vit_base_patch14_reg4_dinov2.lvd142m
| Property | Value |
|---|---|
| Parameter Count | 86.6M |
| Model Type | Vision Transformer (ViT) |
| License | Apache-2.0 |
| Image Size | 518 x 518 |
| Framework | PyTorch (timm) |
What is vit_base_patch14_reg4_dinov2.lvd142m?
This is an advanced Vision Transformer model that incorporates registers, representing a significant evolution in computer vision architectures. Trained using the self-supervised DINOv2 method on the LVD-142M dataset, it's specifically designed for robust image feature extraction and classification tasks.
Implementation Details
The model utilizes a patch size of 14x14 pixels and includes register-based enhancements that improve its feature extraction capabilities. With 86.6M parameters and 117.5 GMACs, it offers a balance between computational efficiency and performance. The architecture processes images of size 518x518 pixels, making it suitable for high-resolution image analysis.
- Incorporates register-based architecture for enhanced feature learning
- Trained using self-supervised DINOv2 methodology
- Optimized for both classification and feature extraction tasks
Core Capabilities
- High-quality image feature extraction
- Robust visual representation learning
- Support for both classification and embedding generation
- Efficient processing of high-resolution images
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its integration of registers into the Vision Transformer architecture, which enhances its ability to capture and process visual information. The combination of register-based architecture with DINOv2 training methodology results in robust visual features without requiring supervised training.
Q: What are the recommended use cases?
The model is particularly well-suited for tasks requiring high-quality image feature extraction, including image classification, visual similarity search, and transfer learning applications. It's especially effective when working with high-resolution images and when robust visual feature representation is crucial.
