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Welcome to the Yolov5_DeepSort_Pytorch wiki!
- Evaluation
- Download MOT16 data from:
https://motchallenge.net/data/MOT16/
under downloads. The structure of the downloaded data:
MOT16
├── train
│ ├── MOT16-02
│ │ ├── det
│ │ ├── gt
│ │ ├── img1
│ │ └── seqinfo.ini
│ ├── MOT16-04
│ ├── MOT16-05
│ ├── MOT16-09
│ ├── MOT16-10
│ ├── MOT16-11
│ └── MOT16-13
│
└── test
├── MOT16-02
├── MOT16-04
├── MOT16-05
├── MOT16-09
├── MOT16-10
├── MOT16-11
└── MOT16-13
- Transform all images under all img1 under each MOT folder into videos by:
ffmpeg -framerate 25 -i %06d.jpg -c:v libx264 -profile:v high -crf 20 -pix_fmt yuv420p MOT16-13.mp4
- Then run:
python3 track.py --source /home/mikel.brostrom/Documents/TrackEval/data/MOT16/train/MOT16-XX/MOT16-XX.mp4 –save-txt
on all the videos and save the results under this cloned repo:
https://github.com/JonathonLuiten/TrackEval
by creating a folder to store the results for the different sequences generated by the yolov5_deep_sort algorithm like this:
trackeval
├── data
├── trackers
├── mot_challange
├── MOT16-train
├── ch_yolov5m_deep_sort
├── MOT16-02.txt
├── MOT16-04.txt
├── MOT16-05.txt
├── MOT16-09.txt
├── MOT16-10.txt
├── MOT16-11.txt
├── MOT16-13.txt
Then you can run the combined evaluation (on all sequences) by:
cd trackeval
python scripts/run_mot_challenge.py --BENCHMARK MOT16 --TRACKERS_TO_EVAL ch_yolov5m_deep_sort --METRICS CLEAR Identity --USE_PARALLEL False --NUM_PARALLEL_CORES 4
if you want to evaluate a single sequence you can use the --SEQ_INFO flag:
python scripts/run_mot_challenge.py --SEQ_INFO MOT16-04 --BENCHMARK MOT16 --TRACKERS_TO_EVAL humancrowd_yolov5_deep_sort --METRICS CLEAR Identity --USE_PARALLEL False --NUM_PARALLEL_CORES 1
NOTICE! That this evaluation is on the train dataset