YOLO For 3D Object Detection

Inference

python inference.py \
    --weights yolov5s.pt \
    --source eval/image_2 \
    --reg_weights weights/resnet_10.pkl \
    --model_list resnet \
    --output_path runs/detect/ \
    --show_result -- save_result

Training

python train.py \
    --epochs 10 \
    --batch_size 32 \
    --num_workers 2 \
    --save_epoch 5 \
    --train_path ./dataset/KITTI/training \
    --model_path ./weights \
    --select_model resnet18

parser = argparse.ArgumentParser(description='Regressor Model Training') parser.add_argument('--epochs', type=int, default=10, help='Number of epochs') parser.add_argument('--batch_size', type=int, default=32, help='Number of batch size') parser.add_argument('--alpha', type=float, default=0.6, help='Aplha default=0.6 DONT CHANGE') parser.add_argument('--w', type=float, default=0.4, help='w DONT CHANGE') parser.add_argument('--num_workers', type=int, default=2, help='Total # workers, for colab & kaggle use 2') parser.add_argument('--lr', type=float, default=0.0001, help='Learning rate') parser.add_argument('--save_epoch', type=int, default=10, help='Save model every # epochs') parser.add_argument('--train_path', type=str, default=ROOT / 'dataset/KITTI/training', help='Training path KITTI') parser.add_argument('--model_path', type=str, default=ROOT / 'weights', help='Weights path, for load and save model') parser.add_argument('--select_model', type=str, default='resnet18', help='Model selection: {resnet18, vgg11}')

Reference

• YOLOv5 by Ultralytics
• shakdem/3D-BoungingBox

@misc{mousavian20173d,
      title={3D Bounding Box Estimation Using Deep Learning and Geometry}, 
      author={Arsalan Mousavian and Dragomir Anguelov and John Flynn and Jana Kosecka},
      year={2017},
      eprint={1612.00496},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}