It's the list with popular deep learning models related to classification and segmentation task
- AlexNet ('One weird trick for parallelizing convolutional neural networks') [2014]
- VGG/BN-VGG ('Very Deep Convolutional Networks for Large-Scale Image Recognition') [2014]
- ResNet ('Deep Residual Learning for Image Recognition') [2015]
- InceptionV3 ('Rethinking the Inception Architecture for Computer Vision') [2015]
- PreResNet ('Identity Mappings in Deep Residual Networks') [2016]
- DenseNet ('Densely Connected Convolutional Networks') [2016]
- PyramidNet ('Deep Pyramidal Residual Networks') [2016]
- ResNeXt ('Aggregated Residual Transformations for Deep Neural Networks') [2016]
- WRN ('Wide Residual Networks') [2016]
- Xception ('Xception: Deep Learning with Depthwise Separable Convolutions') [2016]
- InceptionV4/InceptionResNetV2 ('Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning') [2016]
- PolyNet ('PolyNet: A Pursuit of Structural Diversity in Very Deep Networks') [2016]
- DarkNet ('Darknet: Open source neural networks in C') [2016?]
- ResAttNet ('Residual Attention Network for Image Classification') [2017]
- CondenseNet ('CondenseNet: An Efficient DenseNet using Learned Group Convolutions') [2017]
- DRN-C/DRN-D ('Dilated Residual Networks') [2017]
- DPN ('Dual Path Networks') [2017]
- ShuffleNet ('ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices') [2017]
- DiracNetV2 ('DiracNets: Training Very Deep Neural Networks Without Skip-Connections') [2017]]
- SENet/SE-ResNet/SE-PreResNet/SE-ResNeXt ('Squeeze-and-Excitation Networks') [2017]
- MobileNet ('MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications') [2017]
- NASNet ('Learning Transferable Architectures for Scalable Image Recognition') [2017]
- AirNet/AirNeXt ('Attention Inspiring Receptive-Fields Network for Learning Invariant Representations') [2018]
- BAM-ResNet ('BAM: Bottleneck Attention Module') [2018]
- CBAM-ResNet ('CBAM: Convolutional Block Attention Module') [2018]
- SqueezeNet/SqueezeResNet ('SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size') [2016]
- SqueezeNext ('SqueezeNext: Hardware-Aware Neural Network Design') [2018]
- ShuffleNetV2 ('ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design') [2018]
- MENet ('Merging and Evolution: Improving Convolutional Neural Networks for Mobile Applications') [2018]
- FD-MobileNet ('FD-MobileNet: Improved MobileNet with A Fast Downsampling Strategy') [2018]
- MobileNetV2 ('MobileNetV2: Inverted Residuals and Linear Bottlenecks') [2018]
- IGCV3 ('IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks') [2018]
- DARTS ('DARTS: Differentiable Architecture Search') [2018]
- PNASNet ('Progressive Neural Architecture Search') [2018]
- Amoeba ('Regularized Evolution for Image Classifier Architecture Search') [2018]
- MnasNet ('MnasNet: Platform-Aware Neural Architecture Search for Mobile') [2018]
- IBN-Net ('Two at Once: Enhancing Learning andGeneralization Capacities via IBN-Net') [2018]
- MarginNet ('Large Margin Deep Networks for Classification') [2018]
- A^2 Nets ('A^2-Nets: Double Attention Networks') [2018]
- FishNet ('FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction') [2018]
