发布 PPLCNet ReID FP32 固定转换与来源说明

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NOTICE

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+PPLCNet ReID FP32 ONNX 转换
+来源：PaddlePaddle / PaddleDetection、PaddleClas
+原模型结构声明：Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+许可证：Apache License, Version 2.0（附完整 LICENSE）
+
+上游固定提交 b25522a0f4bde8c80603f3ba5e3472059972e3b5。
+官方原权重 deepsort_pplcnet.pdparams：
+https://paddledet.bj.bcebos.com/models/mot/deepsort/deepsort_pplcnet.pdparams
+SHA-256 abce7d12af14b5b5c10c287ba01517470db3247ee06e3beeaa5ffc1c76628758
+
+2026年由chenmohan转换为固定batch1 FP32 ONNX/opset17；移除注册依赖，
+不导出不属于Embedding forward的训练分类头，未重新训练或量化。
+ONNX SHA-256 24d347f47405bb1bd24fd582783507edbcb16571d2e845d0528b0ad7856336e4。
+本项目正常朝向RGB预处理与上游转置BGR部署不同，未嵌入ONNX。
+未包含上游tracker、Kalman、matching代码、原始训练或评测图像。
+分发依据和训练披露范围见README.md；本转换不表示上游背书。

README.en.md

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+# PPLCNet ReID FP32 · Web SDK adaptation
+
+[中文](README.md)
+
+This is an FP32 ONNX conversion of PaddleDetection's officially linked human `deepsort_pplcnet.pdparams`. It extracts appearance features from supplied human detection boxes; it neither detects objects, assigns track IDs, nor identifies a person's real identity. Adapted by chenmohan for [web-sdk-PP-Tracking](https://github.com/chenmohan123/web-sdk-PP-Tracking); this is not an official Paddle release.
+
+## Asset identity
+
+File: `pplcnet-reid/0.1.0/pplcnet-reid-fp32.onnx`; FP32, opset17; 33,704,835 bytes; SHA-256 `24d347f47405bb1bd24fd582783507edbcb16571d2e845d0528b0ad7856336e4`.
+
+Input: `crops`, float32 `[1,3,192,64]`. Output: `save_infer_model/scale_0.tmp_0`, float32 `[1,512]`. Architecture: PPLCNetEmbedding scale2.5, 1280-dimensional neck input, 512-dimensional output. Its 146 inference state tensors contain 8,419,792 elements including BN state, not a verified count of trainable parameters. The checkpoint's additional `head.weight [512,1502]` is outside the official embedding forward and is not exported.
+
+## Provenance, conversion and license basis
+
+- Fixed [PaddleDetection revision b25522a0f4bde8c80603f3ba5e3472059972e3b5](https://github.com/PaddlePaddle/PaddleDetection/tree/b25522a0f4bde8c80603f3ba5e3472059972e3b5). The [ReID README](https://github.com/PaddlePaddle/PaddleDetection/blob/b25522a0f4bde8c80603f3ba5e3472059972e3b5/configs/mot/deepsort/reid/README.md) explicitly lists the human PPLCNet-2.5x checkpoint.
+- [Official checkpoint](https://paddledet.bj.bcebos.com/models/mot/deepsort/deepsort_pplcnet.pdparams): 36,769,814 bytes; SHA-256 `abce7d12af14b5b5c10c287ba01517470db3247ee06e3beeaa5ffc1c76628758`.
+- Paddle2.6.2 / Paddle2ONNX1.3.1, fixed batch1. Registration dependencies were removed to export the original embedding inference graph; no retraining or quantization. Cropping, normalization and output L2 are outside ONNX.
+- Distribution adopts the official repository's overall **Apache-2.0** license as its basis for this officially linked checkpoint and conversion, retaining [LICENSE](LICENSE), [NOTICE](NOTICE), attribution and modification disclosure. The reviewed material does not include a separate checkpoint license/model card; no additional checkpoint-specific grant or downstream-use warranty is claimed.
+- Official documentation credits PaddleClas and discloses training on Market1501 (751 training identities), with the detailed training process still to be published. Head dimensions do not establish additional datasets. No training images/data are distributed; the model's license declaration does not replace dataset terms.
+
+## Web preprocessing and output
+
+`rgba8-white-upright-rgb-halfpixel-f64-imagenet-f32-v1`: the caller supplies upright sRGB, unpremultiplied RGBA8 and fully in-bounds human xywh boxes. Floor left/top, ceil right/bottom; composite alpha onto white; upright RGB, half-pixel bilinear resize to64×192 using float64 intermediate arithmetic; ImageNet mean `[0.485,0.456,0.406]` and std `[0.229,0.224,0.225]`; final float32 NCHW.
+
+Normalize each output using a float64 L2 norm and store float32 (`l2-f32-v1`), producing a512-dimensional unit vector. Bind the complete model SHA, preprocessing ID and L2 version to the feature space. This Web adaptation differs from the pinned upstream deployment's transposed BGR path: vectors are not interchangeable, and undocumented training preprocessing is not claimed as reproduced.
+
+## Validation and limits
+
+2026-09-21, Windows11 / i5-10400F / RTX5060Ti / Chromium153 / ORT Web1.27: WASM and WebGPU each pass34 fixtures against Paddle; maximum normalized absolute errors approximately3.502e-7/4.396e-7. WebGPU CPU fallback is disabled. Independent references and reports are in SDK `reports/2026-09-21-reid-module/`. These results do not establish all-browser or phone compatibility.
+
+Small closed-set retrieval with GT boxes is not complete tracking evaluation. Similar clothing, occlusion, low resolution, lighting and camera changes can confuse appearance matching. Distribution adds no claim of improved IDF1/MOTA or complete video FPS. This human model is not a general vehicle/object ReID model. The host decodes files; transparent PNG colors may change through Canvas, so original-file pixels and decoded RGBA are not guaranteed byte-identical.

