Vision Transformer Base Patch16 224
| Property | Value |
|---|---|
| Parameter Count | 85.8M |
| License | Apache-2.0 |
| Research Paper | Link |
| Image Size | 224 x 224 |
| Training Dataset | ImageNet-21k |
What is vit_base_patch16_224.orig_in21k?
This is a Vision Transformer (ViT) model originally developed by Google Research and ported to PyTorch by Ross Wightman. It represents a groundbreaking approach to image classification that applies transformer architecture, traditionally used in NLP, to computer vision tasks. The model processes images by splitting them into 16x16 pixel patches and treating these patches as tokens.
Implementation Details
The model architecture features a robust transformer-based design with 85.8M parameters, operating on 224x224 pixel images. It achieves 16.9 GMACs efficiency and maintains 16.5M activations during processing. Originally trained on the extensive ImageNet-21k dataset, this implementation is particularly suited for feature extraction and fine-tuning tasks.
- Pre-trained on ImageNet-21k for comprehensive visual understanding
- Processes images using 16x16 pixel patches
- Supports both classification and feature extraction workflows
Core Capabilities
- Image classification with state-of-the-art accuracy
- Feature extraction for downstream tasks
- Flexible integration with PyTorch workflows
- Support for both inference and fine-tuning
Frequently Asked Questions
Q: What makes this model unique?
This model is unique in its original training on ImageNet-21k and its architecture that effectively applies transformer mechanics to vision tasks. It provides a strong foundation for transfer learning and feature extraction without a classification head.
Q: What are the recommended use cases?
The model excels in image classification tasks, feature extraction for downstream applications, and as a backbone for fine-tuning on specific domains. It's particularly valuable when working with complex visual hierarchies due to its ImageNet-21k pre-training.
