ID: openscience.crowdsource.video.experiments
Version: 2.6
Size: 0 Mb
Crowdsource Video Experiments Screen Preview
Crowdsource Video Experiments Details
This application lets you participate in collaborative experiments shared by users which may require camera. Current public scenarios include crowd-benchmarking and crowd-tuning Deep Neural Network frameworks such as Caffe, TensorFlow, etc. Public scenarios are implemented using open-source Collective Knowledge Framework: * http://cKnowledge.org * https://github.com/ctuning/ck * https://github.com/dividiti/ck-caffe * https://github.com/ctuning/ck-crowd-scenario Crowd-results are continuously aggregated and processed via open CK repository (select "crowd-benchmarking DNN libs" scenario): * http://cKnowledge.org/repo Open sources of this application are also available for collaborative development (BSD license): * https://github.com/dividiti/crowdsource-video-experiments-on-android If you have questions or comments, do not hesitate to get in touch with us via this public mailing list: * https://groups.google.com/forum/#!forum/collective-knowledge You may read more about our long-term vision in the following publications: * http://arxiv.org/abs/1506.06256 * http://dx.doi.org/10.1145/2909437.2909449 * http://bit.ly/ck-date16 * http://hal.inria.fr/hal-01054763 * http://arxiv.org/abs/1406.4020 * https://hal.inria.fr/inria-00436029 We have been struggling with a lack of computational resources and diverse workloads/data sets/hardware for our own research to make faster, smaller, more power efficient reliable self-tuning software and hardware for more than a decade! Indeed, computer systems are becoming very inefficient - it is nowadays not uncommon to obtain 10x speedups, 2x size reduction and 40% energy reduction for popular algorithms (DNN, BLAS, video processing) on latest hardware using so-called autotuning of various algorithm parameters and compiler optimizations. However, this process is extremely time consuming due to very large design and optimization spaces. Eventually, we developed this open source Collective Knowledge technology (CK) to let the community share workloads, data sets, tools and experimental workflows in an open CK format via GitHub or BitBucket, crowdsource experiments (such as multi-objective benchmarking and multi-dimensional autotuning) across diverse devices provided by volunteers, classify solutions on the fly (active learning), apply predictive analytics, exchange knowledge, and reproduce results. Your participation supports open science and reproducible research initiatives such as Artifact Evaluation at leading conferences (sharing experimental workflows with all related artifacts and results along with publications in a reproducible and reusable way to be validated by the community): * http://cTuning.org/ae * http://adapt-workshop.org * http://ctuning.org/reproducibility-wiki This community-driven initiative is coordinated by: * http://cTuning.org (non-profit research organization) * http://dividiti.com Thank you very much for participating in experiment crowdsourcing and enabling open science!What's new in Crowdsource Video Experiments 2.6
V2.6 powered by Collective Knowledge has: * completely redesigned GUI * support for customizable DNN crowd-benchmarking for Android 5+ * support for BVLC GoogleNet, BVLC CaffeNet and AlexNet, DeepScale Squeezenet 1.0 and 1.1 * result sharing via http://cKnowledge.org/repo (select scenario "crowd-benchmark DNN libraries using mobile devices") * misprediction sharing (images and correct labels) NOTE: Don't forget to get latest crowd-benchmarking scenarios via Info -> Update ScenariosDownload Crowdsource Video Experiments 2.6 APK
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