DeiT Base Distilled Vision Transformer
| Property | Value |
|---|---|
| Parameter Count | 87.3M |
| License | Apache-2.0 |
| Framework | PyTorch (timm) |
| Paper | Training data-efficient image transformers & distillation through attention |
| Image Size | 224 x 224 |
What is deit_base_distilled_patch16_224.fb_in1k?
This is a Data-efficient Image Transformer (DeiT) model developed by Facebook Research, specifically designed for efficient image classification. It employs a unique distillation approach through attention mechanisms, using patch-based image processing with 16x16 pixel patches. The model has been trained on the ImageNet-1k dataset and demonstrates strong performance while maintaining computational efficiency.
Implementation Details
The model architecture is based on the Vision Transformer paradigm with several key optimizations. It processes images by dividing them into 16x16 pixel patches and uses distillation tokens to improve training efficiency. The model requires 17.7 GMACs for inference and maintains 24.0M activations.
- Utilizes patch-based image processing (16x16 patches)
- Implements distillation through attention mechanisms
- Optimized for ImageNet-1k dataset
- Supports both classification and feature extraction
Core Capabilities
- Image Classification with high accuracy
- Feature Extraction for downstream tasks
- Efficient processing of 224x224 pixel images
- Distillation-based knowledge transfer
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its implementation of distillation tokens within the attention mechanism, allowing for more efficient training while maintaining high performance. The architecture balances computational efficiency with accuracy, making it suitable for production environments.
Q: What are the recommended use cases?
The model is ideal for image classification tasks, particularly when working with standardized 224x224 pixel images. It can be used for both direct classification and as a feature extractor for transfer learning applications. The model is particularly effective when deployment efficiency is a concern while maintaining high accuracy requirements.
