Vision Transformer (ViT) Large Patch16
| Property | Value |
|---|---|
| Parameter Count | 304.3M |
| Model Type | Vision Transformer |
| License | Apache-2.0 |
| Image Size | 224 x 224 |
| GMACs | 59.7 |
| Paper | How to train your ViT? |
What is vit_large_patch16_224.augreg_in21k_ft_in1k?
This is a large-scale Vision Transformer model that represents a significant advancement in computer vision. Originally trained on ImageNet-21k and fine-tuned on ImageNet-1k, this model implements advanced augmentation and regularization techniques to achieve superior performance in image classification tasks.
Implementation Details
The model utilizes a patch-based approach where images are divided into 16x16 pixel patches and processed through a transformer architecture. With 304.3M parameters, it offers substantial modeling capacity while maintaining efficient processing through its attention-based mechanism.
- Pre-trained on ImageNet-21k (14M images, 21k classes)
- Fine-tuned on ImageNet-1k with augmentation
- Implements patch-based image processing (16x16)
- Features 43.8M activations
Core Capabilities
- High-accuracy image classification
- Feature extraction for downstream tasks
- Support for 224x224 pixel input images
- Both classification and embedding generation
Frequently Asked Questions
Q: What makes this model unique?
This model combines extensive pre-training on ImageNet-21k with sophisticated augmentation and regularization techniques, making it particularly robust for real-world applications. Its large parameter count enables capturing complex visual patterns effectively.
Q: What are the recommended use cases?
The model excels in image classification tasks and can be used for feature extraction in transfer learning scenarios. It's particularly suitable for applications requiring high accuracy and robust feature representation, such as fine-grained classification or visual recognition systems.
