The implementation of simple face classification based on siamese network.

Add GOT10K toolkit and optimize the interface. 

We use the VID data set for training . 

The testing results are slightly lower than the paper(without hyperparameter adjustment). 

Add GOT10K toolkit and optimize the interface. 

We use YTB and VID  data sets for training. 

The testing results are slightly lower than the paper(without hyperparameter adjustment). 

Add PYSOT toolkit and optimize the interface. 

You can  debug, train and test easily.  

The results of testing are consistent with the paper.

Note that you shound have python3  environment.

Add PYSOT toolkit and optimize the interface. 

The model is sensitive to learning rate. 

Our results is higher than the original paper on VOT2018 dataset. EAO=0.403(Ours)  EAO=0.393(Paper)

The paper mainly analyzed the impact of padding on the tracking network. 

Support VScode single-step debugging.

Add test scripts for 4 drone datasets.

Change distributed multi-machine multi-GPU parallel to single-machine multi-GPU parallel.

Train SiamRPNpp AlexNet version using four datasets (training time is  3~4 days with 2 1080 GPUs ).

Support VScode single-step debugging.

Support testing and training.

Support VScode single-step debugging.

Add test scripts for 4 drone datasets.

Use  GOT10K data set to retrain the AlexNet version, the training time is 15~20 hours (2 1080 GPUs).

Support VScode single-step debugging.

Support VScode single-step debugging.

Support testing and training.◊

Support VScode single-step debugging.

Support testing and training.

[1] SiamFC

Bertinetto L, Valmadre J, Henriques J F, et al. Fully-convolutional siamese networks for object tracking.European conference on computer vision. Springer, Cham, 2016: 850-865.
   
[2] SiamRPN

Li B, Yan J, Wu W, et al. High performance visual tracking with siamese region proposal network.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 8971-8980.

[3] DaSiamRPN

Zhu Z, Wang Q, Li B, et al. Distractor-aware siamese networks for visual object tracking.Proceedings of the European Conference on Computer Vision (ECCV). 2018: 101-117.

[4] UpdateNet

Zhang L, Gonzalez-Garcia A, Weijer J, et al. Learning the Model Update for Siamese Trackers. Proceedings of the IEEE International Conference on Computer Vision. 2019: 4010-4019.
   
[5] SiamDW

Zhang Z, Peng H. Deeper and wider siamese networks for real-time visual tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 4591-4600.

[6] SiamRPNpp

Li B, Wu W, Wang Q, et al. SiamRPNpp: Evolution of siamese visual tracking with very deep networks.Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 4282-4291.

[7] SiamMask

Wang Q, Zhang L, Bertinetto L, et al. Fast online object tracking and segmentation: A unifying approach. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 1328-1338.
   
[8] SiamFCpp

Xu Y, Wang Z, Li Z, et al. SiamFCpp: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines. AAAI, 2020.

[9] SiamCAR
Guo D ,  Wang J ,  Cui Y , et al. SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2020.

[10] SiamBAN
Chen Z, Zhong B, Li G, et al. Siamese box adaptive network for visual tracking[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 6668-6677.

[11] TrTr 
Zhao M, Okada K, Inaba M. TrTr: Visual Tracking with Transformer[J]. arXiv preprint arXiv:2105.03817, 2021.