Vision Transformer (ViT) Base Patch32-384
| Property | Value |
|---|---|
| Parameter Count | 88.3M |
| License | Apache 2.0 |
| Architecture | Vision Transformer (ViT) |
| Paper | Original Paper |
| Training Data | ImageNet-21k, ImageNet-1k |
What is vit-base-patch32-384?
The vit-base-patch32-384 is a Vision Transformer model developed by Google that represents a significant advancement in computer vision. This model processes images by dividing them into 32x32 pixel patches and applies transformer architecture traditionally used in NLP to perform image classification tasks. The model was pre-trained on ImageNet-21k (14M images) and fine-tuned on ImageNet-1k at 384x384 resolution.
Implementation Details
This implementation features a BERT-like transformer encoder architecture specifically adapted for image processing. The model converts images into sequences of fixed-size patches, adds positional embeddings, and includes a special [CLS] token for classification tasks.
- Input Resolution: 384x384 pixels
- Patch Size: 32x32 pixels
- Pre-training Resolution: 224x224
- Fine-tuning Resolution: 384x384
- Normalization: RGB channels normalized with mean (0.5, 0.5, 0.5) and std (0.5, 0.5, 0.5)
Core Capabilities
- High-accuracy image classification across 1,000 ImageNet classes
- Feature extraction for downstream computer vision tasks
- Efficient processing of high-resolution images
- State-of-the-art performance on various image recognition benchmarks
Frequently Asked Questions
Q: What makes this model unique?
This model is unique in its approach to image processing by treating images as sequences of patches and applying transformer architecture, which was traditionally used for text processing. The 384x384 resolution and 32x32 patch size make it particularly effective for detailed image analysis.
Q: What are the recommended use cases?
The model is well-suited for image classification tasks, feature extraction, and transfer learning applications. It's particularly effective for scenarios requiring high-resolution image processing and can be fine-tuned for specific domain applications.
