Op

Open-Source Image Model Leaderboard with Public Preference Data

Hacker News

Open-Source Image Model Leaderboard with Public Preference Data

We recently open sourced this public leaderboard and dataset with 50K+ preference data points across 20+ image models. Hoping that this is a useful resource for fine-tuning / preference optimization. Preference data is updated every day. Code for frontend: https://github.com/fal-ai/imgsys-public Preference Data: https://huggingface.co/datasets/fal-ai/imgsys-results

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Actual performance

4points
7comments
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, code · Missing: mac, agents, macos
68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
50%50% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, io · Missing: https docs, excited, just released
48%48% 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 · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
27%27% 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
17%17% 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
7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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