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 |<b>[YOLO v2-coco](vision/object_detection_segmentation/yolov2-coco)</b>|[Redmon et al.](https://arxiv.org/abs/1612.08242)|A CNN model for real-time object detection system that can detect over 9000 object categories. It uses a single network evaluation, enabling it to be more than 1000x faster than R-CNN and 100x faster than Faster R-CNN. This model is trained with COCO dataset and contains 80 classes.
 |<b>[YOLO v3](vision/object_detection_segmentation/yolov3)</b>|[Redmon et al.](https://arxiv.org/pdf/1804.02767.pdf)|A deep CNN model for real-time object detection that detects 80 different classes. A little bigger than YOLOv2 but still very fast. As accurate as SSD but 3 times faster.|
 |<b>[Tiny YOLOv3](vision/object_detection_segmentation/tiny-yolov3)</b>|[Redmon et al.](https://arxiv.org/pdf/1804.02767.pdf)| A smaller version of YOLOv3 model. |
-|<b>[YOLOv4](vision/object_detection_segmentation/yolov4)</b>|[Bochkovskiy et al.](https://arxiv.org/abs/2004.10934)|Optimizes the speed and accuracy of object detection. Two times faster than EfficientDet. It improves YOLOv3's AP and FPS by 10% and 12%, respectively, with mAP50 of 52.32 on the COCO 2017 dataset and FPS of 41.7 on Tesla 100.|
+|<b>[YOLOv4](vision/object_detection_segmentation/yolov4)</b>|[Bochkovskiy et al.](https://arxiv.org/abs/2004.10934)|Optimizes the speed and accuracy of object detection. Two times faster than EfficientDet. It improves YOLOv3's AP and FPS by 10% and 12%, respectively, with mAP50 of 52.32 on the COCO 2017 dataset and FPS of 41.7 on a Tesla V100.|
 |<b>[DUC](vision/object_detection_segmentation/duc)</b>|[Wang et al.](https://arxiv.org/abs/1702.08502)|Deep CNN based pixel-wise semantic segmentation model with >80% [mIOU](/models/semantic_segmentation/DUC/README.md/#metric) (mean Intersection Over Union). Trained on cityscapes dataset, which can be effectively implemented in self driving vehicle systems.|
 |FCN|[Long et al.](https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf)|Deep CNN based segmentation model trained end-to-end, pixel-to-pixel that produces efficient inference and learning. Built off of AlexNet, VGG net, GoogLeNet classification methods. <br>[contribute](contribute.md)|
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