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To handle the variability in real-world data, the\nsystem relies upon the technique of domain randomization, in which the\nparameters of the simulator$-$such as lighting, pose, object textures,\netc.$-$are randomized in non-realistic ways to force the neural network to\nlearn the essential features of the object of interest. We explore the\nimportance of these parameters, showing that it is possible to produce a\nnetwork with compelling performance using only non-artistically-generated\nsynthetic data. With additional fine-tuning on real data, the network yields\nbetter performance than using real data alone. This result opens up the\npossibility of using inexpensive synthetic data for training neural networks\nwhile avoiding the need to collect large amounts of hand-annotated real-world\ndata or to generate high-fidelity synthetic worlds$-$both of which remain\nbottlenecks for many applications. The approach is evaluated on bounding box\ndetection of cars on the KITTI dataset.","url_abs":"http://arxiv.org/abs/1804.06516v3","url_pdf":"http://arxiv.org/pdf/1804.06516v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"training-deep-networks-with-synthetic-data","repo_url":"https://github.com/diyer22/bpycv","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1804.06516","atlas_url":"https://app.syntology.ai/?focus=1804.06516","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.06516"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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