DeiT-base-patch16-384
| Property | Value |
|---|---|
| Parameter Count | 87M parameters |
| Model Type | Vision Transformer (ViT) |
| Architecture | BERT-like transformer encoder |
| Input Resolution | 384x384 pixels |
| Top-1 Accuracy | 82.9% |
| Training Dataset | ImageNet-1k |
What is deit-base-patch16-384?
DeiT-base-patch16-384 is a data-efficient variant of the Vision Transformer (ViT) architecture, specifically designed for efficient training on image classification tasks. Developed by Facebook, this model processes images by dividing them into 16x16 pixel patches and applies transformer-based processing to achieve state-of-the-art performance while requiring less computational resources during training.
Implementation Details
The model operates by converting images into sequences of fixed-size patches (16x16 pixels), which are linearly embedded. It includes a special [CLS] token for classification tasks and uses absolute position embeddings. The model was pre-trained at 224x224 resolution and fine-tuned at 384x384 resolution on ImageNet-1k, comprising 1 million images across 1,000 classes.
- Optimized for 384x384 resolution input images
- Uses patch-based image processing (16x16 patches)
- Implements a BERT-like transformer encoder architecture
- Trained on a single 8-GPU node for 3 days
Core Capabilities
- High-accuracy image classification (82.9% top-1 accuracy)
- Efficient training methodology
- Robust feature extraction for downstream tasks
- Seamless integration with PyTorch
- Compatible with standard image classification pipelines
Frequently Asked Questions
Q: What makes this model unique?
DeiT-base-patch16-384 stands out for its efficient training approach while maintaining high accuracy. It achieves 82.9% top-1 accuracy on ImageNet while being more data-efficient than traditional ViT models, making it particularly valuable when computational resources are limited.
Q: What are the recommended use cases?
The model is primarily designed for image classification tasks and can be effectively used for feature extraction in downstream computer vision applications. It's particularly well-suited for applications requiring high-resolution image processing (384x384) and those needing a good balance between accuracy and computational efficiency.