| Model | Number of parameters | Top-1 Error | Top-5 Error |
|---|---|---|---|
| AlexNet | 61.1M | 44.12 | 21.26 |
| VGG-16 | 138.3M | 26.78 | 8.69 |
| ResNet-50 | 25.5M | 23.50 | 6.87 |
| Inception v3 | 23.8M | 21.2 | 5.6 |
| PreResNet-50 | 25.5M | 23.39 | 6.68 |
| DenseNet-121 | 7.9M | 25.0 | 7.71 |
| PyramidNet-200(a=300) | 62.1M | 19.5 | 4.8 |
| PyramidNet-200(a=450) | 116.4M | 19.2 | 4.7 |
| ResNeXt-101 | 83.5M | 20.4 | 5.3 |
| WRN-50-2-bottleneck | 68.9M | 21.9 | 6.03 |
| Xception | ? | 21.0 | 5.5 |
| Inception-ResNet-v2 | 55.9M | 19.9 | 4.9 |
| Inception-v4 | 42.6M | 20.0 | 5.0 |
| Very Deep PolyNet | ? | 18.71 | 4.25 |
| DarkNet Ref | 7.3M | 38.09 | 16.71 |
| Attention-92 | 51.3M | 19.5 | 4.8 |
| CondenseNet (G=C=8) | 4.8M | 26.2 | 8.3 |
| DRN-A-50 | 25.6M | 22.94 | 6.57 |
| DPN-131 | 79.3M | 18.55 | 4.16 |
| ShuffleNet 2×(g=3) | ? | 26.3 | ? |
| DiracNet-34 | 21.8M | 27.79 | 9.34 |
| SENet-154 | 115.2M | 18.84 | 4.65 |
| MobileNet | 4.2M | 29.4 | 10.5 |
| NASNet-A | 5.3M | 26.0 | 8.7 |
| AirNet50-1x64d (r=2) | 27.43M | 22.48 | 6.21 |
| BAM-ResNet-50 | 25.92M | 23.68 | 6.96 |
| CBAM-ResNet-50 | 28.1M | 23.02 | 6.38 |
| SqueezeResNet | 1.23M | 39.83 | 17.84 |
| 2.0-SqNxt-23v5 | 3.2M | 32.56 | 11.8 |
| ShuffleNet v2 2x SE | 7.6M | 24.6 | ? |
| 456-MENet-24×1(g=3) | 5.3M | 28.4 | 9.8 |
| FD-MobileNet 1x | 2.9M | 34.7 | ? |
| MobileNetV2 | 3.4M | 28.0 | ? |
| IGCV3 | 3.5M | 28.22 | 9.54 |
| DARTS | 4.9M | 26.9 | 9.0 |
| PNASNet-5 | 5.1M | 25.8 | 8.1 |
| AmoebaNet-C | 5.1M | 24.3 | 7.6 |
| MnasNet-92 (+SE) | 5.1M | 23.87 | 7.15 |
| IBN-Net50-a | ? | 22.54 | 6.32 |
| MarginNet | ? | 22.0 | ? |
| A^2 Net | ? | 23.0 | 6.5 |
| FishNeXt-150 | 26.2M | 21.5 | ? |
- U-Net ('U-Net: Convolutional Networks for Biomedical Image Segmentation') [2015]
- DeconvNet ('Learning Deconvolution Network for Semantic Segmentation') [2015]
- ParseNet ('ParseNet: Looking Wider to See Better') [2015]
- Piecewise ('Efficient piecewise training of deep structured models for semantic segmentation') [2015]
- SegNet ('SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation') [2016]
- FCN ('Fully Convolutional Networks for Semantic Segmentation') [2016]
- ENet ('ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation') [2016]
- DilatedNet ('MULTI-SCALE CONTEXT AGGREGATION BY DILATED CONVOLUTIONS') [2016]
- PixelNet ('PixelNet: Towards a General Pixel-Level Architecture') [2016]
- RefineNet ('RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation') [2016]
- LRR ('Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation') [2016]
- FRRN ('Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes') [2016]
- Semantic Segmentation using Adversarial Networks ('Semantic Segmentation using Adversarial Networks') [2016]
- MultiNet ('MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving') [2016]
- DeepLab ('DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs') [2017]
- LinkNet ('LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation') [2017]
- Tiramisu ('The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation') [2017]
- ICNet ('ICNet for Real-Time Semantic Segmentation on High-Resolution Images') [2017]
