vit_large_patch14_reg4_dinov2.lvd142m
| Property | Value |
|---|---|
| Parameter Count | 304.4M |
| Model Type | Vision Transformer (ViT) |
| License | Apache 2.0 |
| Image Size | 518 x 518 |
| Training Dataset | LVD-142M |
| Architecture | Large ViT with Registers |
What is vit_large_patch14_reg4_dinov2.lvd142m?
This model is an advanced Vision Transformer (ViT) that incorporates registers - a novel architectural enhancement that improves the model's capability for image feature extraction. It was pretrained using the self-supervised DINOv2 method on the extensive LVD-142M dataset, making it particularly robust for visual feature learning without supervision.
Implementation Details
The model utilizes a patch size of 14x14 pixels and implements 4 registers in its architecture. With 304.4M parameters and 416.1 GMACs, it processes images at 518x518 resolution. The model leverages the timm library for efficient implementation and provides both classification and embedding extraction capabilities.
- Sophisticated register-based architecture for enhanced feature extraction
- Self-supervised training using DINOv2 methodology
- Optimized for high-resolution image processing
- Supports both classification and embedding generation
Core Capabilities
- Image classification with high accuracy
- Feature extraction for downstream tasks
- Robust visual representation learning
- Flexible deployment options through timm library
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its register-based architecture, which enhances the traditional ViT design, and its training on the large-scale LVD-142M dataset using the advanced DINOv2 self-supervised learning approach.
Q: What are the recommended use cases?
The model excels in image feature extraction tasks, making it ideal for transfer learning, image classification, and visual representation learning. It's particularly suitable for applications requiring robust visual feature understanding without supervised training.