README.md

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+---
+license: apache-2.0
+library_name: onnx
+tags: [onnx, re-identification, tracking, pplcnet, webgpu]
+language: [zh, en]
+---
+
+# PPLCNet ReID FP32 · Web SDK 适配
+
+[English](README.en.md)
+
+这是 PaddleDetection 官方发布的人体 `deepsort_pplcnet.pdparams` 的 FP32 ONNX 转换，用于已有检测框中的人体外观特征提取。它不是检测器，不直接分配轨迹 ID，也不识别人的真实身份。对应 SDK 为 [web-sdk-PP-Tracking](https://github.com/chenmohan123/web-sdk-PP-Tracking)，转换者 chenmohan；非 Paddle 官方发行。
+
+## 固定资产
+
+| 字段 | 值 |
+| --- | --- |
+| 文件 | `pplcnet-reid/0.1.0/pplcnet-reid-fp32.onnx` |
+| 格式 | FP32 ONNX，opset 17 |
+| 大小 | 33,704,835 bytes |
+| SHA-256 | `24d347f47405bb1bd24fd582783507edbcb16571d2e845d0528b0ad7856336e4` |
+| 输入 | `crops`，float32 `[1,3,192,64]` |
+| 输出 | `save_infer_model/scale_0.tmp_0`，float32 `[1,512]` |
+
+网络为 PPLCNetEmbedding（scale 2.5，1280 维 neck 输入、512 维输出）。推理状态共 146 个张量、8,419,792 个元素，包含 BN 状态；未将其冒称为全部可训练参数。checkpoint 附加的 `head.weight [512,1502]` 不在官方 Embedding forward 内，转换不使用该训练头。
+
+## 来源、转换与许可依据
+
+- 官方仓库固定提交：[b25522a0f4bde8c80603f3ba5e3472059972e3b5](https://github.com/PaddlePaddle/PaddleDetection/tree/b25522a0f4bde8c80603f3ba5e3472059972e3b5)。[ReID README](https://github.com/PaddlePaddle/PaddleDetection/blob/b25522a0f4bde8c80603f3ba5e3472059972e3b5/configs/mot/deepsort/reid/README.md)明确列出 PPLCNet-2.5x 人体模型及权重。
+- 原权重：[官方下载](https://paddledet.bj.bcebos.com/models/mot/deepsort/deepsort_pplcnet.pdparams)，36,769,814 bytes，SHA-256 `abce7d12af14b5b5c10c287ba01517470db3247ee06e3beeaa5ffc1c76628758`。
+- 转换：Paddle 2.6.2 / Paddle2ONNX 1.3.1，固定batch1；仅去除注册依赖、导出原Embedding推理图，没有重新训练或量化。裁剪、标准化及 L2 不在 ONNX 内。
+- 分发采用上述官方仓库整体 **Apache-2.0** 授权作为官方链接权重及转换产物的依据，保留原[LICENSE](LICENSE)与[NOTICE](NOTICE)，注明来源和修改。所读取材料没有权重专属许可/独立模型卡，未声称取得额外的checkpoint专属授权或下游用途保证。
