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Pytorch version is released:

Requirement

• Python 2.7 (The performance is inferior using Python 3)
• Python-opencv
• PyTorch 0.40
• other common packages such as numpy, etc

Data Preparison

• Download ILSVRC15, and unzip it (let's assume that $ILSVRC2015_Root is the path to your ILSVRC2015)

  • Move $ILSVRC2015_Root/Data/VID/val into $ILSVRC2015_Root/Data/VID/train/, so we have five sub-folders in $ILSVRC2015_Root/Data/VID/train/
  • It is a good idea to change the names of five sub-folders in $ILSVRC2015_Root/Data/VID/train/ to a, b, c, d, and e Move $ILSVRC2015_Root/Annotations/VID/val into $ILSVRC2015_Root/Annotations/VID/train/, so we have five sub-folders in $ILSVRC2015_Root/Annotations/VID/train/
  • Change the names of five sub-folders in $ILSVRC2015_Root/Annotations/VID/train/ to a, b, c, d and e, respectively

• Generate image crops

  • cd $SiamFC-PyTorch/ILSVRC15-curation/ (Assume you've downloaded the rep and its path is $SiamFC-PyTorch)
  • change vid_curated_path in gen_image_crops_VID.py to save your crops
  • run $python gen_image_crops_VID.py (I run it in PyCharm), then you can check the cropped images in your saving path (i.e., vid_curated_path)

• Generate imdb for training and validation

  • cd $SiamFC-PyTorch/ILSVRC15-curation/
  • change vid_root_path and vid_curated_path to your custom path in gen_imdb_VID.py
  • run $python gen_imdb_VID.py, 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

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Train

• cd $SiamFC-PyTorch/Train/
• Change data_dir, train_imdb and val_imdb to your custom cropping path, training and validation json files
• run $python run_Train_SiamFC.py
• some notes in training
• the parameters for training are in Config.py
• by default, I use GPU in training, and you can check the details in the function train(data_dir, train_imdb, val_imdb, model_save_path="./model/", use_gpu=True)
• by default, the trained models will be saved to $SiamFC-PyTorch/Train/model/

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Test

• cd $SiamFC-PyTorch/Tracking/
• Firstly, you should take a look at Config.py, which contains all parameters for tracking
• Change self.net_base_path to the path saving your trained models
• Change self.seq_base_path to the path storing your test sequences (OTB format, otherwise you need to revise the function load_sequence() in Tracking_Utils.py
• Change self.net to indicate whcih model you want for evaluation (by default, use the last one), and I've uploaded a trained model SiamFC_50_model.pth in this rep (located in $SiamFC-PyTorch/Train/model/)

This work reused partial code from https://github.com/HengLan/SiamFC-PyTorch

citation

If you find the code useful, please cite

@inproceedings{lu2018deep,  
  title={Deep Regression Tracking with Shrinkage Loss},   
  author={Lu, Xiankai and Ma, Chao and Ni, Bingbing and Yang, Xiaokang and Reid, Ian and Yang, Ming-Hsuan},  
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},  
  pages={353--369},    
  year={2018}
}