| Property | Value |
|---|---|
| Parameter Count | 88.2M |
| License | Apache 2.0 |
| Image Size | 224x224 |
| GMACs | 4.4 |
| Paper | How to train your ViT? |
What is vit_base_patch32_224.augreg_in21k_ft_in1k?
This is a Vision Transformer (ViT) model specifically designed for image classification tasks. Initially trained on the extensive ImageNet-21k dataset and subsequently fine-tuned on ImageNet-1k, this model incorporates advanced augmentation and regularization techniques. Originally implemented in JAX by the paper authors and later ported to PyTorch by Ross Wightman, it represents a sophisticated approach to computer vision tasks.
Implementation Details
The model employs a patch-based approach to image processing, dividing input images into 32x32 patches. With 88.2M parameters and 4.4 GMACs, it strikes a balance between computational efficiency and performance. The architecture processes 224x224 pixel images and generates 4.2M activations during operation.
- Pretrained on ImageNet-21k for robust feature extraction
- Fine-tuned on ImageNet-1k with augmentation
- Implements the transformer architecture for vision tasks
- Supports both classification and embedding extraction
Core Capabilities
- Image classification with 1000 classes
- Feature extraction for downstream tasks
- Efficient handling of 224x224 resolution images
- Support for batch processing and inference
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its specialized training approach combining ImageNet-21k pretraining with carefully tuned augmentation and regularization strategies during ImageNet-1k fine-tuning. The patch size of 32x32 offers a good balance between computational efficiency and performance.
Q: What are the recommended use cases?
The model is ideal for image classification tasks, particularly when dealing with standard resolution images. It can be used for direct classification or as a feature extractor for transfer learning applications. The model is particularly suitable for scenarios requiring robust image understanding with reasonable computational requirements.