DeiT-Tiny-Patch16-224
| Property | Value |
|---|---|
| Parameters | 5M |
| License | Apache 2.0 |
| Paper | Training data-efficient image transformers & distillation through attention |
| ImageNet Accuracy | 72.2% (Top-1) |
What is deit-tiny-patch16-224?
DeiT-tiny is a data-efficient Vision Transformer (ViT) model designed for image classification tasks. Developed by Facebook Research, it represents a more efficient approach to training transformer models for computer vision. The model processes images as 16x16 pixel patches and operates at a 224x224 resolution.
Implementation Details
The model employs a BERT-like transformer encoder architecture, treating images as sequences of patches. It includes a special [CLS] token for classification tasks and uses absolute position embeddings. The tiny variant contains only 5M parameters while maintaining impressive performance.
- Efficient patch-based image processing (16x16 patches)
- Pre-trained on ImageNet-1k dataset (1M images, 1k classes)
- Optimized training procedure on 8-GPU system
- Implements attention-based learning mechanisms
Core Capabilities
- Image classification with 72.2% top-1 accuracy on ImageNet
- Feature extraction for downstream tasks
- Efficient inference with minimal parameter count
- Compatible with standard PyTorch implementations
Frequently Asked Questions
Q: What makes this model unique?
DeiT-tiny stands out for its efficient training approach and small parameter count (5M) while maintaining competitive performance. It demonstrates that transformer architectures can be effectively scaled down for practical applications.
Q: What are the recommended use cases?
The model is ideal for image classification tasks where computational resources are limited. It's particularly suitable for deployment in production environments that require a balance between accuracy and efficiency.
