vit_base_patch16_224.dino
| Property | Value |
|---|---|
| Parameter Count | 85.8M |
| License | Apache-2.0 |
| Framework | PyTorch (timm) |
| Image Size | 224x224 |
| GMACs | 16.9 |
| Paper | Emerging Properties in Self-Supervised Vision Transformers |
What is vit_base_patch16_224.dino?
vit_base_patch16_224.dino is a Vision Transformer (ViT) model trained using the self-supervised DINO method. It represents a powerful approach to image feature extraction and classification, utilizing a transformer architecture that processes images by splitting them into 16x16 pixel patches.
Implementation Details
The model implements a transformer-based architecture with 85.8M parameters, trained on ImageNet-1k. It processes 224x224 pixel images by dividing them into 16x16 patches, creating a sequence of visual tokens that are then processed through the transformer layers.
- Activation Memory: 16.5M
- Computational Complexity: 16.9 GMACs
- Patch Size: 16x16 pixels
- Input Resolution: 224x224
Core Capabilities
- Image Feature Extraction
- Self-supervised Learning
- Image Classification
- Transfer Learning
- Visual Representation Learning
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its self-supervised training approach using DINO, which allows it to learn meaningful visual representations without requiring labeled data. The architecture combines the efficiency of transformers with the capability to process visual information effectively.
Q: What are the recommended use cases?
The model is particularly well-suited for tasks requiring high-quality image feature extraction, including transfer learning applications, image classification, and computer vision tasks where pre-trained visual representations are valuable. It can be used both as a feature extractor and a classifier.
