We lifted 12 functions out of this paper's own repositories and ran 11 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| hammoudhasan/synthclip | canonical | 11 of 12 |
| Function | Status | Where it lives |
|---|---|---|
| all_gather_batch | Ran | hammoudhasan/synthclip/Training/utils.py pointer only (licence: NONE) · get_code("d888047d3f40a1a5") |
| balance_sampling | Ran | hammoudhasan/synthclip/TextGen/balancing.py pointer only (licence: NONE) · get_code("14ba694ed500c3d5") |
| basic_clean | Ran | hammoudhasan/synthclip/Training/tokenizer.py pointer only (licence: NONE) · get_code("98f385d847636a3e") |
| dist_func | Ran | hammoudhasan/synthclip/TextGen/substr_matching.py pointer only (licence: NONE) · get_code("2918369c57cf9ccd") |
| get_imagenet | Ran | hammoudhasan/synthclip/Training/datasets.py pointer only (licence: NONE) · get_code("76b6f8ebe3d85394") |
| get_pairs | Ran | hammoudhasan/synthclip/Training/tokenizer.py pointer only (licence: NONE) · get_code("d919ae32e5e4e616") |
| get_rewrites_from_file | Ran | hammoudhasan/synthclip/ImageGen/batched_txt2img.py pointer only (licence: NONE) · get_code("2d570f88cec83aee") |
| scaled_all_reduce | Ran | hammoudhasan/synthclip/Training/utils.py pointer only (licence: NONE) · get_code("dc80fec8c333239e") |
| spacing | Ran | hammoudhasan/synthclip/TextGen/substr_matching.py pointer only (licence: NONE) · get_code("cc85393791526ee3") |
| substr_matching | Ran | hammoudhasan/synthclip/TextGen/substr_matching.py pointer only (licence: NONE) · get_code("c0af2ad3ff3eea0e") |
| whitespace_clean | Ran | hammoudhasan/synthclip/Training/tokenizer.py pointer only (licence: NONE) · get_code("9542161e9640b858") |
| get_model | Not yet run | hammoudhasan/synthclip/Training/utils.py pointer only (licence: NONE) · get_code("2ec9ac675733a9ac") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2402.01832")
get_code_for_paper("2402.01832")
have("2402.01832")
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