Vision Transformer (ViT) Large Patch32
| Property | Value |
|---|---|
| Model Type | Vision Transformer |
| Developer | |
| Training Data | ImageNet-21k (14M images) |
| Resolution | 224x224 pixels |
| Patch Size | 32x32 pixels |
| Model Hub | Hugging Face |
What is vit-large-patch32-224-in21k?
The vit-large-patch32-224-in21k is a large-scale Vision Transformer model designed for computer vision tasks. It implements a BERT-like transformer encoder architecture that processes images by splitting them into fixed-size patches and treating these patches as tokens in a sequence. The model has been pretrained on ImageNet-21k, encompassing 14 million images across 21,843 classes, making it particularly robust for various image recognition tasks.
Implementation Details
This model processes images by dividing them into 32x32 pixel patches and linearly embedding them. A special [CLS] token is added at the sequence start for classification tasks, and absolute position embeddings are incorporated before feeding the sequence through the Transformer encoder layers. The model includes a pre-trained pooler but does not provide fine-tuned heads, as these were intentionally zeroed by Google researchers.
- Input Resolution: 224x224 pixels
- Patch Size: 32x32 pixels
- Preprocessing: Normalization with mean (0.5, 0.5, 0.5) and std (0.5, 0.5, 0.5)
- Training Infrastructure: TPUv3 hardware (8 cores)
- Batch Size: 4096
- Learning Rate Warmup: 10k steps
Core Capabilities
- Image Classification
- Feature Extraction
- Transfer Learning
- Downstream Task Adaptation
- High-Resolution Image Processing
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its large-scale architecture and extensive pretraining on ImageNet-21k. The 32x32 patch size allows for efficient processing of high-resolution images while maintaining computational efficiency. The model's transformer-based architecture enables it to capture complex relationships between image patches effectively.
Q: What are the recommended use cases?
The model is particularly well-suited for image classification tasks and feature extraction. It can be fine-tuned for specific downstream tasks by adding a linear layer on top of the [CLS] token's output. For optimal results, the model performs best when fine-tuned at higher resolutions (384x384) for specific tasks.
