• August 30, 2022: We've released the inference code / trained model and published web demo, just enjoy it !

• August 24, 2022: We've released the tech report for YOLOPv2. This work is still in progress and code/models are coming soon. Please stay tuned! ☕️

😁We present an excellent multi-task network based on YOLOP💙,which is called YOLOPv2: Better, Faster, Stronger for Panoptic driving Perception. The advantages of YOLOPv2 can be summaried as below:

• Better👏: we proposed the end-to-end perception network which possess better feature extraction backbone, better bag-of-freebies were developed for dealing with the training process.
• Faster✈️: we employed more efficient ELAN structures to achieve reasonable memory allocation for our model.
• Stronger💪: the proposed model has stable network design and has powerful robustness for adapting to various scenarios .

We used the BDD100K as our datasets,and experiments are run on NVIDIA TESLA V100.

• Integrated into Huggingface Spaces 🤗 using Gradio. Try out the Web Demo !

model : trained on the BDD100k dataset and test on T3CAIC camera.

Result	Visualization
Model mAP@0.5 (%) Recall (%) MultiNet 60.2 81.3 DLT-Net 68.4 89.4 Faster R-CNN 55.6 77.2 YOLOv5s 77.2 86.8 YOLOP 76.5 89.2 HybridNets 77.3 92.8 YOLOPv2 83.4(+6.1)⏫ 91.1(-1.7) ⬇️

Result	Visualization
Model Drivable mIoU (%) ——:relaxed:—— MultiNet 71.6 DLT-Net 71.3 PSPNet 89.6 YOLOP 91.5 HybridNets 90.5 YOLOPv2 93.2(+1.7) ⬆️

Result	Visualization
Model Accuracy (%) Lane Line IoU (%) Enet 34.12 14.64 SCNN 35.79 15.84 Enet-SAD 36.56 16.02 YOLOP 70.5 26.2 HybridNets 85.4 31.6 YOLOPv2 87.3(+1.9)⬆️ 27.2(-4.4) ⬇️

You can get the model from here.

We provide two testing method.You can store the image or video.

python demo.py  --source data/example.jpg

• YOLOPv2 NCNN C++ Demo: YOLOPv2-ncnn from FeiGeChuanShu
• YOLOPv2 ONNX and OpenCV DNN Demo: yolopv2-opencv-onnxrun-cpp-py from hpc203

YOLOPv2 is released under the MIT Licence.