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siamfc-pytorch's Introduction

SiamFC-PyTorch

This is the PyTorch 0.4 implementation of SiamFC tracker [1], which was originally implemented using MatConvNet [2].
Support multi-gpu training

Goal

A more compact implementation of SiamFC [1].
Reproduce the results of SiamFC [1].

Requirements

Python 3.6
Python-opencv
PyTorch 0.4.0

Data curation

Download ILSVRC15, and unzip it (let's assume that $ILSVRC2015_Root is the path to your ILSVRC2015)
run ./script/careate_dataset.sh, then you will get two json files imdb_video_train.json (~ 430MB) and imdb_video_val.json (~ 28MB) in current folder, which are used for training and validation.

Train

run ./script/train.sh
some notes for training:
- the options for training are in Config.py
- each epoch (50 in total) may take 6 minuts (Nvidia Titan Pascal, num_worker=8 in my case)

Tracking

Take a look at Config.py first, which contains all parameters for tracking
Change self.net_base_path to the path saving your trained models
Change self.net to indicate whcih model you want for evaluation, and I've uploaded a trained model SiamFC_45_model.pth in this rep (located in $SiamFC-PyTorch/Train/model/)
The default parameters I use for my results is as listed in Config.py.
run ./script/demo.sh

References

[1] L. Bertinetto, J. Valmadre, J. F. Henriques, A. Vedaldi, and P. H. Torr. Fully-convolutional siamese networks for object tracking. In ECCV Workshop, 2016.

[2] A. Vedaldi and K. Lenc. Matconvnet – convolutional neural networks for matlab. In ACM MM, 2015.

[3]https://github.com/StrangerZhang/SiamFC-PyTorch

[4]https://github.com/HengLan/SiamFC-PyTorch

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