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README.md

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@@ -22,7 +22,7 @@ This repository implements two variants of the complex YOLO v4 object detectors:
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- **complex-yolov4-pandaset**: Standard complex yolov4 network for accurate object detection.
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- **tiny-complex-yolov4-pandaset**: Lightweight complex yolov4 network for faster inference.
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The pretrained networks are trained on three different object categories Car, Truck and Pedestrain. These networks are trained on the Pandaset dataset, available at https://scale.com/open-datasets/pandaset.
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The pretrained networks are trained on three different object categories Car, Truck and Pedestrain. These networks are trained on the Pandaset dataset, available at https://pandaset.org/.
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For other variant of Lidar object detection network, refer [Lidar 3-D Object Detection Using PointPillars Deep Learning](https://www.mathworks.com/help/lidar/ug/object-detection-using-pointpillars-network.html).
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@@ -147,3 +147,4 @@ References
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[2] Simon, Martin, et al. “Complex-YOLO: Real-Time 3D Object Detection on Point Clouds.” ArXiv:1803.06199 [Cs], Sept. 2018. arXiv.org, http://arxiv.org/abs/1803.06199.
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Copyright 2021 The MathWorks, Inc.
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