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The training framework in based on Pytorch and PytorchLightning. And the detection models are based on mmdet. Please create a python environment and install specific versions of these packages suitable for your hardware.
Download the BDD100k tracking dataset to data/bdd100k and the SHIFT tracking dataset to data/shift_dataset.
Running CL Experiments
Run BDD experiments:
bash run_scripts/run_bdd.sh run_scripts/bdd_cfgs/most_to_least.toml --config configs/experiments/bdd/PL_GT_contrast/cooler_ct_loss.toml --exp_name most_to_least ## most to least setting
bash run_scripts/run_bdd.sh run_scripts/bdd_cfgs/general_to_specific.toml --config configs/experiments/bdd/PL_GT_contrast/cooler_ct_loss.toml --exp_name general_to_specific ## general to specific setting
bash run_scripts/run_bdd.sh run_scripts/bdd_cfgs/vehicle_bike_person.toml --config configs/experiments/bdd/PL_GT_contrast/cooler_ct_loss.toml --exp_name vehicle_bike_person ## vehicle to bike to human setting
Run SHIFT experiments:
bash run_scripts/run_shift.sh run_scripts/shift_cfgs/most_to_least.toml --config configs/experiments/clear_daytime_shift/PL_GT_contrast/cooler_ct_loss.toml --exp_name most_to_least ## most to least setting
bash run_scripts/run_shift.sh run_scripts/shift_cfgs/general_to_specific.toml --config configs/experiments/clear_daytime_shift/PL_GT_contrast/cooler_ct_loss.toml --exp_name general_to_sepcific ## general to specific setting
bash run_scripts/run_shift.sh run_scripts/shift_cfgs/vehicle_bike_person.toml --config configs/experiments/clear_daytime_shift/PL_GT_contrast/cooler_ct_loss.toml --exp_name vehicle_bike_person ## vehicle to bike to human setting
Usage
Training
python3 -m ilmot.trainer train --config <config_path><maybe other arguments>
Testing
python3 -m ilmot.trainer test --config <config_path><maybe other arguments>
Prediction
python3 -m ilmot.trainer predict --config <config_path><maybe other arguments>
Visualization
python3 -m ilmot.trainer visualize --config <config_path><maybe other arguments>
About
[GCPR'23 Oral] COOLer: Class-Incremental Learning for Appearance-Based Multiple Object Tracking
