Here, we provide a number of tracker models trained using PyTracking. We also report the results of the models on standard tracking datasets.
| Model | VOT18 EAO (%) |
OTB100 AUC (%) |
NFS AUC (%) |
UAV123 AUC (%) |
LaSOT AUC (%) |
TrackingNet AUC (%) |
GOT-10k AO (%) |
Links |
|---|---|---|---|---|---|---|---|---|
| ATOM | 0.401 | 66.3 | 58.4 | 64.2 | 51.5 | 70.3 | 55.6 | model | results |
| DiMP-18 | 0.402 | 66.0 | 61.0 | 64.3 | 53.5 | 72.3 | 57.9 | model | results |
| DiMP-50 | 0.440 | 68.4 | 61.9 | 65.3 | 56.9 | 74.0 | 61.1 | model | results |
Note: The raw results are in the format [top_left_x, top_left_y, width, height]. Due to the stochastic nature of the trackers, the results reported here are an average over multiple runs. For OTB-100, NFS, UAV123, and LaSOT, the results were averaged over 5 runs. For VOT2018, 15 runs were used as per the VOT protocol. As TrackingNet results are obtained using the online evaluation server, only a single run was used for TrackingNet. For GOT-10k, 3 runs are used as per protocol.
The success plots for our trained models on the standard tracking datasets are shown below.
