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The content discusses the Efficient Hybrid Encoder in the RT-DETR model for object detection. The encoder transforms features into a sequence through intra-scale feature interaction and cross-scale feature fusion. Different variants of the encoder are explored, with the most efficient design being Variant E. This variant enhances intra-scale interaction and cross-scale fusion using an efficient hybrid encoder. Two components, AIFI and CCFF, are proposed to improve the encoder’s performance.
AIFI focuses on reducing computational costs by performing intra-scale interaction on specific features, resulting in reduced latency and improved accuracy. CCFF uses fusion blocks to merge adjacent scale features and employs RepBlocks for feature fusion. The uncertainty-minimal query selection method is introduced to select encoder features with minimal uncertainty, improving the initialization for the decoder.
The content also discusses the design of Scaled RT-DETR, where the width and depth of the encoder and decoder can be adjusted by manipulating various parameters. The speed of RT-DETR is flexible and can be controlled by adjusting the number of decoder layers. Overall, the content highlights the importance of efficient encoder design and feature selection for optimal performance in object detection models.
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Source link: https://sh-tsang.medium.com/review-detrs-beat-yolos-on-real-time-object-detection-9d10b5bccf9b?source=rss——artificial_intelligence-5
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