Vision Transformer (ViT) Large Patch32-384
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Research Paper | An Image is Worth 16x16 Words |
| Author | |
| Training Data | ImageNet-21k, ImageNet 2012 |
What is vit-large-patch32-384?
The Vision Transformer (ViT) Large Patch32-384 is a state-of-the-art transformer-based model designed for image classification tasks. It implements a BERT-like architecture that processes images as sequences of 32x32 pixel patches, with pre-training on ImageNet-21k (14M images) and fine-tuning on ImageNet 2012 at 384x384 resolution.
Implementation Details
This model processes images by dividing them into fixed-size patches of 32x32 pixels, which are linearly embedded along with position embeddings. A special [CLS] token is prepended to the sequence for classification tasks. The model operates at a high resolution of 384x384 pixels during fine-tuning, enabling detailed feature extraction.
- Pre-trained on ImageNet-21k with 14M images and 21,843 classes
- Fine-tuned on ImageNet 2012 with 1M images and 1,000 classes
- Uses normalized RGB channels (mean: 0.5, std: 0.5)
- Trained on TPUv3 hardware with 8 cores and 4096 batch size
Core Capabilities
- High-resolution image classification (384x384)
- Feature extraction for downstream tasks
- Support for transfer learning
- Robust performance on standard vision benchmarks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its large-scale architecture and high-resolution processing capability. It's particularly notable for using transformer architecture, traditionally used in NLP, for computer vision tasks, showing excellent performance on image classification benchmarks.
Q: What are the recommended use cases?
The model is best suited for high-quality image classification tasks, feature extraction, and transfer learning applications. It's particularly effective when working with high-resolution images and when precise classification is required.
