SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training?
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909261116735488 |
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| author | Hammoud, Hasan Abed Al Kader Itani, Hani Pizzati, Fabio Torr, Philip Bibi, Adel Ghanem, Bernard |
| author_facet | Hammoud, Hasan Abed Al Kader Itani, Hani Pizzati, Fabio Torr, Philip Bibi, Adel Ghanem, Bernard |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01832 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training? Hammoud, Hasan Abed Al Kader Itani, Hani Pizzati, Fabio Torr, Philip Bibi, Adel Ghanem, Bernard Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 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 |
| title | SynthCLIP: Are We Ready for a Fully Synthetic CLIP Training? |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2402.01832 |