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Florence2-Sharp – Advanced Image Understanding and OCR in C#

Hacker News

Florence2-Sharp – Advanced Image Understanding and OCR in C#

We're excited to share florence2-sharp, a C# library implementing the Florence-2-model for advanced image understanding tasks. Florence-2 uses a prompt-based approach to a variety of vision tasks, and provides great zero-shot performance across many vision tasks. Our C# library supports: - Image captioning (from concise to detailed) - Optical Character Recognition (OCR) - Region-based OCR - Object detection - Optional phrase grounding The library is a C# port of Microsoft's Florence-2 model (from https://huggingface.co/microsoft/Florence-2-base ), based on the original model and the JS port by Frank Krueger ( https://github.com/praeclarum/transformers-js ). Repo: https://github.com/curiosity-ai/florence2-sharp NuGet: https://www.nuget.org/packages/Florence2

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, ios · Missing: reddit linkedin, podcasting, created
76%76% 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: excited, ide, io · Missing: https docs, just released, exist
58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: ios · Missing: mobile apps, personal, entrepreneurs
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: model, tasks · Missing: mac, agents, macos
39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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 · Missing: arr, mrr, revenue
11%11% 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.

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