DocumentAIKit/configs/mot/deepsort/detector

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Detector for DeepSORT

Introduction

DeepSORT(Deep Cosine Metric Learning SORT) is composed of a detector and a ReID model in series. The configs of several common detectors are provided here as a reference. Note that different training dataset, backbone, input size, training epochs and NMS threshold will lead to differences in model accuracy and performance. Please adapt according to your needs.

Model Zoo

Results on MOT17-half dataset

Backbone	Model	input size	lr schedule	FPS	Box AP	download	config
DarkNet-53	YOLOv3	608X608	40e	----	42.7	download	config
ResNet50-vd	PPYOLOv2	640x640	365e	----	46.8	download	config
CSPResNet	PPYOLOe	640x640	36e	----	52.9	download	config

Notes:

• The above models are trained with MOT17-half train set, it can be downloaded from this link.
• MOT17-half train set is a dataset composed of pictures and labels of the first half frame of each video in MOT17 Train dataset (7 sequences in total). MOT17-half val set is used for evaluation, which is composed of the second half frame of each video. They can be downloaded from this link. Download and unzip it in the dataset/mot/MOT17/images/folder.
• YOLOv3 is trained with the same pedestrian dataset as configs/pphuman/pedestrian_yolov3/pedestrian_yolov3_darknet.yml, which is not open yet.
• For pedestrian tracking, please use pedestrian detector combined with pedestrian ReID model. For vehicle tracking, please use vehicle detector combined with vehicle ReID model.
• High quality detected boxes are required for DeepSORT tracking, so the post-processing settings such as NMS threshold of these models are different from those in pure detection tasks.

Quick Start

Start the training and evaluation with the following command

job_name=ppyoloe_crn_l_36e_640x640_mot17half
config=configs/mot/deepsort/detector/${job_name}.yml
log_dir=log_dir/${job_name}
# 1. training
python -m paddle.distributed.launch --log_dir=${log_dir} --gpus 0,1,2,3,4,5,6,7 tools/train.py -c ${config} --eval --amp --fleet
# 2. evaluation
CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c ${config} -o weights=https://paddledet.bj.bcebos.com/models/mot/deepsort/${job_name}.pdparams