{"schema":"record/v1","id":"art_01M5M8D0ZKQ5C9RWWKSMMFGVSP","type":"article","revision":1,"etag":"\"r1.s0\"","title":"Which implementation details (resizing, JPEG, quantization, data_range, feature layer) are documented to change image-quality metric values?","summary":"The clean-fid authors report that resizing FFHQ with non-antialiased bicubic implementations (OpenCV, PyTorch, TensorFlow) yields FID of 6 or more against PIL-bicubic, while other PIL filters stay at 0.75 or below, and that JPEG compression strongly shifts FID; pytorch-fid lets users pick Inception feature layers (64/192/768/2048 dims, default 2048) whose scores are not comparable. Component terms differ: clean-fid code MIT, its Inception wrapper derived from Apache-2.0 pytorch-fid, the Inception-v3 weights originally Apache-2.0 via TensorFlow Models.","status":"active","body":{"text":"The clean-fid authors report that resizing FFHQ with non-antialiased bicubic implementations (OpenCV, PyTorch, TensorFlow) yields FID of 6 or more against PIL-bicubic, while other PIL filters stay at 0.75 or below, and that JPEG compression strongly shifts FID; pytorch-fid lets users pick Inception feature layers (64/192/768/2048 dims, default 2048) whose scores are not comparable. Component terms differ: clean-fid code MIT, its Inception wrapper derived from Apache-2.0 pytorch-fid, the Inception-v3 weights originally Apache-2.0 via TensorFlow Models.\n\nCurator qualifications: Paper v3 (S11) fetched but not read in detail; figures cited from the README. PSNR ValueError behaviour (E5) not re-read. Values are author-reported, not reproduced. 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README/paper text only; no metric computed; no Inception weights downloaded.\n\nLimitations: One dataset (FFHQ) in the reported comparison.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/pdf/2104.11222v3","note":"GaParmar/clean-fid README at e88c4d62, lines 56-73, 481-487; mseitzer/pytorch-fid README at b9c18118, lines 14, 41-56, 81-83; scikit-image SSIM notes (as in CA-B137)"},{"url":"https://github.com/scikit-image/scikit-image/blob/ee0a7a3ebd9ac8c2602f40e55bc015a3c8a81ae8/src/skimage/metrics/_structural_similarity.py","note":"GaParmar/clean-fid README at e88c4d62, lines 56-73, 481-487; mseitzer/pytorch-fid README at b9c18118, lines 14, 41-56, 81-83; scikit-image SSIM notes (as in CA-B137)"},{"url":"https://github.com/scikit-image/scikit-image/blob/ee0a7a3ebd9ac8c2602f40e55bc015a3c8a81ae8/src/skimage/metrics/simple_metrics.py","note":"GaParmar/clean-fid README at e88c4d62, lines 56-73, 481-487; mseitzer/pytorch-fid README at b9c18118, lines 14, 41-56, 81-83; scikit-image SSIM notes (as in CA-B137)"},{"url":"https://github.com/mseitzer/pytorch-fid/blob/b9c18118d082cbd263c1b8963fc4221dc1cbb659/README.md","note":"GaParmar/clean-fid README at e88c4d62, lines 56-73, 481-487; mseitzer/pytorch-fid README at b9c18118, lines 14, 41-56, 81-83; scikit-image SSIM notes (as in CA-B137)"},{"url":"https://github.com/GaParmar/clean-fid/blob/e88c4d6269a4bbf04c04deeb578475b57719acee/README.md","note":"GaParmar/clean-fid README at e88c4d62, lines 56-73, 481-487; mseitzer/pytorch-fid README at b9c18118, lines 14, 41-56, 81-83; scikit-image SSIM notes (as in CA-B137)"}]},"tags":[],"type":"article","links":[],"title":"Which implementation details (resizing, JPEG, quantization, data_range, feature layer) are documented to change image-quality metric values?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"The clean-fid authors report that resizing FFHQ with non-antialiased bicubic implementations (OpenCV, PyTorch, TensorFlow) yields FID of 6 or more against PIL-bicubic, while other PIL filters stay at 0.75 or below, and that JPEG compression strongly shifts FID; pytorch-fid lets users pick Inception feature layers (64/192/768/2048 dims, default 2048) whose scores are not comparable. 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