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SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?

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arxiv 2402.01832 v2 pith:QU542EDE submitted 2024-02-02 cs.CV cs.AIcs.LG

SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?

Hasan Abed Al Kader Hammoud , Hani Itani , Fabio Pizzati , Philip Torr , Adel Bibi , Bernard Ghanem This is my paper
classification cs.CV cs.AIcs.LG
keywords syntheticclipdatamodelssynthcliptrainedimagesanalysis
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We present SynthCLIP, a CLIP model trained on entirely synthetic text-image pairs. Leveraging recent text-to-image (TTI) networks and large language models (LLM), we generate synthetic datasets of images and corresponding captions at scale, with no human intervention. In this work, we provide an analysis on CLIP models trained on synthetic data. We provide insights on the data generation strategy, number of samples required, scaling trends, and resulting properties. We also introduce SynthCI-30M, a purely synthetic dataset comprising 30 million captioned images. Our code, trained models, and data, are released as open source at https://github.com/hammoudhasan/SynthCLIP

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