YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud (ECCV 2018) - Matom-ai/YOLO3D-YOLOv4-PyTorch
在x86 ,ubuntu18.04(cpu)上,使用pytorch实现的yolov4训练自己的数据集,并进行推理。 二 环境准备 该样例依赖以下环境: numpy==1.18.2 tensorboardX==2.0 scikit_image==0.16.2 matplotlib==2.2.3 tqdm==4.43.0 easydict==1.9 Pillow==7.1.2 opencv_python pycocotools pytorch==1.4(注意不要直接下) onnx o...
YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud (ECCV 2018) - maudzung/YOLO3D-YOLOv4-PyTorch
python evaluate.py --gpu_idx 0 --pretrained_path ../checkpoints/complex_yolov3/complex_yolov3.pth --cfgfile ./config/complex_yolov3.cfg (Complex-YOLOv4 trained model will be provided soon. Please watch the repo to get notifications for next update.) The comparison of this implementation wi...
Ultralytics/yolov3_and_v4 WongKinYiu/PyTorch_YOLOv4 VCasecnikovs/Yet-Another-YOLOv4-Pytorch 2.4. How to run 2.4.1. Visualize the dataset (both BEV images from LiDAR and camera images) cdsrc/data_process To visualize BEV maps and camera images (with 3D boxes), let's execute(theoutput-...
Ultralytics/yolov3_and_v4 WongKinYiu/PyTorch_YOLOv4 VCasecnikovs/Yet-Another-YOLOv4-Pytorch 2.4. How to run 2.4.1. Visualize the dataset (both BEV images from LiDAR and camera images) cdsrc/data_process To visualize BEV maps and camera images (with 3D boxes), let's execute(theoutput-...