vit_large_patch14_dinov2.lvd142m
| Property | Value |
|---|---|
| Parameter Count | 304M |
| Architecture | Vision Transformer (ViT) |
| License | Apache-2.0 |
| Image Size | 518 x 518 |
| Training Dataset | LVD-142M |
What is vit_large_patch14_dinov2.lvd142m?
This is a large-scale Vision Transformer model trained using the innovative DINOv2 self-supervised learning method. It represents a significant advancement in computer vision, capable of extracting robust visual features without traditional supervision. The model processes images by dividing them into 14x14 patches and employs a transformer architecture to analyze spatial relationships.
Implementation Details
The model features 304.4M parameters and operates with F32 tensor precision. It processes images at 518x518 resolution, utilizing 507.1 GMACs and 1058.8M activations. The architecture builds upon the original ViT design while incorporating DINOv2's self-supervised learning improvements.
- Patch-based image processing (14x14 patches)
- Self-supervised training on LVD-142M dataset
- Optimized for feature extraction tasks
- Compatible with PyTorch and timm library
Core Capabilities
- High-quality image feature extraction
- Support for both classification and embedding generation
- Flexible integration through timm API
- Robust visual representation learning
Frequently Asked Questions
Q: What makes this model unique?
This model combines the powerful ViT architecture with DINOv2's self-supervised learning approach, enabling it to learn robust visual features without requiring labeled data. The large parameter count (304M) and training on the extensive LVD-142M dataset make it particularly effective for feature extraction tasks.
Q: What are the recommended use cases?
The model excels in image feature extraction tasks, making it ideal for transfer learning, image similarity comparison, and as a backbone for downstream computer vision tasks. It can be used both for classification and for generating image embeddings.
