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creat env for YOLO3D

conda create -n Yolo3D python=3.8 numpy
conda activate Yolo3D
pip install -r requirements.txt

datasets

train data

cd yolo3d-lighting
ln -s /your/KITTI/path data/KITTI
├── data
│   └── KITTI
│       ├── calib
│       ├── images_2
│       └── labels_2

Demo

![demo](./docs/assets/show.png)

train

python src/train.py experiment=sample

log path: /logs
model path: /weights

covert

modify convert.yaml file to trans .ckpt to .pt model

python covert.py

inference

In order to show the real model infer ability, we crop image according to gt 2d box as yolo3d input, you can use following command to plot 3d result.

modify inference.yaml file to change configs

python inference.py \
          source_dir=./data/KITTI \
          detector.classes=6 \
          regressor_weights=./weights/pytorch-kitti.pt \
          export_onnx=False \
          func=image
  • source_dir: path os datasets, include /image_2 and /label_2 folder
  • detector.classes: kitti class
  • regressor_weights: your model
  • export_onnx: export onnx model for apollo

result path: /outputs

evaluate

generate label for 3d result:

python inference.py \
          source_dir=./data/KITTI \
          detector.classes=6 \
          regressor_weights=./weights/pytorch-kitti.pt \
          export_onnx=False \
          func=label

result path: /data/KITTI/result

├── data
│   └── KITTI
│       ├── calib
│       ├── images_2
│       ├── labels_2
│       └── result

use kitti evaluate tool to calculate mAP:

python evaluate.py \
          gt_dir=./data/KITTI/label_2 \
          pred_dir=./data/KITTI/result
  • gt_dir: gt labels folder
  • pred_dir: model output result labels

python evaluate.py
detector.model_path=./weights/detector_yolov5s.pt
regressor_weights=./weights/regressor_resnet18.pt

case:

case1: AttributeError: ‘Upsample‘ object has no attribute ‘recompute_scale_factor‘ [https://blog.csdn.net/Thebest_jack/article/details/124723687]

case2: cv2.error: Caught error in DataLoader worker process 1 [ultralytics/yolov3#1721]