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Object Tracking Inference

Box-level Tracking

For box-level tracking tasks like SOT and MOT, ${exp_name} should NOT contain mask. Specifically, legal ${exp_name} for box-level tracking include unicorn_track_large, unicorn_track_large_mot_challenge, unicorn_track_tiny, unicorn_track_tiny_rt, unicorn_track_r50. Their corresponding weights are exactly the same as those which end with mask. For exmple, if you want to run experiments about unicorn_track_tiny, you should first copy Unicorn_outputs/unicorn_track_tiny_mask to Unicorn_outputs/unicorn_track_tiny.

SOT

  • LaSOT
python3 tools/test.py unicorn_sot ${exp_name} --dataset lasot --threads 32
python3 tools/analysis_results.py --name ${exp_name}
  • TrackingNet
python3 tools/test.py unicorn_sot ${exp_name} --dataset trackingnet --threads 32
python3 external/lib/test/utils/transform_trackingnet.py --tracker_name unicorn_sot --cfg_name ${exp_name}

MOT

  • BDD100K
cd external/qdtrack
# track
bash tools/dist_test_omni.sh configs/bdd100k/unicorn.py ../../Unicorn_outputs/${exp_name}/latest_ckpt.pth 8 ${exp_name} --eval track
# bbox
python3 tools/eval.py configs/bdd100k/unicorn.py result_omni.pkl --eval bbox
  • MOT Challenge 17
python3 tools/track.py -f exps/default/${exp_name} -c <ckpt path> -b 1 -d 1 # using the association strategy in ByteTrack
python3 tools/track_omni.py -f exps/default/${exp_name} -c <ckpt path> -b 1 -d 1 # using the association strategy in QDTrack
python3 tools/interpolation.py # need to change some paths

Mask-level Tracking

For Mask-level tracking tasks like VOS and MOTS, ${exp_name} should contain mask. Specifically, legal ${exp_name} for mask-level tracking include unicorn_track_large_mask, unicorn_track_large_mot_challenge_mask, unicorn_track_tiny_mask, unicorn_track_tiny_rt_mask, unicorn_track_r50_mask.

VOS

  • DAVIS-2016
python3 tools/test.py unicorn_vos ${exp_name} --dataset dv2016_val --threads 20
cd external/PyDavis16EvalToolbox
python3 eval.py --name_list_path ../../datasets/DAVIS/ImageSets/2016/val.txt --mask_root ../../datasets/DAVIS/Annotations/480p --pred_path ../../test/segmentation_results/unicorn_vos/${exp_name}/ --save_path ../../result.pkl
  • DAVIS-2017
python3 tools/test.py unicorn_vos ${exp_name} --dataset dv2017_val --threads 30
cd external/davis2017-evaluation
python3 evaluation_method.py --task semi-supervised --results_path ../../test/segmentation_results/unicorn_vos/${exp_name} --davis_path ../../datasets/DAVIS

MOTS

  • MOTSChallenge
python3 tools/track_omni.py -f <exp file path> -c <ckpt path> -b 1 -d 1 --mots --mask_thres 0.3
# for train split
cp Unicorn_outputs/${exp_name}/track_results/* ../MOTChallengeEvalKit/res/MOTSres
cd ../MOTChallengeEvalKit
python MOTS/evalMOTS.py
  • BDD100K MOTS
cd external/qdtrack
# track
bash tools/dist_test_omni.sh configs/bdd100k_mots/segtrack-frcnn_r50_fpn_12e_bdd10k_fixed_pcan.py ../../Unicorn_outputs/${exp_name}/latest_ckpt.pth 8 ${exp_name} --eval segm --mots
# convert to BDD100K format (bitmask)
python3 tools/to_bdd100k.py configs/bdd100k_mots/segtrack-frcnn_r50_fpn_12e_bdd10k_fixed_pcan.py --res result_omni.pkl --task seg_track --bdd-dir . --nproc 32
# evaluate
bash eval_bdd_submit.sh