VQ
VQASynth – pipelines to synthesize VQA datasets
VQASynth – pipelines to synthesize VQA datasets
Inspired by the recent work in SpatialVLM, we reproduce similar data synthesis pipelines using openly available models. We compare our results to alternative annotation pipelines like RAM-Grounded-SAM. Our repo uses a simple pipeline in docker compose to produce datasets suitable for fine-tuning multimodal models like LLaVA.
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Analyze your own launch →82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.
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53%53% predicted probability of success on AppSumo, based on ML models trained on real launch data.
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
31%31% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
16%16% predicted probability of success on BetaList, based on ML models trained on real launch data.
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
Incorrect prediction on native model
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