● Covered by 1 source · 1 reportMedium impact
PRX Update: New Data Strategy for Training Model
Key points
- PRX's data strategy prioritizes large, diverse dataset assembly.
- Long captions improve the quality of image samples.
- The model is pre-trained on a mix of public and internal datasets.
Overview of PRX's Data Strategy
PRX has shared details on its data strategy for training, which involves assembling a diverse dataset from public and internal sources. This strategy aims to prioritize breadth and diversity rather than perfection in image quality.
Importance of Dataset Composition
The goal during pre-training is for the model to learn about visual concepts and the range of possible images. A larger, more varied dataset provides better training material for understanding the visual world than a smaller set of aesthetically pleasing images. Filtering data for aesthetics prematurely can limit the model's learning potential.
Utilization of Existing Resources
To create its pre-training dataset, PRX leverages existing curated datasets rather than starting from scratch. This pragmatic approach allows for quick assembly and maintains quality by using already-filtered sources for NSFW content and personal information.
Captioning Strategy for Enhanced Training
PRX found that using long, descriptive captions significantly improved the quality of image samples. Faithful captioning ensures that even imperfect images contribute positively to model training, as they provide necessary context and details.
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Primary sources
GitHub mosaicml/streaming GitHub vllm-project/vllm GitHub Photoroom/PRX GitHub huggingface/diffusers GitHub Photoroom/PRX. arXiv 2103.00020Reporting from
PRX outlines its data strategy for model training, focusing on assembling a diverse dataset. The approach emphasizes breadth over perfection in data selection, utilizing existing public and internal datasets for training efficiencies.