+- 官方文档披露由 PaddleClas 提供、Market1501（751训练身份）训练，具体训练过程待公布。训练头维度不能用于推断额外训练集合。本仓库不分发训练图像或数据，模型的Apache许可声明不替代数据集自身条件。
+
+## Web SDK 预处理与输出
+
+预处理版本 `rgba8-white-upright-rgb-halfpixel-f64-imagenet-f32-v1`：调用者提供方向已处理的 sRGB、非预乘 RGBA8 与完整图内 xywh 人体框；左/上 floor、右/下 ceil；透明像素白底合成；正常朝向 RGB、half-pixel 双线性缩放到 64×192，float64 中间计算；ImageNet mean `[0.485,0.456,0.406]` / std `[0.229,0.224,0.225]`，最终 float32 NCHW。
+
+输出按 float64 范数 L2 归一化并写回 float32（`l2-f32-v1`），得到512维单位向量。特征空间须同时绑定完整模型 SHA、预处理及 L2 版本。该 Web 适配与固定上游部署的转置 BGR 路径不同，不能与其向量混用，也不宣称复现未公开的训练预处理。
+
+## 验证与限制
+
+2026-09-21，本机 Windows 11 / i5-10400F / RTX5060Ti / Chromium153 / ORT Web1.27：WASM 与 WebGPU 各34组向量对 Paddle 参考通过；归一化最大绝对差约3.502e-7/4.396e-7。WebGPU禁CPU回退。源码与独立参考报告在SDK仓库的 `reports/2026-09-21-reid-module/`；不代表所有浏览器或手机兼容。
+
+小样本、GT人体框的闭集检索不是完整跟踪精度；相似衣着、遮挡、低分辨率、光照与相机变化可能导致混淆。没有以本次分发宣称IDF1/MOTA提升或完整视频FPS。模型面向人体，不应把512维接口视为车辆或所有物体的通用ReID。文件解码由宿主处理，透明PNG经Canvas可能发生颜色变化，原始文件与解码RGBA并非逐字节等价。

configuration.json

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+{}

pplcnet-reid/0.1.0/LICENSE

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pplcnet-reid/0.1.0/NOTICE

@@ -0,0 +1,16 @@
+PPLCNet ReID FP32 ONNX 转换
+来源：PaddlePaddle / PaddleDetection、PaddleClas
+原模型结构声明：Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+许可证：Apache License, Version 2.0（附完整 LICENSE）
+
+上游固定提交 b25522a0f4bde8c80603f3ba5e3472059972e3b5。
+官方原权重 deepsort_pplcnet.pdparams：
+https://paddledet.bj.bcebos.com/models/mot/deepsort/deepsort_pplcnet.pdparams
+SHA-256 abce7d12af14b5b5c10c287ba01517470db3247ee06e3beeaa5ffc1c76628758
+
+2026年由chenmohan转换为固定batch1 FP32 ONNX/opset17；移除注册依赖，
+不导出不属于Embedding forward的训练分类头，未重新训练或量化。
+ONNX SHA-256 24d347f47405bb1bd24fd582783507edbcb16571d2e845d0528b0ad7856336e4。
+本项目正常朝向RGB预处理与上游转置BGR部署不同，未嵌入ONNX。
+未包含上游tracker、Kalman、matching代码、原始训练或评测图像。
+分发依据和训练披露范围见README.md；本转换不表示上游背书。

