Vision Transformer (ViT) DINO Base Model
| Property | Value |
|---|---|
| Parameter Count | 85.8M |
| GMACs | 66.9 |
| Input Size | 224x224 |
| Training Method | Self-Supervised DINO |
| Paper | Emerging Properties in Self-Supervised Vision Transformers |
What is vit_base_patch8_224.dino?
The vit_base_patch8_224.dino is a Vision Transformer (ViT) model trained using the self-supervised DINO method on ImageNet-1k. This model represents a significant advancement in computer vision, utilizing a patch-based approach where images are divided into 8x8 pixel patches and processed through a transformer architecture.
Implementation Details
This implementation features a base-sized ViT architecture with 85.8M parameters and requires 66.9 GMACs for inference. The model processes 224x224 pixel images by dividing them into patches and maintains 65.7M activations during processing.
- Patch size: 8x8 pixels
- Self-supervised training using DINO methodology
- Pretrained on ImageNet-1k dataset
- Supports both classification and feature extraction
Core Capabilities
- Image Classification: Can be used for standard classification tasks
- Feature Extraction: Capable of generating rich image embeddings
- Zero-shot Transfer: Demonstrates strong performance on unseen tasks
- Flexible Integration: Easy to use with the timm library
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its self-supervised training approach using DINO, which allows it to learn robust visual representations without requiring labeled data. The 8x8 patch size provides finer granularity compared to typical 16x16 implementations.
Q: What are the recommended use cases?
The model excels in both image classification tasks and as a feature extractor for downstream tasks. It's particularly useful when you need high-quality image embeddings or transfer learning capabilities for computer vision applications.
