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README.md

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@@ -40,6 +40,8 @@ ncnn 是一个为手机端极致优化的高性能神经网络前向计算框架
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* [Build for iOS on Linux with cctools-port](https://github.com/Tencent/ncnn/wiki/how-to-build#build-for-ios-on-linux-with-cctools-port)
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* [Build for Hisilicon platform with cross-compiling](https://github.com/Tencent/ncnn/wiki/how-to-build#build-for-hisilicon-platform-with-cross-compiling)
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**[download prebuild binary package for android and ios](https://github.com/Tencent/ncnn/releases)**
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**[how to use ncnn with alexnet](https://github.com/Tencent/ncnn/wiki/how-to-use-ncnn-with-alexnet) with detailed steps, recommended for beginners :)**
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**[ncnn 组件使用指北 alexnet](https://github.com/Tencent/ncnn/wiki/ncnn-%E7%BB%84%E4%BB%B6%E4%BD%BF%E7%94%A8%E6%8C%87%E5%8C%97-alexnet) 附带详细步骤,新人强烈推荐 :)**
@@ -60,6 +62,8 @@ ncnn 是一个为手机端极致优化的高性能神经网络前向计算框架
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**[ncnn produce wrong result](https://github.com/Tencent/ncnn/wiki/FAQ-ncnn-produce-wrong-result)**
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**[ncnn vulkan](https://github.com/Tencent/ncnn/wiki/FAQ-ncnn-vulkan)**
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### Features
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* ARM NEON assembly level of careful optimization, calculation speed is extremely high
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* Sophisticated memory management and data structure design, very low memory footprint
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* Supports multi-core parallel computing acceleration, ARM big.LITTLE cpu scheduling optimization
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* The overall library size is less than 500K, and can be easily reduced to less than 300K
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* Supports GPU acceleration via the next-generation low-overhead vulkan api
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* The overall library size is less than 700K, and can be easily reduced to less than 300K
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* Extensible model design, supports 8bit quantization and half-precision floating point storage, can import caffe/pytorch/mxnet/onnx models
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* Support direct memory zero copy reference load network model
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* Can be registered with custom layer implementation and extended
@@ -84,12 +89,36 @@ ncnn 是一个为手机端极致优化的高性能神经网络前向计算框架
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* ARM NEON 汇编级良心优化,计算速度极快
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* 精细的内存管理和数据结构设计,内存占用极低
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* 支持多核并行计算加速,ARM big.LITTLE cpu 调度优化
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* 整体库体积小于 500K,并可轻松精简到小于 300K
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* 支持基于全新低消耗的 vulkan api GPU 加速
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* 整体库体积小于 700K,并可轻松精简到小于 300K
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* 可扩展的模型设计,支持 8bit 量化和半精度浮点存储,可导入 caffe/pytorch/mxnet/onnx 模型
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* 支持直接内存零拷贝引用加载网络模型
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* 可注册自定义层实现并扩展
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* 恩,很强就是了,不怕被塞卷 QvQ
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---
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### supported platform matrix
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* YY = known work and runs fast with good optimization
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* Y = known work, but speed may not be fast enough
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* ? = shall work, not confirmed
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* / = not applied
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| |Windows|Linux|Android|MacOS|iOS|
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|---|---|---|---|---|---|
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|intel-cpu|Y|Y|?|Y|/|
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|intel-gpu|Y|Y|?|?|/|
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|amd-cpu|Y|Y|?|Y|/|
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|amd-gpu|Y|Y|?|?|/|
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|nvidia-gpu|Y|Y|?|?|/|
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|qcom-cpu|?|Y|YY|/|/|
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|qcom-gpu|?|Y|Y|/|/|
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|arm-cpu|?|?|YY|/|/|
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|arm-gpu|?|?|Y|/|/|
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|apple-cpu|/|/|/|/|YY|
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|apple-gpu|/|/|/|/|Y|
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### Example project
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* https://github.com/chehongshu/ncnnforandroid_objectiondetection_Mobilenetssd
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* https://github.com/moli232777144/mtcnn_ncnn
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![](https://github.com/nihui/ncnn-assets/raw/master/20181217/ncnn-1.jpg)
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![](https://github.com/nihui/ncnn-assets/raw/master/20181217/ncnn-2.jpg)
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![](https://github.com/nihui/ncnn-assets/raw/master/20181217/ncnn-23.jpg)
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![](https://github.com/nihui/ncnn-assets/raw/master/20181217/ncnn-m.png)
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