: 768
Core Capabilities
- Image Classification with 21k classes support
- Feature extraction and embedding generation
- Transfer learning potential for downstream tasks
- Efficient processing of high-resolution images
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its advanced training methodology incorporating additional augmentation and regularization techniques. It's trained on the extensive ImageNet-21k dataset, making it particularly robust for diverse image classification tasks.
Q: What are the recommended use cases?
The model is ideal for image classification tasks, particularly when dealing with complex scenes or when transfer learning to domain-specific applications is needed. It's also excellent for generating image embeddings for downstream tasks.