pplcnet-reid/0.1.0/README.en.md

@@ -0,0 +1,31 @@
+# PPLCNet ReID FP32 · Web SDK adaptation
+
+[中文](README.md)
+
+This is an FP32 ONNX conversion of PaddleDetection's officially linked human `deepsort_pplcnet.pdparams`. It extracts appearance features from supplied human detection boxes; it neither detects objects, assigns track IDs, nor identifies a person's real identity. Adapted by chenmohan for [web-sdk-PP-Tracking](https://github.com/chenmohan123/web-sdk-PP-Tracking); this is not an official Paddle release.
+
+## Asset identity
+
+File: `pplcnet-reid/0.1.0/pplcnet-reid-fp32.onnx`; FP32, opset17; 33,704,835 bytes; SHA-256 `24d347f47405bb1bd24fd582783507edbcb16571d2e845d0528b0ad7856336e4`.
+
+Input: `crops`, float32 `[1,3,192,64]`. Output: `save_infer_model/scale_0.tmp_0`, float32 `[1,512]`. Architecture: PPLCNetEmbedding scale2.5, 1280-dimensional neck input, 512-dimensional output. Its 146 inference state tensors contain 8,419,792 elements including BN state, not a verified count of trainable parameters. The checkpoint's additional `head.weight [512,1502]` is outside the official embedding forward and is not exported.
+
+## Provenance, conversion and license basis
+
+- Fixed [PaddleDetection revision b25522a0f4bde8c80603f3ba5e3472059972e3b5](https://github.com/PaddlePaddle/PaddleDetection/tree/b25522a0f4bde8c80603f3ba5e3472059972e3b5). The [ReID README](https://github.com/PaddlePaddle/PaddleDetection/blob/b25522a0f4bde8c80603f3ba5e3472059972e3b5/configs/mot/deepsort/reid/README.md) explicitly lists the human PPLCNet-2.5x checkpoint.
+- [Official checkpoint](https://paddledet.bj.bcebos.com/models/mot/deepsort/deepsort_pplcnet.pdparams): 36,769,814 bytes; SHA-256 `abce7d12af14b5b5c10c287ba01517470db3247ee06e3beeaa5ffc1c76628758`.
+- Paddle2.6.2 / Paddle2ONNX1.3.1, fixed batch1. Registration dependencies were removed to export the original embedding inference graph; no retraining or quantization. Cropping, normalization and output L2 are outside ONNX.
+- Distribution adopts the official repository's overall **Apache-2.0** license as its basis for this officially linked checkpoint and conversion, retaining [LICENSE](LICENSE), [NOTICE](NOTICE), attribution and modification disclosure. The reviewed material does not include a separate checkpoint license/model card; no additional checkpoint-specific grant or downstream-use warranty is claimed.
+- Official documentation credits PaddleClas and discloses training on Market1501 (751 training identities), with the detailed training process still to be published. Head dimensions do not establish additional datasets. No training images/data are distributed; the model's license declaration does not replace dataset terms.
+
+## Web preprocessing and output
+
+`rgba8-white-upright-rgb-halfpixel-f64-imagenet-f32-v1`: the caller supplies upright sRGB, unpremultiplied RGBA8 and fully in-bounds human xywh boxes. Floor left/top, ceil right/bottom; composite alpha onto white; upright RGB, half-pixel bilinear resize to64×192 using float64 intermediate arithmetic; ImageNet mean `[0.485,0.456,0.406]` and std `[0.229,0.224,0.225]`; final float32 NCHW.
+
+Normalize each output using a float64 L2 norm and store float32 (`l2-f32-v1`), producing a512-dimensional unit vector. Bind the complete model SHA, preprocessing ID and L2 version to the feature space. This Web adaptation differs from the pinned upstream deployment's transposed BGR path: vectors are not interchangeable, and undocumented training preprocessing is not claimed as reproduced.
+
+## Validation and limits
+
+2026-09-21, Windows11 / i5-10400F / RTX5060Ti / Chromium153 / ORT Web1.27: WASM and WebGPU each pass34 fixtures against Paddle; maximum normalized absolute errors approximately3.502e-7/4.396e-7. WebGPU CPU fallback is disabled. Independent references and reports are in SDK `reports/2026-09-21-reid-module/`. These results do not establish all-browser or phone compatibility.
+
+Small closed-set retrieval with GT boxes is not complete tracking evaluation. Similar clothing, occlusion, low resolution, lighting and camera changes can confuse appearance matching. Distribution adds no claim of improved IDF1/MOTA or complete video FPS. This human model is not a general vehicle/object ReID model. The host decodes files; transparent PNG colors may change through Canvas, so original-file pixels and decoded RGBA are not guaranteed byte-identical.

