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Remove Image Backgrounds, Free HD, No Signup

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Remove Image Backgrounds, Free HD, No Signup

Hi HN, Can old dogs learn new tricks? We've been running clippingmagic.com since we launched it here on hn in May 2013 [0]. It was originally based on a purely "classical" algorithm, but since then Deep Learning has taken over the field, leaving us with the simple choice to adapt or die. Most of our old competitors shut down. The new competition is "a bunch of kids". We're two 40+ year olds with young kids, mortgages, and a 15 year investment in classical image processing algorithms rendered largely obsolete by Deep Learning. Fun times. So it's been challenging, interesting, humbling & frustrating to switch out our entire toolbox and go from zero to our first full end-to-end DL solution for background removal over these last few years. It's been a journey with fits and starts. Classical algorithms are slow-and-steady work, craftsmanship if you will. DL is much more "doesn't work, doesn't work, it's a miracle, no it's crap, fixed it with better data, now don't touch the golden recipe!" (Along the way CM got a little DL boost, but it's a DL + classical algorithms hybrid) The big takeaway for us has been that DL is all about the data . The networks get all the glory, but it's the data that makes the bulk of the difference. While we have a host of ideas for changing the business model for background removal to enable a wider range of use-cases, right now we're primarily looking for feedback on this very early MVP. Thanks! [0]: https://news.ycombinator.com/item?id=5682831

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
87%87% 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.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: host · Missing: plus, platform, intuitive
52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: model, new · Missing: mac, agents, macos
32%32% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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