- ERFNet ('Efficient ConvNet for Real-time Semantic Segmentation') [2017]
- PSPNet ('Pyramid Scene Parsing Network') [2017]
- GCN ('Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network') [2017]
- Segaware ('Segmentation-Aware Convolutional Networks Using Local Attention Masks') [2017]
- PixelDCN ('PIXEL DECONVOLUTIONAL NETWORKS') [2017]
- DeepLabv3 ('Rethinking Atrous Convolution for Semantic Image Segmentation') [2017]
- DUC, HDC ('Understanding Convolution for Semantic Segmentation') [2018]
- ShuffleSeg ('SHUFFLESEG: REAL-TIME SEMANTIC SEGMENTATION NETWORK') [2018]
- AdaptSegNet ('Learning to Adapt Structured Output Space for Semantic Segmentation') [2018]
- TuSimple-DUC ('Understanding Convolution for Semantic Segmentation') [2018]
- R2U-Net ('Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation') [2018]
- Attention U-Net ('Attention U-Net: Learning Where to Look for the Pancreas') [2018]
- DANet ('Dual Attention Network for Scene Segmentation') [2018]
- ENCNet ('Context Encoding for Semantic Segmentation') [2018]
- ShelfNet ('ShelfNet for Real-time Semantic Segmentation') [2018]
- LadderNet ('LADDERNET: MULTI-PATH NETWORKS BASED ON U-NET FOR MEDICAL IMAGE SEGMENTATION') [2018]
- ССС ('Concentrated-Comprehensive Convolutions for lightweight semantic segmentation') [2018]
- DifNet ('DifNet: Semantic Segmentation by Diffusion Networks') [2018]
- BiSeNet ('BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation') [2018]
- ESPNet ('ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation') [2018]
| Model | PASCAL-Context | Cityscapes (mIOU) | PASCAL VOC 2012 (mIOU) | COCO Stuff | ADE20K VAL (mIOU) |
|---|---|---|---|---|---|
| U-Net | ? | ? | ? | ? | ? |
| DeconvNet | ? | ? | 72.5 | ? | ? |
| ParseNet | 40.4 | ? | 69.8 | ? | ? |
| Piecewise | 43.3 | 71.6 | 78.0 | ? | ? |
| SegNet | ? | 56.1 | ? | ? | ? |
| FCN | 37.8 | 65.3 | 62.2 | 22.7 | 29.39 |
| ENet | ? | 58.3 | ? | ? | ? |
| DilatedNet | ? | ? | 67.6 | ? | 32.31 |
| PixelNet | ? | ? | 69.8 | ? | ? |
| RefineNet | 47.3 | 73.6 | 83.4 | 33.6 | 40.70 |
| LRR | ? | 71.8 | 79.3 | ? | ? |
| FRRN | ? | 71.8 | ? | ? | ? |
| MultiNet | ? | ? | ? | ? | ? |
| DeepLab | 45.7 | 64.8 | 79.7 | ? | ? |
| LinkNet | ? | ? | ? | ? | ? |
| Tiramisu | ? | ? | ? | ? | ? |
| ICNet | ? | 70.6 | ? | ? | ? |
| ERFNet | ? | 68.0 | ? | ? | ? |
| PSPNet | 47.8 | 80.2 | 85.4 | ? | 44.94 |
| GCN | ? | 76.9 | 82.2 | ? | ? |
| Segaware | ? | ? | 69.0 | ? | ? |
| PixelDCN | ? | ? | 73.0 | ? | ? |
| DeepLabv3 | ? | ? | 85.7 | ? | ? |
| DUC, HDC | ? | 77.1 | ? | ? | ? |
| ShuffleSeg | ? | 59.3 | ? | ? | ? |
| AdaptSegNet | ? | 46.7 | ? | ? | ? |
| TuSimple-DUC | 80.1 | ? | 83.1 | ? | ? |
| R2U-Net | ? | ? | ? | ? | ? |
| Attention U-Net | ? | ? | ? | ? | ? |
| DANet | 52.6 | 81.5 | ? | 39.7 | ? |
| ENCNet | 51.7 | 75.8 | 85.9 | ? | 44.65 |
| ShelfNet | 48.4 | 75.8 | 84.2 | ? | ? |
| LadderNet | ? | ? | ? | ? | ? |
| CCC-ERFnet | ? | 69.01 | ? | ? | ? |
| DifNet-101 | 45.1 | ? | 73.2 | ? | ? |
| BiSeNet(Res18) | ? | ? | 74.7 | 28.1 | ? |
| ESPNet | ? | ? | 63.01 | ? | ? |
