PySOT Model Zoo
June 13, 2019 · View on GitHub
Introduction
This file documents a large collection of baselines trained with pysot. All configurations for these baselines are located in the experiments directory. The tables below provide results about inference. Links to the trained models as well as their output are provided.
Visual Tracking Baselines
Short-term Tracking
| Model （arch+backbone+xcorr） | VOT16 (EAO/A/R) | VOT18 (EAO/A/R) | VOT19 (EAO/A/R) | OTB2015 (AUC/Prec.) | VOT18-LT (F1) | Speed (fps) | url |
|---|---|---|---|---|---|---|---|
| siamrpn_alex_dwxcorr | 0.393/0.618/0.238 | 0.352/0.576/0.290 | 0.260/0.573/0.547 | - | - | 180 | link |
| siamrpn_alex_dwxcorr_otb | - | - | - | 0.666/0.876 | - | 180 | link |
| siamrpn_r50_l234_dwxcorr | 0.464/0.642/0.196 | 0.415/0.601/0.234 | 0.287/0.595/0.467 | - | - | 35 | link |
| siamrpn_r50_l234_dwxcorr_otb | - | - | - | 0.696/0.914 | - | 35 | link |
| siamrpn_mobilev2_l234_dwxcorr | 0.455/0.624/0.214 | 0.410/0.586/0.229 | 0.292/0.580/0.446 | - | - | 75 | link |
| siammask_r50_l3 | 0.455/0.634/0.219 | 0.423/0.615/0.248 | 0.283/0.597/0.461 | - | - | 56 | link |
| siamrpn_r50_l234_dwxcorr_lt | - | - | - | - | 0.629 | 20 | link |
The models can also be downloaded from Baidu Yun Extraction Code: j9yb
Note:
- speed tested on GTX-1080Ti
alexdenotes AlexNet,r50_lxyzdenotes the outputs of stage x, y, and z in ResNet-50, andmobilev2denotes MobileNetV2.dwxcorrdenotes Depth-wise Cross Correlation. See more in SiamRPN++ Section 3.4.- The suffixes
otbandltare designed for the OTB and VOT long-term tracking challenge, the default (without suffix) is designed for VOT short-term tracking challenge. - All above models are trained on VID,YoutubeBB,COCO,ImageNetDet which are the same as DaSiamRPN.
Video Object Segmentation Baselines
License
All models available for download through this document are licensed under the Creative Commons Attribution-ShareAlike 3.0 license.