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

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 …

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 …

Aimv2 BEiT BiT Conditional DETR ConvNeXT ConvNeXTV2 CvT D-FINE DAB-DETR Deformable DETR DeiT Depth Anything Depth Anything V2 DepthPro DETA DETR DiNAT DINOV2 …

Jul 12, 2024 · DeiT (Data-efficient Image Transformer) is an advanced vision transformer model designed to achieve high performance in image classification tasks with significantly less data …

Oct 17, 2024 · DeiT is a new VIT method to handle the problem of VIT requiring a large amount of data. It incorporates a distillation token and learns from the teacher model's prediction to …

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

Today we are going to implement Training data-efficient image transformers & distillation through attention a new method to perform knowledge distillation on Vision Transformers called DeiT.

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.

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 …