- [R-CNN] Rich feature hierarchies for accurate object detection and semantic segmentation | Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik | [CVPR' 14] |
[pdf][official code - caffe][2014] - [OverFeat] OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks | Pierre Sermanet, et al. | [ICLR' 14] |
[pdf][official code - torch][2014] - [MultiBox] Scalable Object Detection using Deep Neural Networks | Dumitru Erhan, et al. | [CVPR' 14] |
[pdf][2014] - [SPP-Net] Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition | Kaiming He, et al. | [ECCV' 14] |
[pdf][official code - caffe][unofficial code - keras][unofficial code - tensorflow][2014] - [MR-CNN] Object detection via a multi-region & semantic segmentation-aware CNN model | Spyros Gidaris, Nikos Komodakis | [ICCV' 15] |
[pdf][official code - caffe][2015] - [DeepBox] DeepBox: Learning Objectness with Convolutional Networks | Weicheng Kuo, Bharath Hariharan, Jitendra Malik | [ICCV' 15] |
[pdf][official code - caffe][2015] - [AttentionNet] AttentionNet: Aggregating Weak Directions for Accurate Object Detection | Donggeun Yoo, et al. | [ICCV' 15] |
[pdf][2015] - [Fast R-CNN] Fast R-CNN | Ross Girshick | [ICCV' 15] |
[pdf][official code - caffe][2015] - [DeepProposal] DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers | Amir Ghodrati, et al. | [ICCV' 15] |
[pdf][official code - matconvnet][2015] - [Faster R-CNN, RPN] Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks | Shaoqing Ren, et al. | [NIPS' 15] |
[pdf][official code - caffe][unofficial code - tensorflow][unofficial code - pytorch] - [YOLO v1] You Only Look Once: Unified, Real-Time Object Detection | Joseph Redmon, et al. | [CVPR' 16] |
[pdf][official code - c][2016] - [G-CNN] G-CNN: an Iterative Grid Based Object Detector | Mahyar Najibi, et al. | [CVPR' 16] |
[pdf][2016] - [AZNet] Adaptive Object Detection Using Adjacency and Zoom Prediction | Yongxi Lu, Tara Javidi. | [CVPR' 16] |
[pdf][2016] - [ION] Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks | Sean Bell, et al. | [CVPR' 16] |
[pdf][2016] - [HyperNet] HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection | Tao Kong, et al. | [CVPR' 16] |
[pdf][2016] - [OHEM] Training Region-based Object Detectors with Online Hard Example Mining | Abhinav Shrivastava, et al. | [CVPR' 16] |
[pdf][official code - caffe][2016] - [CRAPF] CRAFT Objects from Images | Bin Yang, et al. | [CVPR' 16] |
[pdf][official code - caffe][2016] - [MPN] A MultiPath Network for Object Detection | Sergey Zagoruyko, et al. | [BMVC' 16] |
[pdf][official code - torch][2016] - [SSD] SSD: Single Shot MultiBox Detector | Wei Liu, et al. | [ECCV' 16] |
[pdf][official code - caffe][unofficial code - tensorflow][unofficial code - pytorch][2016] - [GBDNet] Crafting GBD-Net for Object Detection | Xingyu Zeng, et al. | [ECCV' 16] |
[pdf][official code - caffe][2016] - [CPF] Contextual Priming and Feedback for Faster R-CNN | Abhinav Shrivastava and Abhinav Gupta | [ECCV' 16] |
[pdf][2016] - [MS-CNN] A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection | Zhaowei Cai, et al. | [ECCV' 16] |
[pdf][official code - caffe][2016] - [R-FCN] R-FCN: Object Detection via Region-based Fully Convolutional Networks | Jifeng Dai, et al. | [NIPS' 16] |
