SiamMask is an innovative algorithm for fast online object tracking and segmentation. In this blog, we’ll guide you through the essential steps needed to set up, train, and test the SiamMask model. Let’s get started!
Environment Setup
To run SiamMask smoothly, it’s critical to set up your environment correctly. This code has been tested on Ubuntu 16.04, with Python 3.6, Pytorch 0.4.1, and CUDA 9.2, using RTX 2080 GPUs.
- Clone the repository:
git clone https://github.com/foolwood/SiamMask.git
cd SiamMask
export SiamMask=$PWD
conda create -n siammask python=3.6
source activate siammask
pip install -r requirements.txt
bash make.sh
export PYTHONPATH=$PWD:$PYTHONPATH
Running a Demo
To check if everything is working fine, let’s run a demo:
- First, download the SiamMask model:
cd $SiamMask/experiments/siammask_sharp
wget http://www.robots.ox.ac.uk/~qwang/SiamMask_VOT.pth
wget http://www.robots.ox.ac.uk/~qwang/SiamMask_DAVIS.pth
cd $SiamMask/experiments/siammask_sharp
export PYTHONPATH=$PWD:$PYTHONPATH
python ....tools/demo.py --resume SiamMask_DAVIS.pth --config config_davis.json
Make sure to visualize the results that should appear in a sample GIF format.
Testing Models
You can test the SiamMask on various datasets using the following steps:
- Download test data:
cd $SiamMask/data
sudo apt-get install jq
bash get_test_data.sh
cd $SiamMask/experiments/siammask_sharp
wget http://www.robots.ox.ac.uk/~qwang/SiamMask_VOT.pth
wget http://www.robots.ox.ac.uk/~qwang/SiamMask_VOT_LD.pth
wget http://www.robots.ox.ac.uk/~qwang/SiamMask_DAVIS.pth
bash test_mask_refine.sh config_vot.json SiamMask_VOT.pth VOT2016 0
Training Models
Training SiamMask efficiently involves following these steps:
- Prepare your training data from sources like Youtube-VOS, COCO, and ImageNet datasets, and preprocess them according to the guidance in respective README files.
- Download the pre-trained model:
cd $SiamMask/experiments
wget http://www.robots.ox.ac.uk/~qwang/resnet.model
ls | grep siam | xargs -I cp resnet.model
cd $SiamMask/experiments/siammask_base
bash run.sh
Troubleshooting
If you encounter any issues, here are some troubleshooting tips:
- If you face out-of-memory errors during training, reduce the batch size in the run.sh file.
- Make sure all the dependencies are correctly installed, especially in your Python environment.
- If errors persist, consider checking your CUDA installation, as compatibility with certain libraries is critical.
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Conclusion
By following these steps, you’re well on your way to mastering the SiamMask model for effective object tracking and segmentation. With the right environment set up along with thorough testing and training, the possibilities are boundless!
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.