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LICENSE

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+Easy4K SFDP - Siamese NET AI
+Copyright (c) 2026 Easy4K
+
+本模型权重与示例代码采用 Creative Commons Attribution-NonCommercial 4.0
+International License（CC BY-NC 4.0）授权。
+
+你可以自由地共享（复制、分发）与演绎（修改、转换、基于本作品创作），
+但必须遵守以下条件：
+  - 署名：保留作者署名与许可证声明，并注明是否作出修改；
+  - 非商业性使用：不得将本作品用于商业目的。
+
+许可证全文：https://creativecommons.org/licenses/by-nc/4.0/legalcode
+中文摘要：https://creativecommons.org/licenses/by-nc/4.0/deed.zh-hans

README.md

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----
-license: cc-by-nc-4.0
----
+---
+license: cc-by-nc-4.0
+library_name: onnxruntime
+tags:
+  - video
+  - frame-deduplication
+  - siamese-network
+  - onnx
+  - directml
+datasets:
+  - wikimedia-commons
+---
+
+# Easy4K SFDP · Siamese NET AI
+
+轻量孪生网络，用于判断视频相邻两帧的局部画面是否重复（1 拍 2 / 1 拍 3 去重场景）。
+是对 Easy4K 现有 SFDP 四阶判决算法（像素差 / dHash / QR 分解 / Farneback 光流）的知识蒸馏。
+
+## 规格
+
+| 项 | 值 |
+|---|---|
+| 输入 | 两帧灰度 128x128，float32，范围 [0,1]，通道顺序 NCHW |
+| 输出 | `score` ∈ [0,1]，越接近 1 越可能重复 |
+| 参数量 | 1402465 |
+| ONNX | opset 17，动态 batch |
+| 运行时 | onnxruntime（DirectML / CPU），NVIDIA / AMD / Intel 通用 |
+| 模型大小 | 5.36 MB |
+
+## 训练方式
+
+1. 教师：Easy4K 生产用 SFDP 判决器（完美模式，四阶全开）；
+2. 数据：Wikimedia Commons 的 CC BY / CC BY-SA / CC0 视频，切成固定窗口连续帧，
+   按 1拍1 / 1拍2 / 1拍3 / 混合节奏生成训练片段；
+3. 洗数据：教师判重的记为 1，光流位移映射为 0~1 连续软标签；
+   光流 0.1~0.4 的模糊区丢弃；正负比控制在 1:1~1:2；
+4. 难例：第一阶认为一样、被光流救回的样本加权（hard weight 2.0）；
+5. 目标：对软标签做 MSE 回归，不是二分类。
+
+## 验证指标
+
+- 验证集 MAE：0.00573
+- 0.5 阈值判决准确率：0.99435
+- 正样本 MAE：0.00476 ｜ 负样本 MAE：0.00613
+- 建议阈值：0.5（要更少误判可用 0.6~0.7）
+
+## 实测
+
+- NVIDIA P106-100（CPU 执行器，批量 32 对）：0.22 ms/对
+- AMD Radeon RX 580（Windows 11 + DirectML 1.24.4）：真实切片 744 对与教师判决 100% 一致，
+  0.61 ms/对（批量 64）
+- Intel 使用同一套 DirectML 算子；模型仅含 Conv / Clip / Relu / Sigmoid / Gemm / ReduceMean
+  等标准算子，无厂商专有节点
+
+## 用法（Python / onnxruntime）
+
+```python
+import numpy as np, onnxruntime as ort
+sess = ort.InferenceSession("model.onnx", providers=["DmlExecutionProvider", "CPUExecutionProvider"])
+a = np.random.rand(1, 1, 128, 128).astype(np.float32)   # 灰度 /255
+b = a.copy()
+score = sess.run(["score"], {"frame_a": a, "frame_b": b})[0][0, 0]
+print(float(score))   # 1.0 = 重复
+```
+
+DirectML 需要 Windows 10 1903+ / Windows 11；旧系统自动退回 CPU 执行。
+
+## 数据来源与署名
+
+训练素材来自 Wikimedia Commons，均为可公开使用的授权（CC BY / CC BY-SA / CC0）。
+完整清单与作者信息见 `sources.json`（随模型一并发布）。
+
+## License
+
+模型权重与示例代码：CC BY-NC 4.0（署名 - 非商业性使用 4.0 国际）。
+允许个人/研究用途的复制、修改与再分发，需保留署名且不得用于商业目的。

config.json

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+{
+  "model_name": "Easy4K SFDP - Siamese NET AI",
+  "architecture": "siamese depthwise-separable CNN + difference/product fusion head",
+  "input": {
+    "names": [
+      "frame_a",
+      "frame_b"
+    ],
+    "shape": [
+      "batch",
+      1,
+      128,
+      128
+    ],
+    "dtype": "float32",
+    "range": [
+      0,
+      1
+    ],
+    "color": "grayscale"
+  },
+  "output": {
+    "name": "score",
+    "shape": [
+      "batch",
+      1
+    ],
+    "meaning": "1 = duplicate"
+  },
+  "opset": 17,
+  "params": 1402465,
+  "providers": [
+    "DmlExecutionProvider",
+    "CPUExecutionProvider"
+  ],
+  "recommended_threshold": 0.5,
+  "metrics": {
+    "val_mae": "0.00573",
+    "val_acc": "0.99435",
+    "pos_mae": "0.00476",
+    "neg_mae": "0.00613"
+  },
+  "license": "cc-by-nc-4.0"
+}

example_infer.py

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+"""Easy4K SFDP · Siamese NET AI 推理示例（DirectML / CPU 自动选择）。"""
+import sys
+import numpy as np
+import onnxruntime as ort
+
+
+def make_session(model_path="model.onnx"):
+    providers = ["DmlExecutionProvider", "CPUExecutionProvider"]
+    return ort.InferenceSession(model_path, providers=providers)
+
+
+def score(sess, gray_a, gray_b):
+    """gray_a / gray_b: uint8 或 float32 的 128x128 灰度块。返回 0~1。"""
+    def prep(x):
+        x = np.asarray(x, dtype=np.float32)
+        if x.max() > 1.5:
+            x = x / 255.0
+        return x.reshape(1, 1, 128, 128)
+    return float(sess.run(["score"], {"frame_a": prep(gray_a), "frame_b": prep(gray_b)})[0][0, 0])
+
+
+if __name__ == "__main__":
+    s = make_session(sys.argv[1] if len(sys.argv) > 1 else "model.onnx")
+    a = np.zeros((128, 128), np.uint8)
+    print("same  ->", score(s, a, a))
+    print("noise ->", score(s, a, np.random.randint(0, 255, (128, 128), np.uint8)))

model.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:749d1705c43610787f1df3b738e0fcaf37d85c30bc09cb987644b1cd88aac537
+size 5624038

requirements.txt

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+onnxruntime-directml>=1.18
+numpy>=1.26

sources.json

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+    "artist": "Freddy Mack",
+    "page": "https://commons.wikimedia.org/wiki/File:191_Street_tunnel_video_vc.webm",
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