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        "description": "Spatial pyramid pooling predates this deep-network integration. Original training uses selected fixed input sizes with shared weights; acceptance of variable shapes is not proven accuracy invariance across arbitrary scales. Detector speedups are source benchmark conditions, not universal end-to-end ratios.",
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      "statement": "# SPP-net: spatial pyramid pooling on convolutional feature maps\n\nPlaced fixed-bin spatial pyramid pooling above convolutional maps to produce fixed-length features from varying image/region sizes and reuse whole-image convolutions across detection proposals.\n\n## Scientific scope\n\nSpatial pyramid pooling predates this deep-network integration. Original training uses selected fixed input sizes with shared weights; acceptance of variable shapes is not proven accuracy invariance across arbitrary scales. Detector speedups are source benchmark conditions, not universal end-to-end ratios.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nKaiming He; Xiangyu Zhang; Shaoqing Ren; Jian Sun\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://arxiv.org/abs/1406.4729v1)\n",
      "summary": "Placed fixed-bin spatial pyramid pooling above convolutional maps to produce fixed-length features from varying image/region sizes and reuse whole-image convolutions across detection proposals.",
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    "id": "he-et-al-spp-net-2014",
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    "title": "SPP-net: spatial pyramid pooling on convolutional feature maps",
    "context": "Convolutional visual recognition",
    "track": "Human-designed research method or resource",
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    "what_changed": "Placed fixed-bin spatial pyramid pooling above convolutional maps to produce fixed-length features from varying image/region sizes and reuse whole-image convolutions across detection proposals.",
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