| Property | Value |
|---|---|
| Parameter Count | 87M |
| License | Apache 2.0 |
| Paper | Training data-efficient image transformers & distillation through attention |
| Top-1 Accuracy | 83.4% |
| Architecture | Vision Transformer with Distillation |
What is deit-base-distilled-patch16-224?
DeiT-base-distilled is a sophisticated Vision Transformer model developed by Facebook Research that implements an innovative distillation approach for image classification. The model processes images as 16x16 pixel patches and utilizes a unique distillation token alongside the traditional class token to learn effectively from a teacher CNN model.
Implementation Details
This model represents a significant advancement in efficient transformer training for computer vision tasks. It was trained on ImageNet-1k using an 8-GPU setup over three days, processing images at 224x224 resolution.
- Implements patch-based image processing (16x16 patches)
- Uses distillation token for teacher-student learning
- Achieves 83.4% top-1 accuracy on ImageNet
- Optimized for 224x224 resolution images
Core Capabilities
- High-performance image classification
- Efficient training through distillation
- Flexible integration with PyTorch workflows
- Robust feature extraction for downstream tasks
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its distillation approach, using a specific distillation token that learns through interaction with class and patch tokens via self-attention layers. This enables efficient knowledge transfer from a teacher model while maintaining strong performance.
Q: What are the recommended use cases?
This model is ideal for image classification tasks requiring high accuracy and efficiency. It's particularly well-suited for production environments where both performance and computational efficiency are important considerations.