From 4ee3be7e68fb9c7eda5cc390e47e59f01e40dded Mon Sep 17 00:00:00 2001
From: Alexey <AlexeyAB@users.noreply.github.com>
Date: Mon, 7 Dec 2020 17:27:02 +0300
Subject: [PATCH] Update README.md

---
 README.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 7fe5e228..062f1c2e 100644
--- a/README.md
+++ b/README.md
@@ -196,7 +196,8 @@ You can get cfg-files by path: `darknet/cfg/`
 
 #### Yolo v4 in other frameworks
 
-* **TensorFlow:** YOLOv4 on TensorFlow 2.0 / TFlite / Andriod: https://github.com/hunglc007/tensorflow-yolov4-tflite
+* **Pytorch - Scaled-YOLOv4:** https://github.com/WongKinYiu/ScaledYOLOv4
+* **TensorFlow:** `pip install yolov4` 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 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 2020 R4:** (NPU Myriad X / USB Neural Compute Stick / Arria FPGA): 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) )
@@ -205,6 +206,7 @@ You can get cfg-files by path: `darknet/cfg/`
     * [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). [wang-xinyu/tensorrtx](https://github.com/wang-xinyu/tensorrtx) implemented yolov3-spp, yolov4, etc.