PyTorch implementation of "Learning to Track at 100 FPS with Deep Regression Networks"
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This repository contains the reimplementation of GOTURN in PyTorch. If you are interested in the following, you should consider using this repository
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Understand different moving parts of the GOTURN algorithm, independently through code.
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Plug and play with different parts of the pipeline such as data, network, optimizers, cost functions.
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Built with following frameworks:
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Similar to original implementation by the author, you will be able to train the model in a day
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Bike | Bear |
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# Clone the repository
$ git clone https://github.com/nrupatunga/goturn-pytorch
# install all the required repositories
$ cd goturn-pytorch/src
$ pip install -r requirements.txt
# Add current directory to environment
$ source settings.sh
cd goturn-pytorch/src/scripts
$ ./download_data.sh /path/to/data/directory
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If you want to use your own custom dataset to train, you might want to understand the how current dataloader works. For that you might need a smaller dataset to debug, which you can find it here. This contains few samples of ImageNet and Alov dataset.
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Once you understand the dataloaders, please refer here for more information on where to modify training script.
$ cd goturn-pytorch/src/scripts
# Modify the following variables in the script
# Path to imagenet
IMAGENET_PATH='/media/nthere/datasets/ISLVRC2014_Det/'
# Path to alov dataset
ALOV_PATH='/media/nthere/datasets/ALOV/'
# save path for models
SAVE_PATH='./caffenet/'
# open another terminal and run
$ visdom
# You can visualize the train/val images, loss curves,
# simply open https://localhost:8097 in your browser to visualize
# training
$ bash train.sh
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In order to test the model, you can use the model in this link or you can use your trained model
$ mkdir goturn-pytorch/models
# Copy the extracted caffenet folder into models folder, if you are
# using the trained model
$ cd goturn-pytorch/src/scripts
$ bash demo_folder.sh
# once you run, select the bounding box on the frame using mouse,
# once you select the bounding box press 's' to start tracking,
# if the model lose tracking, please press 'p' to pause and mark the
# bounding box again as before and press 's' again to continue the
# tracking
# To test on a new video, you need to extract the frames from the video
# using ffmpeg or any other tool and modify folder path in
# demo_folder.sh accordingly
- Model trained is Caffe is better in getting the objectness, this way it handles some of the cases quite well than the PyTorch model I have trained. I tried to debug this issue. You can refer to the discussion here. If someone solves this issue, please open a PR