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SiamFC-PyTorch

This is the PyTorch (0.40) implementation of SiamFC tracker [1], which was originally implemented in Matlab using MatConvNet [2]. In our implementation, we obtain better perforamnce than the original one.

Goal

  • A more compact implementation of SiamFC [1]
  • Reproduce the results of SiamFC [1], including data generation, training and tracking

Requirements

  • Python 2.7 (I use Anaconda 2.* here. If you use Python3, you may get the very different results!)
  • Python-opencv
  • PyTorch 0.40
  • other common packages such as numpy, etc

Data curation

  • 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

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/
    • each epoch (50 in total) may take 7-8 minuts (Nvidia 1080 GPU), and you can use parallelling utilities in PyTorch for speeding up
    • I tried to use fixed random seeds to get the same results, but it doesn't work ):, so results for each training may be slightly different (still better than the original)
    • only color images are used for training, and better performance is expected if using color+gray as in original paper

Test (Tracking)

  • 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/)
  • Change other parameters as your willing :)
  • Now, let's run $python run_Train_SiamFC.py
  • some notes in tracking
    • two evaluation types are provided: single video demo and evaluation on the whole (OTB-100) benchmark
    • you can also change whihc net for evaluation in run_Train_SiamFC.py

Results

I tested the trained model on OTB-100 using a Nvidia 1080 GPU. The results and comparisons to the original implementation are shown in the below image. The running speed of our implementation is 82 fps. Note that, both models are trained from stratch.

image

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.

Contact

Any question are welcomed to hengfan@temple.edu.