pplcnet-reid/0.1.0/README.md

@@ -0,0 +1,45 @@
+---
+license: apache-2.0
+library_name: onnx
+tags: [onnx, re-identification, tracking, pplcnet, webgpu]
+language: [zh, en]
+---
+
+# PPLCNet ReID FP32 · Web SDK 适配
+
+[English](README.en.md)
+
+这是 PaddleDetection 官方发布的人体 `deepsort_pplcnet.pdparams` 的 FP32 ONNX 转换，用于已有检测框中的人体外观特征提取。它不是检测器，不直接分配轨迹 ID，也不识别人的真实身份。对应 SDK 为 [web-sdk-PP-Tracking](https://github.com/chenmohan123/web-sdk-PP-Tracking)，转换者 chenmohan；非 Paddle 官方发行。
+
+## 固定资产
+
+| 字段 | 值 |
+| --- | --- |
+| 文件 | `pplcnet-reid/0.1.0/pplcnet-reid-fp32.onnx` |
+| 格式 | FP32 ONNX，opset 17 |
+| 大小 | 33,704,835 bytes |
+| SHA-256 | `24d347f47405bb1bd24fd582783507edbcb16571d2e845d0528b0ad7856336e4` |
+| 输入 | `crops`，float32 `[1,3,192,64]` |
+| 输出 | `save_infer_model/scale_0.tmp_0`，float32 `[1,512]` |
+
+网络为 PPLCNetEmbedding（scale 2.5，1280 维 neck 输入、512 维输出）。推理状态共 146 个张量、8,419,792 个元素，包含 BN 状态；未将其冒称为全部可训练参数。checkpoint 附加的 `head.weight [512,1502]` 不在官方 Embedding forward 内，转换不使用该训练头。
+
+## 来源、转换与许可依据
+
+- 官方仓库固定提交：[b25522a0f4bde8c80603f3ba5e3472059972e3b5](https://github.com/PaddlePaddle/PaddleDetection/tree/b25522a0f4bde8c80603f3ba5e3472059972e3b5)。[ReID README](https://github.com/PaddlePaddle/PaddleDetection/blob/b25522a0f4bde8c80603f3ba5e3472059972e3b5/configs/mot/deepsort/reid/README.md)明确列出 PPLCNet-2.5x 人体模型及权重。
+- 原权重：[官方下载](https://paddledet.bj.bcebos.com/models/mot/deepsort/deepsort_pplcnet.pdparams)，36,769,814 bytes，SHA-256 `abce7d12af14b5b5c10c287ba01517470db3247ee06e3beeaa5ffc1c76628758`。
+- 转换：Paddle 2.6.2 / Paddle2ONNX 1.3.1，固定batch1；仅去除注册依赖、导出原Embedding推理图，没有重新训练或量化。裁剪、标准化及 L2 不在 ONNX 内。
+- 分发采用上述官方仓库整体 **Apache-2.0** 授权作为官方链接权重及转换产物的依据，保留原[LICENSE](LICENSE)与[NOTICE](NOTICE)，注明来源和修改。所读取材料没有权重专属许可/独立模型卡，未声称取得额外的checkpoint专属授权或下游用途保证。
+- 官方文档披露由 PaddleClas 提供、Market1501（751训练身份）训练，具体训练过程待公布。训练头维度不能用于推断额外训练集合。本仓库不分发训练图像或数据，模型的Apache许可声明不替代数据集自身条件。
+
+## Web SDK 预处理与输出
+
+预处理版本 `rgba8-white-upright-rgb-halfpixel-f64-imagenet-f32-v1`：调用者提供方向已处理的 sRGB、非预乘 RGBA8 与完整图内 xywh 人体框；左/上 floor、右/下 ceil；透明像素白底合成；正常朝向 RGB、half-pixel 双线性缩放到 64×192，float64 中间计算；ImageNet mean `[0.485,0.456,0.406]` / std `[0.229,0.224,0.225]`，最终 float32 NCHW。
+
+输出按 float64 范数 L2 归一化并写回 float32（`l2-f32-v1`），得到512维单位向量。特征空间须同时绑定完整模型 SHA、预处理及 L2 版本。该 Web 适配与固定上游部署的转置 BGR 路径不同，不能与其向量混用，也不宣称复现未公开的训练预处理。
+
+## 验证与限制
+
+2026-09-21，本机 Windows 11 / i5-10400F / RTX5060Ti / Chromium153 / ORT Web1.27：WASM 与 WebGPU 各34组向量对 Paddle 参考通过；归一化最大绝对差约3.502e-7/4.396e-7。WebGPU禁CPU回退。源码与独立参考报告在SDK仓库的 `reports/2026-09-21-reid-module/`；不代表所有浏览器或手机兼容。
+
+小样本、GT人体框的闭集检索不是完整跟踪精度；相似衣着、遮挡、低分辨率、光照与相机变化可能导致混淆。没有以本次分发宣称IDF1/MOTA提升或完整视频FPS。模型面向人体，不应把512维接口视为车辆或所有物体的通用ReID。文件解码由宿主处理，透明PNG经Canvas可能发生颜色变化，原始文件与解码RGBA并非逐字节等价。

