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GETTING_STARTED.md

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Getting Started

This document provides tutorials to train and evaluate CenterTrack. Before getting started, make sure you have finished installation and dataset setup.

Benchmark evaluation

First, download the models you want to evaluate from our model zoo and put them in CenterTrack_ROOT/models/.

MOT17

To test the tracking performance on MOT17 with our pretrained model, run

 python test.py tracking --exp_id mot17_half --dataset mot --dataset_version 17halfval --pre_hm --ltrb_amodal --track_thresh 0.4 --pre_thresh 0.5 --load_model ../models/mot17_half.pth

This will give a MOTA of 66.1 if set up correctly. --pre_hm is to enable the input heatmap. --ltrb_amodal is to use the left, top, right, bottom bounding box representation to enable detecting out-of-image bounding box (We observed this is important for MOT datasets). And --track_thresh and --pre_thresh are the score threshold for predicting a bounding box ($\theta$ in the paper) and feeding the heatmap to the next frame ($\tau$ in the paper), respectively.

To test with public detection, run

 python test.py tracking --exp_id mot17_half_public --dataset mot --dataset_version 17halfval --pre_hm --ltrb_amodal --track_thresh 0.4 --pre_thresh 0.5 --load_model ../models/mot17_half.pth --public_det --load_results ../data/mot17/results/val_half_det.json

The expected MOTA is 63.1.

To test on the test set, run

 python test.py tracking --exp_id mot17_fulltrain_public --dataset mot --dataset_version 17test --pre_hm --ltrb_amodal --track_thresh 0.4 --pre_thresh 0.5 --load_model ../models/mot17_fulltrain_sc.pth --public_det --load_results ../data/mot17/results/test_det.json

The Test set evaluation requires submitting to the official test server. We discourage the users to submit our predictions to the test set to prevent test set abuse. You can append --debug 2 to above commends to visualize the predictions.

See the experiments folder for testing in other settings.

KITTI Tracking

Run:

python test.py tracking --exp_id kitti_half --dataset kitti_tracking --dataset_version val_half --pre_hm --track_thresh 0.4 --load_model ../models/kitti_half.pth

The expected MOTA is 88.7.

nuScenes

Run:

python test.py tracking,ddd --exp_id nuScenes_3Dtracking --load_model ../models/nuScenes_3Dtracking.pth --dataset nuscenes --track_thresh 0.1 --pre_hm

The expected AMOTA is 6.8.

Training

We have packed all the training scripts in the experiments folder. The experiment names correspond to the model name in the model zoo. The number of GPUs for each experiment can be found in the scripts and the model zoo. If the training is terminated before finishing, you can use the same command with --resume to resume training. It will found the latest model with the same exp_id. Some experiments rely on pretraining on another model. In this case, download the pretrained model from our model zoo or train that model first.