DINO Vision Transformer (ViT-B/8)
| Property | Value |
|---|---|
| Parameter Count | 85.8M parameters |
| License | Apache-2.0 |
| Architecture | Vision Transformer (Base) |
| Paper | Emerging Properties in Self-Supervised Vision Transformers |
| Training Data | ImageNet-1k |
What is dino-vitb8?
DINO-ViTB8 is a self-supervised Vision Transformer model developed by Facebook Research that processes images as sequences of 8x8 pixel patches. This model represents a significant advancement in self-supervised learning for computer vision tasks, trained on the ImageNet-1k dataset without requiring explicit labels.
Implementation Details
The model implements a BERT-like transformer encoder architecture that processes images at 224x224 resolution, divided into fixed-size 8x8 patches. It includes a special [CLS] token and uses absolute position embeddings to maintain spatial information.
- Self-supervised training methodology using DINO approach
- Base-sized architecture with 85.8M parameters
- F32 tensor type for precise computations
- Patch-based image processing (8x8 pixels)
Core Capabilities
- Feature extraction from images
- Transfer learning for downstream vision tasks
- Image representation learning
- Classification task support via [CLS] token
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its self-supervised training approach using DINO, allowing it to learn meaningful image representations without labeled data. The 8x8 patch size provides a fine-grained analysis of image features, making it particularly effective for detailed visual understanding tasks.
Q: What are the recommended use cases?
The model is best suited for image feature extraction and transfer learning tasks. It can be used as a backbone for various computer vision applications by adding task-specific heads on top of the pre-trained encoder, particularly effective for classification tasks when utilizing the [CLS] token representations.
