Multi-Adapter RGBT Tracking implementation on Pytorch
this code is update version based on submitted for VOT RGBT race code simplified version. So there are some differences from MANET's paper.
CPU: Intel(R) Core(TM) i7-7700K CPU @ 3.75GHz GPU: NVIDIA GTX1080 Ubuntu 16.04
- pytorch == 0.3.1
- by yourself need install some library functions
Pretrained model for MANet
In our tracker, we use an VGG-M Net variant as our backbone, which is end-to-end trained for visual tracking.
The train on gtot model file in models folder,name called MANet-2IC.pth ,you can use this tracking rgbt234
Then,You need to modify the path in the tracking/options.py file depending on where the file is placed. It is best to use an absolute path. you can change code version of CPU/GPU in this flie
you can use RGBT dataset as train data , in pretrain floder you need first genrate sequence list .pkl file use prepro_data.py , sencod change your data path , fainlly excute train.py
in the tracking/run_tracker.py file you need change dataset path and save result file dirpath in the tracking/options.py file you need set model file path ,and set learning rate depend on annotation. in tracking and train stage you need update modules/MANet3x1x1_IC.py file depend on annotation.