[pdf][official code - caffe][unofficial code - caffe][2016] - [PVANET] PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection | Kye-Hyeon Kim, et al. | [NIPSW' 16] |
[pdf][official code - caffe][2016] - [DeepID-Net] DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection | Wanli Ouyang, et al. | [PAMI' 16] |
[pdf][2016] - [NoC] Object Detection Networks on Convolutional Feature Maps | Shaoqing Ren, et al. | [TPAMI' 16] |
[pdf] - [DSSD] DSSD : Deconvolutional Single Shot Detector | Cheng-Yang Fu1, et al. | [arXiv' 17] |
[pdf][official code - caffe][2017] - [TDM] Beyond Skip Connections: Top-Down Modulation for Object Detection | Abhinav Shrivastava, et al. | [CVPR' 17] |
[pdf][2017] - [FPN] Feature Pyramid Networks for Object Detection | Tsung-Yi Lin, et al. | [CVPR' 17] |
[pdf][unofficial code - caffe][2017] - [YOLO v2] YOLO9000: Better, Faster, Stronger | Joseph Redmon, Ali Farhadi | [CVPR' 17] |
[pdf][official code - c][unofficial code - caffe][unofficial code - tensorflow][unofficial code - tensorflow][unofficial code - pytorch][2017] - [RON] RON: Reverse Connection with Objectness Prior Networks for Object Detection | Tao Kong, et al. | [CVPR' 17] |
[pdf][official code - caffe][unofficial code - tensorflow][2017] - [DCN] Deformable Convolutional Networks | Jifeng Dai, et al. | [ICCV' 17] |
[pdf][official code - mxnet][unofficial code - tensorflow][unofficial code - pytorch][2017] - [DeNet] DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling | Lachlan Tychsen-Smith, Lars Petersson | [ICCV' 17] |
[pdf][official code - theano][2017] - [CoupleNet] CoupleNet: Coupling Global Structure with Local Parts for Object Detection | Yousong Zhu, et al. | [ICCV' 17] |
[pdf][official code - caffe][2017] - [RetinaNet] Focal Loss for Dense Object Detection | Tsung-Yi Lin, et al. | [ICCV' 17] |
[pdf][official code - keras][unofficial code - pytorch][unofficial code - mxnet][unofficial code - tensorflow][2017] - [Mask R-CNN] Mask R-CNN | Kaiming He, et al. | [ICCV' 17] |
[pdf][official code - caffe2][unofficial code - tensorflow][unofficial code - tensorflow][unofficial code - pytorch][2017] - [DSOD] DSOD: Learning Deeply Supervised Object Detectors from Scratch | Zhiqiang Shen, et al. | [ICCV' 17] |
[pdf][official code - caffe][unofficial code - pytorch][2017] - [SMN] Spatial Memory for Context Reasoning in Object Detection | Xinlei Chen, Abhinav Gupta | [ICCV' 17] |
[pdf][2017] - [YOLO v3] YOLOv3: An Incremental Improvement | Joseph Redmon, Ali Farhadi | [arXiv' 18] |
[pdf][official code - c][unofficial code - pytorch][unofficial code - pytorch][unofficial code - keras][unofficial code - tensorflow][2018] - [ZIP] Zoom Out-and-In Network with Recursive Training for Object Proposal | Hongyang Li, et al. | [IJCV' 18] |
[pdf][official code - caffe][2018] - [SIN] Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships | Yong Liu, et al. | [CVPR' 18] |
[pdf][official code - tensorflow][2018] - [STDN] Scale-Transferrable Object Detection | Peng Zhou, et al. | [CVPR' 18] |
[pdf] - [RefineDet] Single-Shot Refinement Neural Network for Object Detection | Shifeng Zhang, et al. | [CVPR' 18] |
