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Results of our implementations

Model Size #Param. FLOPs AP AP50 AP75
Pelee-YOLOv3-tiny [1][2] 320 3.91M 2.99B 21.2 41.4 19.9
Pelee-YOLOv3-tiny [2] 416 3.91M 5.06B 23.6 45.3 22.5
Pelee-FPN [3] 320 3.75M 2.86B 21.3 41.4 20.0
Pelee-FPN [3] 416 3.75M 4.84B 23.9 45.7 22.7
Pelee-PRN [4] 320 3.16M 2.39B 20.9 40.9 19.4
Pelee-PRN [4] 416 3.16M 4.04B 23.3 45.0 22.0
Pelee-PRN-3l [4] 320 3.36M 3.98B 21.2 42.5 19.8
Pelee-PRN-3l [4] 416 3.36M 5.03B 24.1 46.3 22.9
Pelee-PAN [5] 320 - 2.95B 21.4 41.6 20.2
Pelee-PAN [5] 416 - 4.99B 24.1 45.9 23.2
Pelee-PAN2 [5] 320 - 2.56B 21.4 41.6 20.2
Pelee-PAN2 [5] 416 - 4.33B 23.6 45.4 22.5
Pelee2-PRN (private) 320 3.81M 2.82B 22.2 42.7 21.0
Pelee2-PRN (private) 416 3.81M 4.76B 24.8 46.8 24.0
Pelee2-PRN-GIoU (private) [6] 320 3.81M 2.82B 24.4 41.6 25.3
Pelee2-PRN-GIoU (private) [6] 416 3.81M 4.76B 26.8 45.3 28.1
Pelee2-PRN-GIoU-Scale (private) 416 3.81M 4.76B 26.9 45.5 28.0
Pelee2-SPP-PRN (private) [7] 320 - 2.88B 22.1 42.6 20.8
Pelee2-SPP-PRN (private) [7] 416 - 4.86B 24.7 47.1 23.6
Pelee2-SPP-PRN-GIoU-Scale (private) o320 - 2.88B 24.9 43.6 25.6
Pelee2-SPP-PRN-GIoU-Scale (private) 416 - 4.86B 27.3 46.4 28.5
Pelee2-PRN-GIoU-Mixup (private) [8] 320 3.81M 2.82B 22.4 39.2 22.9
Pelee2-PRN-GIoU-Mixup (private) [8] 416 3.81M 4.76B 24.3 42.1 24.9
SparsePelee-PRN [9] 320 - 2.16B 20.3 40.0 18.7
SparsePelee-PRN [9] 416 - 3.65B 22.7 44.1 21.3
PartialPelee-PRN (private) 320 - 2.17B 20.5 40.2 19.1
PartialPelee-PRN (private) 416 - 3.67B 22.7 44.0 21.4
PartialXPelee-PRN (private) 416 - 4.17B 25.2 47.2 24.6
Pelee2-TridentNet (private) [10] 320 - 3.63B 22.6 42.7 21.6
Pelee2-TridentNet (private) [10] 416 - 6.14B 25.5 47.0 25.2
Pelee2-CEM (private) [11] 320 - 2.85B 21.4 41.2 20.2
Pelee2-CEM (private) [11] 416 - 4.81B 23.7 45.2 22.8
Pelee2-CEM-SAM (private) [11] 320 - 2.90B 21.5 42.0 20.3
Pelee2-CEM-SAM (private) [11] 416 - 4.90B 24.2 46.1 23.3
Pelee2-CEM-SAM-One (private) [11] 416 - 4.95B 25.5 48.0 24.2
Pelee2-DC-SPP (private) [12] 320 - 2.81B 20.0 38.8 18.9
Pelee2-DC-SPP (private) [12] 416 - 4.75B 23.2 43.7 22.4
Pelee-ReCORE (private) 320 - 2.86B 21.6 42.6 19.7
Pelee-ReCORE (private) 416 - 4.82B 23.9 46.0 22.5
Pelee-ReCORE-BiF (private) 320 - 3.15B 24.3 45.8 23.6
Pelee-ReCORE-BiF (private) 416 - 5.32B 26.7 49.5 26.3
Pelee-ReCORE-BiF-Scale (private) 416 - 5.32B 26.7 49.6 26.4
Pelee-ReCORE-BiF-GIoU (private) 416 - 5.32B 26.8 45.7 27.7
Pelee-ReCORE-BiF-GIoU-Scale (private) 416 - 5.32B 27.4 46.6 28.5
Pelee-ReBiF (private) 416 - 6.63B 26.6 48.8 26.4
Pelee-ReBiF-Scale (private) 416 - 6.63B 26.9 49.1 26.5
Pelee-ReBiF-GIoU (private) 416 - 6.63B 26.9 45.4 28.0
Pelee-ReBiF-GIoU-Scale (private) 416 - 6.63B 27.5 46.2 28.6

[1] Wang, R. J., Li, X., & Ling, C. X. (2018). Pelee: A real-time object detection system on mobile devices. In Advances in Neural Information Processing Systems (pp. 1963-1972).

[2] Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.

[3] Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017). Feature pyramid networks for object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2117-2125).

[4] Wang, C. Y., Mark Liao, H. Y., Chen, P. Y., & Hsieh, J. W. (2019). Enriching Variety of Layer-wise Learning Information by Gradient Combination. In Proceedings of the IEEE International Conference on Computer Vision Workshops (pp. 0-0).

[5] Liu, S., Qi, L., Qin, H., Shi, J., & Jia, J. (2018). Path aggregation network for instance segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 8759-8768).

[6] Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., & Savarese, S. (2019). Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

[7] He, K., Zhang, X., Ren, S., & Sun, J. (2015). Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE transactions on pattern analysis and machine intelligence, 37(9), 1904-1916.

[8] Zhang, Z., He, T., Zhang, H., Zhang, Z., Xie, J., & Li, M. (2019). Bag of Freebies for Training Object Detection Neural Networks. arXiv preprint arXiv:1902.04103.

[9] Zhu, L., Deng, R., Maire, M., Deng, Z., Mori, G., & Tan, P. (2019). Sparsely Aggregated Convolutional Networks. In Proceedings of the European Conference on Computer Vision.

[10] Li, Y., Chen, Y., Wang, N., & Zhang, Z. (2019). Scale-Aware Trident Networks for Object Detection. arXiv preprint arXiv:1901.01892.

[11] Qin, Z., Li, Z., Zhang, Z., Bao, Y., Yu, G., Peng, Y., & Sun, J. (2019). ThunderNet: Towards Real-time Generic Object Detection. arXiv preprint arXiv:1903.11752.

[12] Huang, Z., & Wang, J. (2019). DC-SPP-YOLO: Dense Connection and Spatial Pyramid Pooling Based YOLO for Object Detection. arXiv preprint arXiv:1903.08589.