| Property | Value |
|---|---|
| Parameter Count | 86.9M |
| License | Apache 2.0 |
| Architecture | Vision Transformer |
| Input Resolution | 384x384 pixels |
| Paper | Original Paper |
What is vit-base-patch16-384?
The vit-base-patch16-384 is a Vision Transformer model that represents a significant advancement in computer vision. Originally developed by Google, this model processes images by dividing them into 16x16 pixel patches and treating them as a sequence of tokens, similar to how language transformers process words. The model was pre-trained on ImageNet-21k (14M images) and fine-tuned on ImageNet-1k at 384x384 resolution.
Implementation Details
This implementation uses a transformer encoder architecture with patch embeddings and position encodings. The model processes images at 384x384 resolution, dividing them into 16x16 pixel patches. It includes a special [CLS] token for classification tasks and uses absolute position embeddings.
- Pre-trained on ImageNet-21k with 21,843 classes
- Fine-tuned on ImageNet 2012 with 1,000 classes
- Uses F32 tensor type for computations
- Implements patch-based image processing
Core Capabilities
- High-resolution image classification (384x384)
- Feature extraction for downstream tasks
- Transfer learning capabilities
- Robust performance on standard vision benchmarks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its transformer-based architecture applied to computer vision, eliminating the need for conventional CNN architectures. It processes images as sequences of patches, achieving strong performance while maintaining computational efficiency.
Q: What are the recommended use cases?
The model is ideal for image classification tasks, feature extraction, and transfer learning applications. It performs particularly well on high-resolution images and can be fine-tuned for specific domain applications.