vit_base_patch14_dinov2.lvd142m
| Property | Value |
|---|---|
| Parameter Count | 86.6M |
| Input Size | 518 x 518 |
| License | Apache-2.0 |
| Framework | PyTorch (timm) |
| Training Dataset | LVD-142M |
What is vit_base_patch14_dinov2.lvd142m?
This is a Vision Transformer (ViT) model trained using the innovative DINOv2 self-supervised learning approach. Built on the foundation of the ViT architecture, it processes images by dividing them into 14x14 patches and employs transformer mechanisms to extract robust visual features without requiring labeled data.
Implementation Details
The model leverages a base-sized ViT architecture with 86.6M parameters and operates on 518x518 pixel images. It processes these through patch embeddings and transformer layers to generate high-quality feature representations, making it particularly suitable for downstream computer vision tasks.
- Self-supervised training on LVD-142M dataset
- 14x14 patch size for image tokenization
- 151.7 GMACs computational requirement
- 397.6M activations
Core Capabilities
- Image feature extraction and embedding generation
- Support for both classification and feature backbone usage
- Flexible integration with PyTorch workflows via timm library
- Robust visual representation learning without supervision
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its training with DINOv2, a state-of-the-art self-supervised learning method, allowing it to learn powerful visual representations without requiring labeled data. The combination of ViT architecture with 14x14 patch size and training on the large-scale LVD-142M dataset makes it particularly effective for various computer vision tasks.
Q: What are the recommended use cases?
The model excels in scenarios requiring high-quality image feature extraction, such as transfer learning, image similarity search, and visual representation learning. It's particularly valuable when labeled data is scarce, as it can provide rich feature embeddings that can be fine-tuned for specific downstream tasks.
