DeiT Tiny Patch16 224
| Property | Value |
|---|---|
| Parameter Count | 5.7M |
| License | Apache-2.0 |
| Image Size | 224 x 224 |
| GMACs | 1.3 |
| Paper | Training data-efficient image transformers & distillation through attention |
What is deit_tiny_patch16_224.fb_in1k?
DeiT Tiny is a compact vision transformer model designed by Facebook Research for efficient image classification. It represents a lightweight implementation of the Vision Transformer (ViT) architecture, trained on the ImageNet-1k dataset. With only 5.7M parameters, it offers an excellent balance between performance and computational efficiency.
Implementation Details
The model processes images by dividing them into 16x16 patches and employs a transformer architecture with attention mechanisms. It operates on 224x224 pixel images and features a streamlined architecture optimized for both accuracy and efficiency.
- Efficient patch-based image processing with 16x16 patches
- Transformer-based architecture with attention mechanisms
- Optimized for 224x224 input images
- Includes both classification and feature extraction capabilities
Core Capabilities
- Image Classification with ImageNet-1k classes
- Feature extraction for downstream tasks
- Efficient processing with only 1.3 GMACs
- Support for both inference and feature backbone usage
Frequently Asked Questions
Q: What makes this model unique?
DeiT Tiny stands out for its efficient architecture that maintains good performance while using only 5.7M parameters, making it suitable for resource-constrained environments. It incorporates distillation through attention, allowing it to learn effectively from teacher models.
Q: What are the recommended use cases?
This model is ideal for image classification tasks where computational resources are limited. It's particularly suitable for mobile applications, edge devices, or scenarios requiring real-time processing while maintaining acceptable accuracy levels.