pplcnet-reid/0.1.0/model.json

@@ -0,0 +1,21 @@
+{
+  "id": "pplcnet-reid-fp32",
+  "version": "0.1.0",
+  "format": "onnx",
+  "precision": "fp32",
+  "opset": 17,
+  "path": "pplcnet-reid/0.1.0/pplcnet-reid-fp32.onnx",
+  "bytes": 33704835,
+  "sha256": "24d347f47405bb1bd24fd582783507edbcb16571d2e845d0528b0ad7856336e4",
+  "parameterCount": null,
+  "inferenceStateElementsIncludingBatchNorm": 8419792,
+  "license": "Apache-2.0",
+  "licenseBasis": "采用官方仓库整体授权；依据与已知披露范围见README.md",
+  "upstreamRevision": "b25522a0f4bde8c80603f3ba5e3472059972e3b5",
+  "input": { "name": "crops", "dtype": "float32", "shape": [1, 3, 192, 64] },
+  "output": { "name": "save_infer_model/scale_0.tmp_0", "dtype": "float32", "shape": [1, 512] },
+  "preprocessingId": "rgba8-white-upright-rgb-halfpixel-f64-imagenet-f32-v1",
+  "normalizationId": "l2-f32-v1",
+  "defaultSource": "modelscope",
+  "backends": ["wasm", "webgpu"]
+}

pplcnet-reid/0.1.0/pplcnet-reid-fp32.onnx

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