196 lines
5.0 KiB
Markdown
196 lines
5.0 KiB
Markdown
# SCRFD 500M 640 模型完整规格
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## 基本信息
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| 属性 | 值 |
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|------|-----|
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| 模型名称 | SCRFD 500M 640 |
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| 版本 | 1.4.1b16-dad86923 |
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| 编译版本 | 1.4.1b13 (a06f28157@2022-11-26T05:23:21) |
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| 目标平台 | RK3588 (RKNPU v2) |
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| 框架 | ONNX |
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| 输入尺寸 | 640x640x3 |
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| 输入格式 | NHWC (RGB/BGR) |
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## 输入规格
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```python
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shape: (1, 640, 640, 3) # NHWC
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format: UINT8 (pass_through=0, RKNN内部转INT8)
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type: static_shape (非动态)
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```
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## 输出规格 (9个张量)
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### 1. Scores (3个尺度)
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| 索引 | 名称 | 形状 | 说明 |
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|------|------|------|------|
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| 0 | score_8 | (1, 12800, 1, 1) | stride=8, 80x80x2 anchors |
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| 1 | score_16 | (1, 3200, 1, 1) | stride=16, 40x40x2 anchors |
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| 2 | score_32 | (1, 800, 1, 1) | stride=32, 20x20x2 anchors |
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**关键发现**: 每个anchor只输出1个前景分数，不是2个通道！
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### 2. Bounding Boxes (3个尺度)
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| 索引 | 名称 | 形状 | 说明 |
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|------|------|------|------|
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| 3 | bbox_8 | (1, 12800, 4, 1) | [dx, dy, dw, dh] x 12800 |
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| 4 | bbox_16 | (1, 3200, 4, 1) | [dx, dy, dw, dh] x 3200 |
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| 5 | bbox_32 | (1, 800, 4, 1) | [dx, dy, dw, dh] x 800 |
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**解码公式**:
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```
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cx = anchor_cx + dx * stride
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cy = anchor_cy + dy * stride
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w = exp(dw) * stride
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h = exp(dh) * stride
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x1 = cx - w/2
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y1 = cy - h/2
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x2 = cx + w/2
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y2 = cy + h/2
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```
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### 3. Keypoints (3个尺度)
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| 索引 | 名称 | 形状 | 说明 |
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|------|------|------|------|
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| 6 | kps_8 | (1, 12800, 10, 1) | 5点x2坐标 x 12800 |
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| 7 | kps_16 | (1, 3200, 10, 1) | 5点x2坐标 x 3200 |
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| 8 | kps_32 | (1, 800, 10, 1) | 5点x2坐标 x 800 |
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**解码公式**:
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```
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kps_x = anchor_cx + kps_dx * stride
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kps_y = anchor_cy + kps_dy * stride
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```
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## 锚点 (Anchors) 生成
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```python
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strides = [8, 16, 32]
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num_anchors_per_location = 2
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total_anchors = 80*80*2 + 40*40*2 + 20*20*2 = 16800
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# anchor生成逻辑
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for stride in [8, 16, 32]:
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grid_size = 640 // stride
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for y in range(grid_size):
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for x in range(grid_size):
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for a in range(2): # 2 anchors per location
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anchor_cx = (x + 0.5) * stride
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anchor_cy = (y + 0.5) * stride
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```
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**Anchor分布**:
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- stride=8: 80x80 grid, 12800 anchors
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- stride=16: 40x40 grid, 3200 anchors
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- stride=32: 20x20 grid, 800 anchors
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## 量化参数
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| 张量 | 类型 | Zero Point | Scale |
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|------|------|------------|-------|
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| input | INT8 | -128 | 0.003921 |
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| score_8 | INT8 | -128 | 0.003534 |
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| score_16 | INT8 | -128 | 0.001638 |
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| score_32 | INT8 | -128 | 0.000163 |
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| bbox_8 | INT8 | -128 | 0.016671 |
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| bbox_16 | INT8 | -128 | 0.016227 |
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| bbox_32 | INT8 | -128 | 0.017979 |
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| kps_8 | INT8 | -7 | 0.020261 |
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| kps_16 | INT8 | -20 | 0.014725 |
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| kps_32 | INT8 | -11 | 0.014612 |
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**注意**: 实际推理时使用 `want_float=1`，RKNN 自动反量化，输出已是 FP32。
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## 后处理流程
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1. **Score筛选**: 对每个anchor，检查 `score > conf_thresh`
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2. **BBox解码**: anchor中心 + 相对偏移 + exp解码宽高
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3. **Kps解码**: 5个面部关键点，相对anchor中心
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4. **坐标映射**: 从640x640映射回原图分辨率
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5. **NMS**: IoU阈值去重
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## 配置参数
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```json
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{
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"model_path": "./models/scrfd_500m_640.rknn",
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"conf_thresh": 0.5, // 置信度阈值 (0-1)
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"nms_thresh": 0.4, // NMS IoU阈值
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"max_faces": 10, // 最大检测人脸数
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"output_landmarks": true, // 输出5个关键点
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"input_format": "rgb" // 输入格式: rgb/bgr
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}
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```
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## 性能指标
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| 指标 | 值 |
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|------|-----|
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| 模型大小 | ~1.5 MB |
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| 输入分辨率 | 640x640 |
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| Anchor数量 | 16800 |
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| 输出张量数 | 9 |
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| 关键点 | 5点 (双眼、鼻尖、嘴角) |
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| 总参数量 | ~500K |
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## 版本兼容性
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| 版本 | 状态 | 说明 |
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|------|------|------|
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| 1.4.1b16 | ✅ 推荐 | 静态形状，C API完美支持 |
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| 2.3.2 | ❌ 不兼容 | 动态形状，C API崩溃 |
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**运行时库要求**:
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- 最低: librknnrt 1.5.2
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- 推荐: librknnrt 2.3.2
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## 调试命令
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```bash
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# 检查模型版本
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strings models/scrfd_500m_640.rknn | grep "compiler version"
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# 检查运行时版本
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strings /usr/lib/librknnrt.so | grep "librknnrt version"
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# Python验证模型
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python3 << 'PYEOF'
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from rknnlite.api import RKNNLite
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import numpy as np
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rknn = RKNNLite()
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rknn.load_rknn("models/scrfd_500m_640.rknn")
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rknn.init_runtime()
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img = np.zeros((1, 640, 640, 3), dtype=np.uint8)
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outputs = rknn.inference(inputs=[img])
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for i, out in enumerate(outputs):
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print(f"output[{i}]: shape={out.shape}")
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rknn.release()
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PYEOF
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```
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## 常见问题
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### 1. 坐标错误
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**原因**: Score张量误解为2通道，实际只有1通道
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**修复**: `score = scores[i]` 而不是 `scores[i*2+1]`
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### 2. 模型崩溃
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**原因**: 使用动态形状版本 (v2.3.2)
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**修复**: 切换到静态形状版本 (v1.4.1b16)
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### 3. RGA任务过多
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**原因**: 检测到太多人脸，OSD绘制任务堆积
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**修复**: 降低 `max_faces` 或提高 `conf_thresh`
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## 参考
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- [SCRFD Paper](https://arxiv.org/abs/2105.04714)
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- [InsightFace SCRFD](https://github.com/deepinsight/insightface/tree/master/detection/scrfd)
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- [RKNN API文档](https://github.com/rockchip-linux/rknpu2)
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