🚀 Rapidata: Setting the Standard for Model Evaluation
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🚀 Rapidata: Setting the Standard for Model Evaluation
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Rapidata is proud to announce our first independent appearance in academic research, featured in the Lumina-Image 2.0 paper. This marks the beginning of our journey to become the standard for testing text-to-image and generative models. Our expertise in large-scale human annotations allows researchers to refine their models with accurate, real-world feedback.
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Rapidata is proud to announce our first independent appearance in academic research, featured in the Lumina-Image 2.0 paper. This marks the beginning of our journey to become the standard for testing text-to-image and generative models. Our expertise in large-scale human annotations allows researchers to refine their models with accurate, real-world feedback.
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As we continue to establish ourselves as a key player in model evaluation, we’re here to support researchers with high-quality annotations at scale. Reach out to info@rapidata.ai to see how we can help.
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As we continue to establish ourselves as a key player in model evaluation, we’re here to support researchers with high-quality annotations at scale. Reach out to info@rapidata.ai to see how we can help.
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Lumina-Image 2.0: A Unified and Efficient Image Generative Framework (2503.21758)
https://huggingface.co/papers/2503.21758
2503.21758
paper
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https://huggingface.co/papers/2503.21758
🚀 Rapidata: Setting the Standard for Model Evaluation

Rapidata is proud to announce our first independent appearance in academic research, featured in the Lumina-Image 2.0 paper. This marks the beginning of our journey to become the standard for testing text-to-image and generative models. Our expertise in large-scale human annotations allows researchers to refine their models with accurate, real-world feedback.

As we continue to establish ourselves as a key player in model evaluation, we’re here to support researchers with high-quality annotations at scale. Reach out to info@rapidata.ai to see how we can help.

https://huggingface.co/papers/2503.21758
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