silverbottlep / meta_trackers

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0. Prerequisites

PyTorch >= v0.2.0

0. Dataset download

Download dataset(OTB, VOT) and prepared the link to the dataset in $(meta_trackers_root)/dataset/ directory.


0. Prepare dataset meta files

I already prepared all necessary meta-files for ILSVRC VID dataset, OTB, VOT dataset. Either you use them or you could generate via scripts in $(meta_trackers_root)/dataset/ directory.

$(meta_trackers_root)/dataset/ilsvrc_train.json # meta file for loading ILSVRC VID dataset to meta-train.
$(meta_trackers_root)/dataset/vot-otb.pkl # meta file for loading VOT dataset to meta-train(for OTB experiments)
$(meta_trackers_root)/dataset/otb-vot.pkl # meta file for loading OTB dataset to meta-train(for VOT experiments)

Also need to download imagenet pretrained models(our base feature extractors) into $(meta_trackers_root)/meta_crest(and meta_sdnet)/models/. (We used the same networks that original trackers used. For meta_sdnet - imagenet-vgg-m.mat, and for meta_crest - imagenet-vgg-verydeep-16.mat)

1. Meta-Training

You can skip this step and download pretrain models, and use them to test the trackers. If you want to meta-train MetaCREST trackers,

$(meta_trackers_root)/meta_crest/meta_pretrain$> python -e OTB # for OTB experiments, for VOT use -e VOT

To meta-train MetaSDNet trackers,

$(meta_trackers_root)/meta_sdnet/meta_pretrain$> python -e OTB # for OTB experiments, for VOT use -e VOT

2. Downloading pretrained models

We provide pretrained models for both meta trackers for your convenience. You can download it from following links and locate them in models directory.

$(meta_trackers_root)/meta_sdnet/models/meta_init_vot_ilsvrc.pth (~35M)

$(meta_trackers_root)/meta_sdnet/models/meta_init_otb_ilsvrc.pth (~35M)

$(meta_trackers_root)/meta_crest/models/meta_init_vot_ilsvrc.pth (~59K)

$(meta_trackers_root)/meta_crest/models/meta_init_otb_ilsvrc.pth (~59K)

3. Testing MetaTrackers

$(meta_trackers_root)/meta_crest/meta_tracking$>python # meta_crest tracker for OTB experiments
$(meta_trackers_root)/meta_sdnet/meta_tracking$>python # meta_sdnet tracker for OTB experiments

To run VOT2016 experiments, I provided following VOT integration files. You can use them and run it via VOT2016 toolkit. Please refer to VOT homepage


4. Evaluations

If you used pre-trained models, you should be able to get same results(or small variation due to randomness in trackers) reported in the papers. If you meta-trained the model, you should also be able to get similar results.

$(meta_trackers_root)/meta_crest$> python 
$(meta_trackers_root)/meta_sdnet$> python

Similarly, please refer to VOT homepage for VOT evaluations. I also provided all raw results for both OTB and VOT experiments that used in the paper(meta_crest_result, meta_sdnet_result)


Many parts of this code are adopted from other related works(pytorch-maml, py-MDNet, CREST).



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