{ "id": "bench-v0", "name": "withoutBG Bench v0: background removal evaluation", "description": "Per-image alpha matte scores (MAE, MGE, band MAE) against ground-truth mattes for 14 background removal models on 60 real photos. Every model runs on the same inputs; ground truth and predictions are scored at a 1024 px shorter side (never upscaled). Lower is better.", "url": "https://withoutbg.com/benchmarks/open-background-removal", "license": "https://creativecommons.org/licenses/by/4.0/", "creator": "Imran Kocabiyik", "publisher": "withoutBG", "methodology": { "canonicalShortSide": 1024, "resize": "Ground truth and prediction are both resized (Lanczos) to a 1024 px shorter side, aspect kept. Images already at or below that size are not upscaled.", "metrics": [ { "key": "mae", "name": "MAE (overall accuracy)" }, { "key": "mge", "name": "MGE (edge quality, Sobel gradient error)" }, { "key": "bandMae", "name": "Band MAE (error within a 10 px band at 1024 px around the matte edge)" } ], "aggregation": "summary. is the per-image average (every image weighs the same; shown on withoutbg.com). summary.Pooled is the sum of per-image error sums divided by the sum of pixels. Each image carries Sum and pixels, so any subset can be re-aggregated.", "excludedImages": [] }, "router": { "description": "withoutBG Open Weights and Pro Model API are routed pipelines. A classifier sends fine strands, soft detail and transparency to the withoutBG matting model, and hard opaque objects, flat scenes and vehicles to BiRefNet (ZhengPeng7/BiRefNet, MIT), which is part of the pipeline. Pro Model API also runs an edge refiner on the result of either branch, so its BiRefNet-routed images are refined BiRefNet output. Car endpoints add further steps (such as cast shadows) that are not part of this benchmark. images[].withoutbgRoute is that decision for each image; summaryByWithoutbgRoute splits every model's scores by it.", "counts": { "matting": 41, "birefnet": 17 } }, "imageCount": 60, "models": [ { "client": "withoutbg-api", "label": "withoutBG Pro Model API", "summary": { "images": 60, "mae": 0.02399212722942383, "maePooled": 0.02127047250368791, "mge": 0.027366739680061467, "mgePooled": 0.024793952345372912, "bandMae": 0.053643226151588376, "bandMaePooled": 0.05254901405360216 }, "summaryByWithoutbgRoute": { "matting": { "images": 41, "mae": 0.012764484953766976, "maePooled": 0.013178078156472388, "mge": 0.01988845011516874, "mgePooled": 0.02079865697010099, "bandMae": 0.0355597891460918, "bandMaePooled": 0.04218803011464354 }, "birefnet": { "images": 17, "mae": 0.012327075756735707, "maePooled": 0.013796603195379875, "mge": 0.02600711855090302, "mgePooled": 0.02857078462539055, "bandMae": 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