ViT Base Patch32-224-In21k
| Property | Value |
|---|---|
| Developer | |
| Training Dataset | ImageNet-21k (14M images) |
| Input Resolution | 224x224 pixels |
| Patch Size | 32x32 pixels |
| Model Type | Vision Transformer |
What is vit-base-patch32-224-in21k?
The Vision Transformer (ViT) base model is a revolutionary approach to computer vision that adapts the Transformer architecture, traditionally used in NLP, for image processing. This particular model is pre-trained on ImageNet-21k, processing images as sequences of 32x32 pixel patches. It incorporates a BERT-like transformer encoder structure and includes a specialized [CLS] token for classification tasks.
Implementation Details
The model processes images by dividing them into fixed-size patches of 32x32 pixels, which are then linearly embedded. It uses a pre-trained pooler and absolute position embeddings, making it particularly effective for downstream tasks. The model was trained on TPUv3 hardware with a batch size of 4096 and includes gradient clipping at global norm 1.
- Pre-trained on 14 million images across 21,843 classes
- Processes images at 224x224 resolution
- Implements normalization with mean and std dev of 0.5 across RGB channels
- Features a BERT-like transformer encoder architecture
Core Capabilities
- Image classification and feature extraction
- Transfer learning for downstream vision tasks
- Flexible integration with custom classification heads
- Robust image representation learning
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its transformer-based approach to computer vision, breaking away from traditional CNN architectures. It's pre-trained on an extensive dataset of 14M images and can process images as sequences of patches, making it highly effective for various vision tasks.
Q: What are the recommended use cases?
The model is ideal for image classification tasks, feature extraction, and as a backbone for transfer learning in computer vision applications. It's particularly effective when fine-tuned on domain-specific datasets, especially at higher resolutions like 384x384 for optimal performance.
