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@ -211,22 +211,25 @@ You can get cfg-files by path: `darknet/cfg/`
* **TensorFlow:** `pip install yolov4` YOLOv4 on TensorFlow 2.0 / TFlite / Android: https://github.com/hunglc007/tensorflow-yolov4-tflite
Official TF models: https://github.com/tensorflow/models/tree/master/official/vision/beta/projects/yolo
For YOLOv4 - convert `yolov4.weights`/`cfg` files to `yolov4.pb` by using [TNTWEN](https://github.com/TNTWEN/OpenVINO-YOLOV4) project, and to `yolov4.tflite` [TensorFlow-lite](https://www.tensorflow.org/lite/guide/get_started#2_convert_the_model_format)
* **OpenCV-dnn** the fastest implementation of YOLOv4 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 `yolov4.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 2021.2:** supports YOLOv4 (NPU Myriad X / USB Neural Compute Stick / Arria FPGA): https://devmesh.intel.com/projects/openvino-yolov4-49c756 read this [manual](https://github.com/TNTWEN/OpenVINO-YOLOV4) (old [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) )
* **Tencent/ncnn:** the fastest inference of YOLOv4 on mobile phone CPU: https://github.com/Tencent/ncnn
* **OpenCV** the fastest implementation of YOLOv4 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 `yolov4.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 2021.2:** supports YOLOv4 (NPU Myriad X / USB Neural Compute Stick / Arria FPGA): https://devmesh.intel.com/projects/openvino-yolov4-49c756 read this [manual](https://github.com/TNTWEN/OpenVINO-YOLOV4) (old [manual](https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow#converting-a-darknet-yolo-model) ) (for [Scaled-YOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4/tree/yolov4-large) models use https://github.com/Chen-MingChang/pytorch_YOLO_OpenVINO_demo )
* **PyTorch > ONNX**:
* [WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4)
* [maudzung/3D-YOLOv4](https://github.com/maudzung/Complex-YOLOv4-Pytorch)
* [Tianxiaomo/pytorch-YOLOv4](https://github.com/Tianxiaomo/pytorch-YOLOv4)
* [YOLOv5](https://github.com/ultralytics/yolov5)
* **ONNX** on Jetson for YOLOv4: https://developer.nvidia.com/blog/announcing-onnx-runtime-for-jetson/
* **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). [jkjung-avt/tensorrt_demos](https://github.com/jkjung-avt/tensorrt_demos) or [wang-xinyu/tensorrtx](https://github.com/wang-xinyu/tensorrtx) implemented yolov3-spp, yolov4, etc.
* **Deepstream 5.0 / TensorRT for YOLOv4** https://github.com/NVIDIA-AI-IOT/yolov4_deepstream or https://github.com/marcoslucianops/DeepStream-Yolo
* **TensorRT+tkDNN**: https://github.com/ceccocats/tkDNN#fps-results
* **Deepstream 5.0 / TensorRT for YOLOv4** https://github.com/NVIDIA-AI-IOT/yolov4_deepstream or https://github.com/marcoslucianops/DeepStream-Yolo read [Yolo is natively supported in DeepStream 4.0](https://news.developer.nvidia.com/deepstream-sdk-4-now-available/) and [PDF](https://docs.nvidia.com/metropolis/deepstream/Custom_YOLO_Model_in_the_DeepStream_YOLO_App.pdf). Additionally [jkjung-avt/tensorrt_demos](https://github.com/jkjung-avt/tensorrt_demos) or [wang-xinyu/tensorrtx](https://github.com/wang-xinyu/tensorrtx)
* **Triton Inference Server / TensorRT** https://github.com/isarsoft/yolov4-triton-tensorrt
* **DirectML** https://github.com/microsoft/DirectML/tree/master/Samples/yolov4
* **OpenCL** (Intel, AMD, Mali GPUs for macOS & GNU/Linux) https://github.com/sowson/darknet
* **HIP** for Training and Detection on AMD GPU https://github.com/os-hackathon/darknet
* **ROS** (Robot Operating System) https://github.com/engcang/ros-yolo-sort
* **Xilinx Zynq Ultrascale+ Deep Learning Processor (DPU) ZCU102/ZCU104:** https://github.com/Xilinx/Vitis-In-Depth-Tutorial/tree/master/Machine_Learning/Design_Tutorials/07-yolov4-tutorial
* **Amazon Neurochip / Amazon EC2 Inf1 instances** 1.85 times higher throughput and 37% lower cost per image for TensorFlow based YOLOv4 model, using Keras [URL](https://aws.amazon.com/ru/blogs/machine-learning/improving-performance-for-deep-learning-based-object-detection-with-an-aws-neuron-compiled-yolov4-model-on-aws-inferentia/)
* **TVM** - compilation of deep learning models (Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet) into minimum deployable modules on diverse hardware backend (CPUs, GPUs, FPGA, and specialized accelerators): https://tvm.ai/about
* **Tencent/ncnn:** the fastest inference of YOLOv4 on mobile phone CPU: https://github.com/Tencent/ncnn
* **OpenDataCam** - It detects, tracks and counts moving objects by using YOLOv4: https://github.com/opendatacam/opendatacam#-hardware-pre-requisite
* **Netron** - Visualizer for neural networks: https://github.com/lutzroeder/netron