- AlexNet http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
- VGG https://arxiv.org/pdf/1409.1556.pdf
- GoogLeNet https://arxiv.org/pdf/1409.4842.pdf
- ResNet https://arxiv.org/pdf/1512.03385.pdf
- MobileNet(v1) https://arxiv.org/pdf/1704.04861.pdf
- MobileNet(v2) https://arxiv.org/pdf/1801.04381.pdf
- MobileNet(v3) https://arxiv.org/pdf/1905.02244.pdf
- ShuffleNet(v1) https://arxiv.org/pdf/1707.01083.pdf
- ShuffleNet(v2) https://arxiv.org/pdf/1807.11164.pdf
- R-CNN https://arxiv.org/pdf/1311.2524.pdf
- Fast R-CNN https://arxiv.org/pdf/1504.08083.pdf
- Faster R-CNN https://arxiv.org/pdf/1506.01497.pdf
- Mask R-CNN https://arxiv.org/pdf/1703.06870.pdf
- SSD https://arxiv.org/pdf/1512.02325.pdf
- FPN(Feature Pyramid Networks for Object Detection) https://arxiv.org/pdf/1612.03144.pdf
- RetinaNet(Focal Loss for Dense Object Detection) https://arxiv.org/pdf/1708.02002.pdf
- YOLOv1 https://arxiv.org/pdf/1506.02640.pdf
- YOLOv2 https://arxiv.org/pdf/1612.08242.pdf
- YOLOv3 https://arxiv.org/pdf/1804.02767.pdf
- YOLOv4 https://arxiv.org/pdf/2004.10934.pdf
- Microsoft COCO: Common Objects in Context https://arxiv.org/pdf/1405.0312.pdf