Open source Deep Learning Background removal service

Hacker News

Me and my colleague have been working on a small POC for Background removal. We used semantic segmentation trained on selfies, and documented the process of training and deploying a Keras model with Docker, Heroku and a Gitlab CI in 3 posts: https://greenscreen-ai.boorgle.com Server side implementation https://greenscreen-ai-client.boorgle.com Client side implementation using KerasJS Training post https://medium.com/@gidishperber/background-removal-with-dee... Deploying Keras model to Heroku with Gitlab CI https://medium.com/@burgalon/deploying-your-keras-model-3564... KerasJS client side post https://medium.com/@burgalon/deploying-your-keras-model-usin... Server side source code https://gitlab.com/fast-science/background-removal-server Client side post https://gitlab.com/fast-science/background-removal-vue

Visit product View on Hacker NewsAug 28, 2017

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: model, dock, using · Missing: mac, agents, macos

41%41% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released

39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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