[
    {
        "Variant name": "main",
        "Reviewer name": "Julie Digne <julie.digne@liris.cnrs.fr>",
        "Is master variant (boolean)": true,
        "Is variant deprecated (boolean)": false,
        "Title": "Scanner: efficient video analysis at scale",
        "DOI": "10.1145/3197517.3201394",
        "Year": 2018,
        "ACM Keywords": [
            "Image manipulation",
            "Graphics systems and interfaces"
        ],
        "Topic {Rendering, Animation and Simulation, Geometry, Images, Virtual Reality, Fabrication}": "Images",
        "Co-authors from academia (boolean)": true,
        "Co-authors from industry (boolean)": false,
        "ACM Open Access (boolean)": false,
        "PDF on the authors' webpage / institution (boolean)": true,
        "PDF URL": "http://graphics.stanford.edu/papers/scanner/poms18_scanner.pdf",
        "PDF on Arxiv or any openarchive initiatives (boolean)": true,
        "Arxiv/OAI page URL": "https://arxiv.org/abs/1805.07339",
        "Project URL": "http://scanner.run/",
        "Code available (boolean)": true,
        "If code not available, pseudo-code available (boolean)": false,
        "If pseudo-code, could the paper be trivially implemented? {0..4}": "",
        "Code URL": "https://github.com/scanner-research/scanner",
        "Code URL2": "",
        "MD5 sum (for archives)": "",
        "git/hg/svn commit hash or revision number": "645fb2388341bc90300e2645fe4f9d0374baa537",
        "MD5 sum (for archives) URL2": "",
        "git/hg/svn commit hash or revision number URL2": "",
        "Software Heritage permalink": "https://archive.softwareheritage.org/swh:1:dir:8cb1e6945fb08e2e77cf3d4ddaaaeef68708cab2;origin=https://github.com/scanner-research/scanner/",
        "Software type {Code, Binary, Partial Code}": "Code",
        "Code License (if any)": "Apache License 2.0",
        "Are the code authors explicit? (boolean)": true,
        "Build/Configure mechanism": "Makefile, CMakeLists, Not applicable (python, Matlab..)",
        "Dependencies": "tensorflow/caffe/opencv/eigen3/ffmpeg/boost/openpose/halide/storehouse/hwang/pybind/grpc/protobuf/libpqxx/cuda/cudnn",
        "Does the software require paywall/proprietary software/material (boolean)?": false,
        "Does the code need data (not examples) (boolean)": false,
        "Nature of the data (pretrained model, LUT...)": "",
        "License of the data": "",
        "Able to perform a replicability test (boolean)": true,
        "If not able to perform a test, was it due to missing hardware/software? (boolean)": false,
        "Documentation score {0=NA,1,2,3}": 1,
        "Dependencies score {0=NA, 1,2,3,4,5}": 5,
        "Build/configure score {0=NA, 1,2,3,4,5}": 0,
        "Fixing bugs score (if any) {0=NA, 1,2,3,4,5}": 0,
        "Replicate paper results score {0=NA, 1,2,3,4,5}": 4,
        "Adaptability score to other contexts {0=NA, 1,2,3,4,5}": 5,
        "Time spent for the test (code download to first successful run, [0,10], 10min slots, 100min max)": 5,
        "Operating system for the test": "Linux",
        "Build instructions/comments": "I used the docker setting, it worked well (cpu). Was not able to install from source because of a problem with hwang (installed by deps.sh although optional dependency). A jupyter notebook extremely detailed is provided and helps understand the code, so it would be very easy to adapt. I did not reproduce all the results in the paper (grayscale conversion worked well with docker).",
        "Misc. comments": "",
        "Software language": "C/C++, Python"
    }
]