This is an PyTorch implementation of SiamRPN++ (CVPR2019), using HRnet as the backbone. We train the model on ILSVRC2015_VID dataset with multi-GPUs, and use LMDB data format to speed up the data loading.
We use HRnet as the backbone and mutiple FPNs to fuse feature maps with different solutions at different depths of the model. Fused features are then fed to SiamRPN for tracking.
Ubuntu 14.04
Python 3.7
PyTorch 1.01
# 1. Download training data. In this project, we provide the downloading and preprocessing scripts for ILSVRC2015_VID dataset. Please download ILSVRC2015_VID dataset (86GB). The cripts for other tracking datasets are coming soon.
cd data
wget -c http://bvisionweb1.cs.unc.edu/ilsvrc2015/ILSVRC2015_VID.tar.gz
tar -xvf ILSVRC2015_VID.tar.gz
rm ILSVRC2015_VID.tar.gz
cd ..
# 2. Preprocess data.
chmod u+x ./preprocessing/create_dataset.sh
./preprocessing/create_dataset.sh
# 3. Pack the preprocessed data into LMDB format to accelerate data loading.
chmod u+x ./preprocessing/create_lmdb.sh
./preprocessing/create_lmdb.sh
# 4. Start the training.
chmod u+x ./train.sh
./train_hrnet.sh