DeiT Small Patch16 224
| Property | Value |
|---|---|
| Parameter Count | 22.1M |
| Image Size | 224 x 224 |
| License | Apache 2.0 |
| Paper | Training data-efficient image transformers & distillation through attention |
| Dataset | ImageNet-1k |
What is deit_small_patch16_224.fb_in1k?
DeiT (Data-efficient image Transformers) is a vision transformer model designed for efficient image classification. This small variant processes images by dividing them into 16x16 patches and utilizes attention mechanisms for feature learning, while maintaining a balanced trade-off between performance and computational resources.
Implementation Details
The model architecture is based on the Vision Transformer (ViT) framework but incorporates distillation through attention techniques. It processes 224x224 pixel images, dividing them into 16x16 patches, resulting in 196 patches plus one classification token. The model achieves efficient training through innovative attention distillation mechanisms.
- 22.1M trainable parameters
- 4.6 GMACs computational requirement
- 11.9M activation size
- Pretrained on ImageNet-1k dataset
Core Capabilities
- Image Classification with high efficiency
- Feature extraction backbone
- Supports both classification and embedding generation
- Efficient inference with 224x224 resolution images
Frequently Asked Questions
Q: What makes this model unique?
The model combines the power of transformers with efficient training techniques through attention distillation, making it more practical for real-world applications while maintaining strong performance on ImageNet-1k.
Q: What are the recommended use cases?
This model is ideal for image classification tasks, feature extraction, and as a backbone for downstream computer vision tasks. It's particularly suitable for applications requiring a good balance between model size and performance.
