Vision Transformer (ViT) Huge Model
| Property | Value |
|---|---|
| Parameter Count | 632M parameters |
| License | Apache 2.0 |
| Training Data | ImageNet-21k (14M images) |
| Original Paper | An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale |
| Architecture | Vision Transformer (Huge) |
What is vit-huge-patch14-224-in21k?
The vit-huge-patch14-224-in21k is Google's implementation of a Vision Transformer (ViT) model, representing a significant advancement in computer vision. This huge-sized variant processes images by dividing them into 14x14 pixel patches and employs transformer architecture originally designed for natural language processing tasks. The model has been pre-trained on ImageNet-21k, encompassing 14 million images across 21,843 classes at a 224x224 pixel resolution.
Implementation Details
The model processes images by first converting them into sequences of fixed-size patches (14x14 pixels), which are then linearly embedded. A special [CLS] token is prepended to the sequence for classification tasks, and absolute position embeddings are added before the sequence is processed through the transformer encoder layers.
- Pre-trained on TPUv3 hardware (8 cores)
- Batch size of 4096 with 10k steps learning rate warmup
- Gradient clipping at global norm 1
- Image normalization with mean and std of (0.5, 0.5, 0.5)
Core Capabilities
- High-quality image feature extraction
- Support for transfer learning to downstream tasks
- Flexible integration with PyTorch frameworks
- Handles 224x224 resolution images with state-of-the-art performance
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its massive scale (632M parameters) and its innovative approach to image processing using transformer architecture. It's one of the largest publicly available vision transformers, trained on an extensive dataset of 14 million images.
Q: What are the recommended use cases?
The model is particularly well-suited for image classification tasks, feature extraction, and transfer learning applications. It can be used as a backbone for various computer vision tasks by adding task-specific heads on top of the pre-trained encoder.
