Comments (4)
Thank you for your problem. I'll update the result after finishing the README.md part.
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Hi, Thank you so much for your open source code which is really rare in MOT community. However I ran into the same issue as @SpyderXu , I used the test_mot17.py to produce results for MOT17 trainset and calculate the MOTA score (tools from https://github.com/cheind/py-motmetrics), with a standard threshold IOU 0.5 (same as the MOTchanllenge). I used the weight that you shared : sst300_0712_83000.pth and I have got the following results:
MOT17-11-DPM
MOTP MOTA FP FN ID_SW num_objects
final 0.219081 43.07% 794 4492 86 9436
MOT17-02-DPM
MOTP MOTA FP FN ID_SW num_objects
final 0.251454 14.32% 697 15052 171 18581
MOT17-11-FRCNN
MOTP MOTA FP FN ID_SW num_objects
final 0.118502 56.44% 303 3740 67 9436
MOT17-02-SDP
MOTP MOTA FP FN ID_SW num_objects
final 0.200413 32.22% 2656 9621 318 18581
MOT17-09-SDP
MOTP MOTA FP FN ID_SW num_objects
final 0.162181 60.79% 145 1868 75 5325
MOT17-10-SDP
MOTP MOTA FP FN ID_SW num_objects
final 0.227044 59.12% 1229 3724 296 12839
MOT17-13-DPM
MOTP MOTA FP FN ID_SW num_objects
final 0.320541 7.62% 602 10069 84 11642
MOT17-05-DPM
MOTP MOTA FP FN ID_SW num_objects
final 0.26054 25.63% 422 4608 114 6917
MOT17-04-SDP
MOTP MOTA FP FN ID_SW num_objects
final 0.151199 73.87% 919 11362 145 47557
MOT17-05-SDP
MOTP MOTA FP FN ID_SW num_objects
final 0.190729 53.78% 413 2567 217 6917
MOT17-10-FRCNN
MOTP MOTA FP FN ID_SW num_objects
final 0.197432 41.55% 2012 5169 323 12839
MOT17-04-FRCNN
MOTP MOTA FP FN ID_SW num_objects
final 0.098745 51.56% 1891 21068 79 47557
MOT17-10-DPM
MOTP MOTA FP FN ID_SW num_objects
final 0.263786 27.18% 815 8437 97 12839
MOT17-13-FRCNN
MOTP MOTA FP FN ID_SW num_objects
final 0.270735 29.38% 2295 5567 359 11642
MOT17-09-DPM
MOTP MOTA FP FN ID_SW num_objects
final 0.267049 37.43% 865 2329 138 5325
MOT17-02-FRCNN
MOTP MOTA FP FN ID_SW num_objects
final 0.125864 26.27% 1593 12001 106 18581
MOT17-13-SDP
MOTP MOTA FP FN ID_SW num_objects
final 0.281644 31.45% 1837 5842 302 11642
MOT17-04-DPM
MOTP MOTA FP FN ID_SW num_objects
final 0.217009 32.21% 3586 28322 330 47557
MOT17-05-FRCNN
MOTP MOTA FP FN ID_SW num_objects
final 0.20983 47.71% 222 3294 101 6917
MOT17-11-SDP
MOTP MOTA FP FN ID_SW num_objects
final 0.165088 66.19% 561 2511 118 9436
MOT17-09-FRCNN
MOTP MOTA FP FN ID_SW num_objects
final 0.124762 55.44% 30 2310 33 5325
avg mota: 43.187 %
total fn: 163953.0
total fp: 23887.0
total idsw: 3559.0
total_num_objects: 336891.0
It there a way to improve the results, please.
Thank you a again for your contribution.
from sst.
Did you try to submit your results for MOT challenge. If so, could you indicate please the name of your method from the ranking board of MOT17 ?
from sst.
@yihongXU hi,I find the paper's results for MOT17 is in the ranking board where the detector is private.And can you repetition the paper's results?Because I don't know which detection the author used,because Faster R-CNN and DPM which mentioned in the paper can't reach the paper's scores
from sst.
Related Issues (20)
- Issues on Track age
- All_similarity and all_iou
- ToPercentCoords
- Input questions about the Network Diagram
- Question about fps HOT 1
- Issues with training HOT 1
- I'm trying to understand the code
- UserWarning HOT 3
- Base network selection HOT 3
- Apply for License HOT 2
- The final affinity matrix HOT 1
- Incorrect ID assignments in the project
- Why the accuracy and speed of retrained model is not as good as the sst300_0712_83000.pth which is given by author?
- About FPS HOT 3
- Two important parameters
- Why DAN get high IDSW score?
- Get Bad results when retraining the DAN on MOT17?
- There is always "None processing" while training on MOT15 dataset HOT 1
- 运行到一半出错
- Understanding the Assembly Loss in the loss function
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