Vision Transformer (ViT) Large Patch16-224
| Property | Value |
|---|---|
| Author | |
| License | Apache 2.0 |
| Paper | Original Paper |
| Training Data | ImageNet-21k, ImageNet-1K |
What is vit-large-patch16-224?
The Vision Transformer (ViT) Large model is a sophisticated transformer-based architecture designed for image classification tasks. It processes images by dividing them into 16x16 pixel patches and treating these patches as tokens in a transformer sequence. The model was pre-trained on ImageNet-21k with 14 million images across 21,843 classes and fine-tuned on ImageNet-1K containing 1 million images across 1,000 classes.
Implementation Details
This implementation uses a large-scale transformer architecture that processes images at 224x224 resolution. The model employs a patch-based approach where images are divided into fixed-size patches (16x16 pixels) that are linearly embedded. A special [CLS] token is added at the sequence start for classification tasks, and absolute position embeddings are incorporated before processing through the transformer encoder.
- Pre-trained on ImageNet-21k (14M images)
- Fine-tuned on ImageNet-1K (1M images)
- Uses 16x16 pixel patches for image processing
- Operates at 224x224 resolution
- Implements absolute position embeddings
Core Capabilities
- High-performance image classification
- Feature extraction for downstream tasks
- Transfer learning capabilities
- Batch processing with 4096 samples
- Normalized preprocessing across RGB channels
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its pure transformer-based approach to computer vision, breaking away from traditional convolutional architectures. It demonstrates that transformers can be effectively applied to image recognition tasks at scale, achieving excellent performance on ImageNet classification.
Q: What are the recommended use cases?
The model is primarily designed for image classification tasks but can be adapted for various computer vision applications through transfer learning. It's particularly effective for tasks requiring high-level image understanding and classification across many categories.
