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README
Apache-2.0
cnet

CNET

CNET 是一个C99开发的的面向iot设备设计的深度学习推理库,实现深度学习算法在iot设备上的快速部署。

1 主要特点

  • 极简,高效的内存管理,清晰的架构设计
  • 扩展,模块化设计,轻松完成裁剪和扩展
  • 轻便,完整的二进制库不到300kb
  • 可靠,完善的内存监测测试, 无内存泄露
  • 完善,支持现有的大量操作,可按需扩展
  • 高效,纯C实现,逐步加入MCU汇编优化

架构简介:

----------------------------------------------------------------
 前      examples        推理结果校验/人脸检测及识别/车牌检测及识别

 处      c-netend        网络后处理模块:支持目标检测/人脸识别及车牌识别
 理      c-image         图像加载及保存,结果绘制,输入预处理
----------------------------------------------------------------
 神      c-network       神经网络组织及推理
 经      nn-case         模型转换工具
 网      cilibration     量化评估工具
 络      ncnn            模型优化/量化支撑/模型源
----------------------------------------------------------------
 基      c-utils         基础工具,包括容器,分配器,线程池等
 础      c-benchmark     性能评估,无外部依赖,可在MCU执行
 模      c-tester        单元测试支持,无外部依赖,可在MCU执行
 块      c-tensor        张量计算支持,基础操作及内存管理
----------------------------------------------------------------

2 支持情况

  • 作为NCNN的延展,将NCNN模型直接优化并量化为部署模型,故可导入Caffe/Onnx/Mxnet/darknet模型;
  • 全面量化支持,支持后量化训练, 支持INT16推理,后续将加入INT8推理;
  • 支持ESP32 等主流芯片,理论上完全兼容支持c99的所有模块;
  • 支持分类, 目标检测,人脸识别,车牌识别等主流算法;
  • 采用直接对象定义模型,无需手动加载,扁平化结构;

2.1支持的算子列表

    /// convolution
    convolution_operation_type,
    /// pooling
    pooling_operation_type,
    /// padding
    padding_operation_type,
    /// crop
    crop_operation_type,
    /// inner product
    inner_product_operation_type,
    /// activation
    activation_operation_type,
    /// batch norm
    batch_norm_operation_type,
    /// slice
    slice_operation_type,
    /// concat
    concat_operation_type,
    /// reshape
    reshape_operation_type,
    /// sigmoid
    sigmoid_operation_type,
    /// softmax
    softmax_operation_type,
    /// permute
    permute_operation_type,
    /// unary
    unary_operation_type,
    /// binary
    binary_operation_type,
    /// upsample
    upsample_operation_type,
    /// prelu
    prelu_operation_type,
    /// memory data
    memory_data_operation_type,
    /// shuffle_channel
    shuffle_channel_operation_type,

2.2 支持的激活函数

    /// bnll
    bnll_activate_type,
    /// clip
    clip_activate_type,
    /// elu
    elu_activate_type,
    /// exp
    exp_activate_type,
    /// hard sigmoid
    hard_sigmoid_activate_type,
    /// hard swish
    hard_swish_activate_type,
    /// log
    log_activate_type,
    /// power
    power_activate_type,
    /// relu
    relu_activate_type,
    /// selu
    selu_activate_type,
    /// hard swish
    swish_activate_type,
    /// threshold
    threshold_activate_type,

3 案例展示

车牌识别 人脸识别
ff ff
车牌检测: retian/yolo, INT16 人脸检测: retian/yolo, INT16
车牌矫正: 关键点检测 + 仿射变换 人脸矫正: 关键点+仿射变换
车牌识别: 车牌+颜色 支持INT16 人脸验证: CNN + VECTOR, mobilenet-face

4. 路线图

  • 支持ESP32,性能优化
  • 支持INT8
  • 支持ESP32系列INT8优化
  • 其它芯片优化
  • 操作优化支持
  • 后端模型优化支持

5. 参与贡献

  1. Fork 本仓库
  2. 新建 Feat_xxx 分支
  3. 提交代码
  4. 新建 Pull Request
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简介

CNET 是一个C99开发的的面向iot设备设计的深度学习推理库,实现深度学习算法在iot设备上的快速部署。 展开 收起
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