Official DeiT repository. Contribute to facebookresearch/deit development by creating an account on GitHub.

DeiT (data-efficient image transformers) are more efficiently trained transformers for image classification, requiring far less data and far less computing resources compared to the original …

Feb 9, 2025 · Data-Efficient Image Transformer (DeiT) [1] is a significant step forward from the original Vision Transformer (ViT) [2] by addressing its major shortcoming: the reliance on …

Dec 23, 2020 · Our new technique — Data-efficient image Transformers (DeiT) — requires far less data and far less computing resources to produce a high-performance image …

Apr 7, 2021 · DeiT are image transformers that do not require large amounts of data to be trained thanks to improved training methods and a novel distillation procedure.

Dec 23, 2020 · Recently, neural networks purely based on attention were shown to address image understanding tasks such as image classification. However, these visual transformers …

Introduced to address the challenge of achieving strong performance with limited labeled data, DeiT leverages distillation techniques and large-scale unlabeled datasets during training.

DeiT was able to out-perform classic ViT and Convolutional networks, of comparable sizes, for image classification on ImageNet, without any external data. The rest of the article covers the …

Data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper …

Official DeiT repository. Contribute to facebookresearch/deit development by creating an account on GitHub.