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CORAL

This repository hosts USC ISI / CORAL teams systems for LWLL evaluations.

Documentation is available at: https://usc-isi.lollllz.com/coral/

This folder is for CORAL TEAM Object Detection System. Currently, this is the Dry Run version.

Usage:

For building the docker for evaluation:

docker build -t detection_pipeline .

For running the Object Detection Pipeline:

docker run --rm -ti --gpus '"device=0"' --env-file env.list --ipc=host -v /lwll/evaluation:/lwll/evaluation detection_pipeline

Assuming LWLL* environment variables are correctly set. If the above command is used, then it will be read from env.list file.

Assuming all datasets are stored at /lwll/evaluation/.

The system uses only one GPU. Please make sure to give access to only one GPU using --gpus '"device=0"'.

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PyTorch code for CVPR 2022 paper Unbiased Teacher v2 Semi-supervised Object Detection for Anchor-free and Anchor-based Detectors

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  • Python 98.3%
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