[pdf][official code - caffe][unofficial code - chainer][unofficial code - pytorch][2018] - [MegDet] MegDet: A Large Mini-Batch Object Detector | Chao Peng, et al. | [CVPR' 18] |
[pdf][2018] - [DA Faster R-CNN] Domain Adaptive Faster R-CNN for Object Detection in the Wild | Yuhua Chen, et al. | [CVPR' 18] |
[pdf][official code - caffe][2018] - [SNIP] An Analysis of Scale Invariance in Object Detection – SNIP | Bharat Singh, Larry S. Davis | [CVPR' 18] |
[pdf][2018] - [Relation-Network] Relation Networks for Object Detection | Han Hu, et al. | [CVPR' 18] |
[pdf][official code - mxnet][2018] - [Cascade R-CNN] Cascade R-CNN: Delving into High Quality Object Detection | Zhaowei Cai, et al. | [CVPR' 18] |
[pdf][official code - caffe][2018] - Finding Tiny Faces in the Wild with Generative Adversarial Network | Yancheng Bai, et al. | [CVPR' 18] |
[pdf][2018] - [STDnet] STDnet: A ConvNet for Small Target Detection | Brais Bosquet, et al. | [BMVC' 18] |
[pdf][2018] - [RFBNet] Receptive Field Block Net for Accurate and Fast Object Detection | Songtao Liu, et al. | [ECCV' 18] |
[pdf][official code - pytorch][2018] - Zero-Annotation Object Detection with Web Knowledge Transfer | Qingyi Tao, et al. | [ECCV' 18] |
[pdf][2018] - [CornerNet] CornerNet: Detecting Objects as Paired Keypoints | Hei Law, et al. | [ECCV' 18] |
[pdf][official code - pytorch][2018] - [Pelee] Pelee: A Real-Time Object Detection System on Mobile Devices | Jun Wang, et al. | [NIPS' 18] |
[pdf][official code - caffe][2018] - [HKRM] Hybrid Knowledge Routed Modules for Large-scale Object Detection | ChenHan Jiang, et al. | [NIPS' 18] |
[pdf][2018] - [MetaAnchor] MetaAnchor: Learning to Detect Objects with Customized Anchors | Tong Yang, et al. | [NIPS' 18] |
[pdf][2018] - [M2Det] M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network | Jun Wang, et al. | [AAAI' 19] |
[pdf][2018]
| Detector | VOC07 (mAP@IoU=0.5) | VOC12 (mAP@IoU=0.5) | COCO (mAP) |
|---|---|---|---|
| R-CNN | 58.5 | - | - |
| OverFeat | - | - | - |
| MultiBox | 29.0 | - | - |
| SPP-Net | 59.2 | - | - |
| MR-CNN | 78.2 | 73.9 | - |
| AttentionNet | - | - | - |
| Fast R-CNN | 70.0 | 68.4 | - |
| Faster R-CNN | 73.2 | 70.4 | 36.8 |
| YOLO v1 | 66.4 | 57.9 | - |
| G-CNN | 66.8 | 66.4 | - |
| AZNet | 70.4 | - | 22.3 |
| ION | 80.1 | 77.9 | 33.1 |
| HyperNet | 76.3 | 71.4 | - |
| OHEM | 78.9 | 76.3 | 22.4 |
| MPN | - | - | 33.2 |
| SSD | 76.8 | 74.9 | 31.2 |
| GBDNet | 77.2 | - | 27.0 |
| CPF | 76.4 | 72.6 | - |
| MS-CNN | - | - | - |
| R-FCN | 79.5 | 77.6 | 29.9 |
| PVANET | - | - | - |
| DeepID-Net | 69.0 | - | - |
| NoC | 71.6 | 68.8 | 27.2 |
| DSSD | 81.5 | 80.0 | - |
| TDM | - | - | 37.3 |
| FPN | - | - | 36.2 |
| YOLO v2 | 78.6 | 73.4 | 21.6 |
| RON | 77.6 | 75.4 | - |
| DCN | - | - | - |
| DeNet | 77.1 | 73.9 | 33.8 |
| CoupleNet | 82.7 | 80.4 | 34.4 |
| RetinaNet | - | - | 39.1 |
| Mask R-CNN | - | - | 39.8 |
| DSOD | 77.7 | 76.3 | - |
| SMN | 70.0 | - | - |
| YOLO v3 | - | - | 33.0 |
| SIN | 76.0 | 73.1 | 23.2 |
| STDN | 80.9 | - | - |
| RefineDet | 83.8 | 83.5 | 41.8 |
| MegDet | - | - | - |
| RFBNet | 82.2 | - | - |
| CornerNet | - | - | 42.1 |