TensorRT/YoloV4

This page will provide some FAQs about using the TensorRT to do inference for the YoloV4 model, which can be helpful if you encounter similar problems.

1. How to convert YoloV4 DarkNet model into ONNX

 * Step1: Download pretrained YOLOv4 model
 * Model definition can be downloaded from here
 * https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg
 * Pretrained weights can be downloaded from here
 * https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights
 * Step2: Open file yolov4.cfg and set values of hight and width at header part of the cfg file
 * The following are input hight and width options and corresponding output sizes
 * {| class="wikitable"

!    !! Input size !! Output 1 !! Output 2 !! Output 3
 * Size Option 1 || 3x608x608 || 255x76x76 || 255x38x38 || 255x19x19
 * Size Option 2 || 3x512x512 || 255x64x64 || 255x32x32 || 255x16x16
 * Size Option 3 || 3x416x416 || 255x52x52 || 255x26x26 || 255x13x13
 * Size Option 4 || 3x320x320 || 255x40x40 || 255x20x20 || 255x10x10
 * }
 * Step3: Clone from https://github.com/Tianxiaomo/pytorch-YOLOv4.git that can help you convert YOLOv4 from DarkNet to ONNX
 * Step4: Follow README.md of this repository to convert DarkNet into ONNX
 * Step5: Transform ONNX model into TensorRT model
 * Generate TensorRT engine in fp16 mode:
 * Generate TensorRT engine in int8 mode:
 * Step4: Follow README.md of this repository to convert DarkNet into ONNX
 * Step5: Transform ONNX model into TensorRT model
 * Generate TensorRT engine in fp16 mode:
 * Generate TensorRT engine in int8 mode:
 * Generate TensorRT engine in int8 mode:
 * Generate TensorRT engine in int8 mode: