DeiT Base Patch16-224
| Property | Value |
|---|---|
| Parameter Count | 86M |
| License | Apache 2.0 |
| Paper | Training data-efficient image transformers |
| Accuracy | 81.8% (Top-1 ImageNet) |
| Input Resolution | 224x224 pixels |
What is deit-base-patch16-224?
DeiT-base-patch16-224 is a data-efficient Vision Transformer (ViT) model developed by Facebook Research. It represents a significant advancement in efficient transformer training for computer vision tasks, particularly designed for image classification on the ImageNet-1k dataset. The model processes images by dividing them into 16x16 pixel patches and employs a transformer architecture to analyze the relationships between these patches.
Implementation Details
The model implements a BERT-like transformer encoder architecture specifically optimized for image processing. It operates by converting images into sequences of fixed-size patches (16x16 pixels), which are linearly embedded along with position embeddings. A special [CLS] token is added at the sequence start for classification tasks.
- Trained on ImageNet-1k with 1 million images across 1,000 classes
- Uses 224x224 pixel input resolution
- Implements efficient training strategies to reduce computational requirements
- Achieves 81.8% top-1 and 95.6% top-5 accuracy on ImageNet
Core Capabilities
- High-accuracy image classification
- Efficient training and inference
- Feature extraction for downstream tasks
- Support for transfer learning applications
Frequently Asked Questions
Q: What makes this model unique?
DeiT's uniqueness lies in its data-efficient training approach, allowing it to achieve competitive performance with less computational resources compared to traditional Vision Transformers. It effectively combines the benefits of transformer architecture with efficient training techniques.
Q: What are the recommended use cases?
The model is primarily designed for image classification tasks but can be effectively used for feature extraction in various computer vision applications. It's particularly suitable for scenarios requiring high accuracy with reasonable computational resources.
