From d65909fbea471d06e52a2e4a41132380dc2edaa6 Mon Sep 17 00:00:00 2001 From: Alexey Date: Sun, 11 Oct 2020 15:54:55 +0300 Subject: [PATCH] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 6fe35295..f754cd88 100644 --- a/README.md +++ b/README.md @@ -190,6 +190,8 @@ You can get cfg-files by path: `darknet/cfg/` * [Tianxiaomo/pytorch-YOLOv4](https://github.com/Tianxiaomo/pytorch-YOLOv4) * **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. +* **Deepstream 5.0 / TensorRT for YOLOv4** https://github.com/NVIDIA-AI-IOT/yolov4_deepstream +* **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 backends (CPUs, GPUs, FPGA, and specialized accelerators): https://tvm.ai/about * **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