
ViTDet
Overview
The ViTDet model was proposed in Exploring Plain Vision Transformer Backbones for Object Detection by Yanghao Li, Hanzi Mao, Ross Girshick, Kaiming He.
VitDet leverages the plain Vision Transformer for the task of object detection.
The abstract from the paper is the following:
We explore the plain, non-hierarchical Vision Transformer (ViT) as a backbone network for object detection. This design enables the original ViT architecture to be fine-tuned for object detection without needing to redesign a hierarchical backbone for pre-training. With minimal adaptations for fine-tuning, our plain-backbone detector can achieve competitive results. Surprisingly, we observe: (i) it is sufficient to build a simple feature pyramid from a single-scale feature map (without the common FPN design) and (ii) it is sufficient to use window attention (without shifting) aided with very few cross-window propagation blocks. With plain ViT backbones pre-trained as Masked Autoencoders (MAE), our detector, named ViTDet, can compete with the previous leading methods that were all based on hierarchical backbones, reaching up to 61.3 AP_box on the COCO dataset using only ImageNet-1K pre-training. We hope our study will draw attention to research on plain-backbone detectors.
This model was contributed by nielsr.
The original code can be found here.
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