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Hybrid RT DETR: Hybrid encoder-decoder network for end-to-end object detection in UAV imagery

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Hybrid-RT-DETR

Hybrid encoder-decoder network for end-to-end object detection in UAV imagery

基于多尺度增强辅助编解码网络的无人机目标检测算法
Keywords:DETR;多尺度特征融合;辅助目标查询

Results on VisDrone2019

Method mAP $AP_{50}$ $AP_{75}$ $AP_s$ $AP_m$ $AP_l$
YOLOv5m6 27.00 44.4
YOLOv6m 20.90 35.50 20.81 10.43 33.21 57.64
YOLOv7 28.74 49.91
YOLOv8m 27.33 44.71
DAB-deformable-detr-R50 25.72 43.51 25.73 17.30 36.74 45.82
DN-DAB-deformable-detr-R50 27.93 46.00 28.41 19.14 39.67 54.31
Lite-DINO-H3L1-R50 32.79 55.21 32.70 23.38 44.65 58.81
RT-DETR-R18 28.93 48.29 29.21 19.55 40.43 61.00
RT-DETR-R50 32.20 52.85 32.65 22.27 44.87 68.43
本章算法-R18 30.57 50.78 30.91 21.40 42.38 59.70
本章算法-R50 33.14 54.43 33.89 23.62 45.94 63.08

模型在TITAN RTX上的推理耗时 FP32/wo TensorRT

模型 平均推理耗时(秒)
baseline_r18 0.027
ours_r18 0.048
baseline_r50 0.048
ours_r50 0.055

Quick start

Install
pip install -r requirements.txt
Data

Download VisDrone and convert it to COCO format annonations of train and val data.

Training & Evaluation
  • Training on a Single GPU:
# training on single-gpu
export CUDA_VISIBLE_DEVICES=0
python tools/train.py -c configs/rtdetr/rtdetr_r50vd_6x_coco.yml
  • Training on Multiple GPUs:
# train on multi-gpu
export CUDA_VISIBLE_DEVICES=0,1,2,3
torchrun --nproc_per_node=4 tools/train.py -c configs/rtdetr/rtdetr_r50vd_6x_coco.yml
  • Evaluation on Multiple GPUs:
# val on multi-gpu
export CUDA_VISIBLE_DEVICES=0,1,2,3
torchrun --nproc_per_node=4 tools/train.py -c configs/rtdetr/rtdetr_r50vd_6x_coco.yml -r path/to/checkpoint --test-only
Test
python tools/infer.py

Tips: set remap_mscoco_category: False.

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Hybrid RT DETR: Hybrid encoder-decoder network for end-to-end object detection in UAV imagery

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