YOLOv8-3D is a LowCode, Simple 2D and 3D Bounding Box Object Detection and Tracking , Python 3.10
YOLOv8-3D is a lightweight and user-friendly library designed for efficient 2D and 3D bounding box object detection in Advanced Driver Assistance Systems (ADAS). With its intuitive API and comprehensive features, EasyADAS makes it straightforward to integrate object detection capabilities into your ADAS projects.
This API supports for easy understanding and integrate 3D perception, systems can make more informed decisions and operate effectively in complex, real-world environments.
augmentations for better training, automated backup training and results plot
conda create -n test1 python=3.10 -y
conda activate test1
pip install tensorflow
For more detailed tensorflow gpu installation instructions and options, refer to this documentation.
####### select model on train.py ########
# select_model = 'resnet50'
# select_model ='resnet101'
# select_model = 'resnet152'
# select_model = 'vgg11'
# select_model = 'vgg16'
# select_model = 'vgg19'
# select_model = 'efficientnetb0'
# select_model = 'efficientnetb5'
select_model = 'mobilenetv2'
###[INFO] set num of iterations to run (train.py) on (run_train.sh) file /// this automatically saves training info for every 20 epochs.
bash run_train.sh
recommended new environment to infer models only on cpu
conda create -n test2 python=3.10 -y
conda activate test2
pip install tensorflow ultralytics
python demo.py
set
## BEV_plot = True
## TracK = True
Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.