Yolo3D

perform 3D object detection base on Yolov5 and 3DDeepbox. To transform to SNPE, change partial layers of yolov5, such as change nn.Upsample to nn.ConvTranspose2d, and adjust Focus module in yolov5.

Quick Start

datasets

Applying kitt dataset. Place kitti_dev sub contents to datasets/data/kitti/ of this project.

Please place it as following:

root
|
---datasets
   |
   ---data
      |
      ---kitti
          ---cache
             |
             ---k_*.npy // list K of camera. * -> (train or test)
             |
             ---label_*.npy // list label. * -> (train or test) 
             |
             ---shape_*.npy // list size of images. * -> (train or test)
          |
          ---ImageSets
             |
             ---train.txt // list of training image.
             |
             ---test.txt // list of testing image.
          ---testing
          |
          ---training
             |
             ---calib
                |
                ---calib_cam_to_cam.txt // camera calibration file for kitti
             |
             ---image_2
             |
             ---label_2

training

python train.py --data ./datasets/configs/kitti.yaml --cfg models/configs/yolo3d_5m.yaml --weights ./weights/yolov5m.pt --batch-size 64 --epochs 2000 --is-rect --is-mosaic --multi-scale --resume

detect

python detect.py --weights ./weights/model3d_5m_best_transconv_11_25.pt --device cpu --is-rect

export onnx

python export_onnx.py --weights ./weights/model3d_5m_best_transconv_11_25.pt --img-size 224 640 --batch-size 1

Pretrained Model

We provide a set of trained models available for download in the Pretrained Model. 提取码: tpqg

License

MIT

Refenrence

yolov5. 3D-BoundingBox.