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Custom Model for Coloring Manga/Comics

Hacker News

Custom Model for Coloring Manga/Comics

Hey Hacker News! Have you ever wanted to read your favorite manga in color! We've trained a custom colorization model specifically on manga to fully automatically or semi-automatically colorize manga! We're also building a suite of tools for turning comics into motion comics, including segmentation, inpainting, and animation! We're looking for people to test our colorization model, it works either fully-automatically or with color hints. Would love to hear your thoughts! - Adi & Michael

Visit product View on Hacker NewsFeb 27, 2024

Actual performance

2points

Did not reach leaderboard

Launch Intel predictions

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

72%72% 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.

Product HuntOn track for Day 1 leaderboard · Strong signals: model, new · Missing: mac, agents, macos

70%70% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: hacker news, io, including · Missing: https docs, excited, just released

52%52% 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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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

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

14%14% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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