Object Detection in the Wild (ODinW) is a benchmark/leaderboard (originating from the "Computer Vision in the Wild" community / EvalAI challenge) that aggregates multiple public object‑detection datasets to evaluate in-the-wild / zero-shot transfer performance of detectors. "ODinW13" refers to a specific subset of 13 datasets from the ODinW collection that is commonly reported as a single metric (average mAP across those 13 datasets) for measuring in-the-wild zero-shot detection. ODinW/ODinW13 is not a single stand-alone dataset with one canonical paper introducing it; instead it is a benchmark suite (used by many papers) and appears as an evaluation collection in numerous object-detection / open-vocabulary detection papers (for example: ScaleDet — arXiv:2306.04849 — which reports results on “13 datasets from Object Detection in the Wild (ODinW)”, and other open-vocabulary detection works that evaluate on ODinW). The ODinW benchmark is linked to the EvalAI challenge page for “Object Detection in the Wild” (Computer Vision in the Wild). Because ODinW13 is a reported subset/metric of that benchmark (not an independent dataset release), there is no single introducing arXiv paper or Hugging Face dataset page to link to; papers that use ODinW13 typically cite the benchmark or the CVinW/ELEVATER resources when reporting results.
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