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@ -26,7 +26,7 @@ More details: http://pjreddie.com/darknet/yolo/
* [Using cmake](#how-to-compile-on-linux-using-cmake) * [Using cmake](#how-to-compile-on-linux-using-cmake)
* [Using make](#how-to-compile-on-linux-using-make) * [Using make](#how-to-compile-on-linux-using-make)
3. How to compile on Windows 3. How to compile on Windows
* [Using CMake-GUI](#how-to-compile-on-windows-using-cmake-gui) * [Using CMake-GUI](#how-to-compile-on-windows-using-cmake)
* [Using vcpkg](#how-to-compile-on-windows-using-vcpkg) * [Using vcpkg](#how-to-compile-on-windows-using-vcpkg)
* [Legacy way](#how-to-compile-on-windows-legacy-way) * [Legacy way](#how-to-compile-on-windows-legacy-way)
4. [Training and Evaluation of speed and accuracy on MS COCO](https://github.com/AlexeyAB/darknet/wiki#training-and-evaluation-of-speed-and-accuracy-on-ms-coco) 4. [Training and Evaluation of speed and accuracy on MS COCO](https://github.com/AlexeyAB/darknet/wiki#training-and-evaluation-of-speed-and-accuracy-on-ms-coco)
@ -140,11 +140,14 @@ You can get cfg-files by path: `darknet/cfg/`
#### Yolo v3 in other frameworks #### Yolo v3 in other frameworks
* **TensorFlow:** convert `yolov3.weights`/`cfg` files to `yolov3.ckpt`/`pb/meta`: by using [mystic123](https://github.com/mystic123/tensorflow-yolo-v3) or [jinyu121](https://github.com/jinyu121/DW2TF) projects, and [TensorFlow-lite](https://www.tensorflow.org/lite/guide/get_started#2_convert_the_model_format)
* **Intel OpenVINO 2019 R1:** (Myriad X / USB Neural Compute Stick / Arria FPGA): read this [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) * **TensorFlow:** YOLOv4 on TensorFlow 2.0 / TFlite / Andriod: https://github.com/hunglc007/tensorflow-yolov4-tflite
For YOLOv3 - convert `yolov3.weights`/`cfg` files to `yolov3.ckpt`/`pb/meta`: by using [mystic123](https://github.com/mystic123/tensorflow-yolo-v3) project, and [TensorFlow-lite](https://www.tensorflow.org/lite/guide/get_started#2_convert_the_model_format)
* **OpenCV-dnn** the fastest implementation for CPU (x86/ARM-Android), OpenCV can be compiled with [OpenVINO-backend](https://github.com/opencv/opencv/wiki/Intel's-Deep-Learning-Inference-Engine-backend) for running on (Myriad X / USB Neural Compute Stick / Arria FPGA), use `yolov3.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221) or [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150) * **OpenCV-dnn** the fastest implementation for CPU (x86/ARM-Android), OpenCV can be compiled with [OpenVINO-backend](https://github.com/opencv/opencv/wiki/Intel's-Deep-Learning-Inference-Engine-backend) for running on (Myriad X / USB Neural Compute Stick / Arria FPGA), use `yolov3.weights`/`cfg` with: [C++ example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.cpp#L192-L221) or [Python example](https://github.com/opencv/opencv/blob/8c25a8eb7b10fb50cda323ee6bec68aa1a9ce43c/samples/dnn/object_detection.py#L129-L150)
* **Intel OpenVINO 2019 R1:** (Myriad X / USB Neural Compute Stick / Arria FPGA): read this [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model)
* **PyTorch > ONNX > CoreML > iOS** how to convert cfg/weights-files to pt-file: [ultralytics/yolov3](https://github.com/ultralytics/yolov3#darknet-conversion) and [iOS App](https://itunes.apple.com/app/id1452689527) * **PyTorch > ONNX > CoreML > iOS** how to convert cfg/weights-files to pt-file: [ultralytics/yolov3](https://github.com/ultralytics/yolov3#darknet-conversion) and [iOS App](https://itunes.apple.com/app/id1452689527)
* **TensorRT** for YOLOv3 (-70% faster inference): [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) read [PDF](https://docs.nvidia.com/metropolis/deepstream/Custom_YOLO_Model_in_the_DeepStream_YOLO_App.pdf). [wang-xinyu/tensorrtx](https://github.com/wang-xinyu/tensorrtx) implemented yolov3-spp, yolov4, etc. * **TensorRT** YOLOv4 on TensorRT+tkDNN: https://github.com/ceccocats/tkDNN
For YOLOv3 (-70% faster inference): [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) read [PDF](https://docs.nvidia.com/metropolis/deepstream/Custom_YOLO_Model_in_the_DeepStream_YOLO_App.pdf). [wang-xinyu/tensorrtx](https://github.com/wang-xinyu/tensorrtx) implemented yolov3-spp, yolov4, etc.
* **TVM** - compilation of deep learning models (Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet) into minimum deployable modules on diverse hardware backends (CPUs, GPUs, FPGA, and specialized accelerators): https://tvm.ai/about * **TVM** - compilation of deep learning models (Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet) into minimum deployable modules on diverse hardware backends (CPUs, GPUs, FPGA, and specialized accelerators): https://tvm.ai/about
* **OpenDataCam** - It detects, tracks and counts moving objects by using Yolo: https://github.com/opendatacam/opendatacam#-hardware-pre-requisite * **OpenDataCam** - It detects, tracks and counts moving objects by using Yolo: https://github.com/opendatacam/opendatacam#-hardware-pre-requisite
* **Netron** - Visualizer for neural networks: https://github.com/lutzroeder/netron * **Netron** - Visualizer for neural networks: https://github.com/lutzroeder/netron
@ -251,7 +254,7 @@ Open a bash terminal inside the cloned repository and launch:
### How to compile on Linux (using `make`) ### How to compile on Linux (using `make`)
Just do `make` in the darknet directory. Just do `make` in the darknet directory. (You can try to compile and run it on Google Colab in cloud [link](https://colab.research.google.com/drive/12QusaaRj_lUwCGDvQNfICpa7kA7_a2dE) (press «Open in Playground» button at the top-left corner) and watch the video [link](https://www.youtube.com/watch?v=mKAEGSxwOAY) )
Before make, you can set such options in the `Makefile`: [link](https://github.com/AlexeyAB/darknet/blob/9c1b9a2cf6363546c152251be578a21f3c3caec6/Makefile#L1) Before make, you can set such options in the `Makefile`: [link](https://github.com/AlexeyAB/darknet/blob/9c1b9a2cf6363546c152251be578a21f3c3caec6/Makefile#L1)
* `GPU=1` to build with CUDA to accelerate by using GPU (CUDA should be in `/usr/local/cuda`) * `GPU=1` to build with CUDA to accelerate by using GPU (CUDA should be in `/usr/local/cuda`)
@ -270,8 +273,8 @@ To run Darknet on Linux use examples from this article, just use `./darknet` ins
### How to compile on Windows (using `CMake`) ### How to compile on Windows (using `CMake`)
This is the recommended approach to build Darknet on Windows if you have already This is the recommended approach to build Darknet on Windows if you have already
installed Visual Studio 2015/2017/2019, CUDA > 10.0, cuDNN > 7.0, and installed Visual Studio 2015/2017/2019, CUDA >= 10.0, cuDNN >= 7.0, and
OpenCV > 2.4. OpenCV >= 2.4.
Open a Powershell terminal inside the cloned repository and launch: Open a Powershell terminal inside the cloned repository and launch:

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