Awesome Computer Vision Models !Awesome (https://awesome.re/badge-flat.svg) (https://awesome.re)

A curated list of popular classification, segmentation and detection models with corresponding evaluation metrics from papers.


Contents

- Classification models (#classification-models)
- Segmentation models (#segmentation-models)
- Detection models (#detection-models)


Classification models

│                                                                                    Model                                                                                    │Number of parameters│  FLOPS   │Top-1 Error│Top-5 Error│Year│
├─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┼────────────────────┼──────────┼───────────┼───────────┼────┤
│                                AlexNet ('One weird trick for parallelizing convolutional neural networks' (https://arxiv.org/abs/1404.5997))                                │       62.3M        │1,132.33M │   40.96   │   18.24   │2014│
│                               VGG-16 ('Very Deep Convolutional Networks for Large-Scale Image Recognition' (https://arxiv.org/abs/1409.1556))                               │       138.3M       │    ?     │   26.78   │   8.69    │2014│
│                                        ResNet-10 ('Deep Residual Learning for Image Recognition' (https://arxiv.org/abs/1512.03385))                                        │        5.5M        │ 894.04M  │   34.69   │   14.36   │2015│
│                                        ResNet-18 ('Deep Residual Learning for Image Recognition' (https://arxiv.org/abs/1512.03385))                                        │       11.7M        │1,820.41M │   28.53   │   9.82    │2015│
│                                        ResNet-34 ('Deep Residual Learning for Image Recognition' (https://arxiv.org/abs/1512.03385))                                        │       21.8M        │3,672.68M │   24.84   │   7.80    │2015│
│                                        ResNet-50 ('Deep Residual Learning for Image Recognition' (https://arxiv.org/abs/1512.03385))                                        │       25.5M        │3,877.95M │   22.28   │   6.33    │2015│
│                                InceptionV3 ('Rethinking the Inception Architecture for Computer Vision' (https://arxiv.org/abs/1512.00567))                                 │       23.8M        │    ?     │   21.2    │    5.6    │2015│
│                                       PreResNet-18 ('Identity Mappings in Deep Residual Networks' (https://arxiv.org/abs/1603.05027))                                       │       11.7M        │1,820.56M │   28.43   │   9.72    │2016│
│                                       PreResNet-34 ('Identity Mappings in Deep Residual Networks' (https://arxiv.org/abs/1603.05027))                                       │       21.8M        │3,672.83M │   24.89   │   7.74    │2016│
│                                       PreResNet-50 ('Identity Mappings in Deep Residual Networks' (https://arxiv.org/abs/1603.05027))                                       │       25.6M        │3,875.44M │   22.40   │   6.47    │2016│
│                                        DenseNet-121 ('Densely Connected Convolutional Networks' (https://arxiv.org/abs/1608.06993))                                         │        8.0M        │2,872.13M │   23.48   │   7.04    │2016│
│                                        DenseNet-161 ('Densely Connected Convolutional Networks' (https://arxiv.org/abs/1608.06993))                                         │       28.7M        │7,793.16M │   22.86   │   6.44    │2016│
│                                           PyramidNet-101 ('Deep Pyramidal Residual Networks' (https://arxiv.org/abs/1610.02915))                                            │       42.5M        │8,743.54M │   21.98   │   6.20    │2016│
│                            ResNeXt-14(32x4d) ('Aggregated Residual Transformations for Deep Neural Networks' (http://arxiv.org/abs/1611.05431))                             │        9.5M        │1,603.46M │   30.32   │   11.46   │2016│
│                            ResNeXt-26(32x4d) ('Aggregated Residual Transformations for Deep Neural Networks' (http://arxiv.org/abs/1611.05431))                             │       15.4M        │2,488.07M │   24.14   │   7.46    │2016│
│                                                   WRN-50-2 ('Wide Residual Networks' (https://arxiv.org/abs/1605.07146))                                                    │       68.9M        │11,405.42M│   22.53   │   6.41    │2016│
│                                Xception ('Xception: Deep Learning with Depthwise Separable Convolutions' (https://arxiv.org/abs/1610.02357))                                │     22,855,952     │8,403.63M │   20.97   │   5.49    │2016│
│                    InceptionV4 ('Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning' (https://arxiv.org/abs/1602.07261))                     │     42,679,816     │12,304.93M│   20.64   │   5.29    │2016│
│                 InceptionResNetV2 ('Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning' (https://arxiv.org/abs/1602.07261))                  │     55,843,464     │13,188.64M│   19.93   │   4.90    │2016│
│                               PolyNet ('PolyNet: A Pursuit of Structural Diversity in Very Deep Networks' (https://arxiv.org/abs/1611.05725))                               │     95,366,600     │34,821.34M│   19.10   │   4.52    │2016│
│                                       DarkNet Ref ('Darknet: Open source neural networks in C' (https://github.com/pjreddie/darknet))                                       │     7,319,416      │ 367.59M  │   38.58   │   17.18   │2016│
│                                      DarkNet Tiny ('Darknet: Open source neural networks in C' (https://github.com/pjreddie/darknet))                                       │     1,042,104      │ 500.85M  │   40.74   │   17.84   │2016│
│                                       DarkNet 53 ('Darknet: Open source neural networks in C' (https://github.com/pjreddie/darknet))                                        │     41,609,928     │7,133.86M │   21.75   │   5.64    │2016│
│                 SqueezeResNet1.1 ('SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size' (https://arxiv.org/abs/1602.07360))                  │     1,235,496      │ 352.02M  │   40.09   │   18.21   │2016│
│                   SqueezeNet1.1 ('SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size' (https://arxiv.org/abs/1602.07360))                   │     1,235,496      │ 352.02M  │   39.31   │   17.72   │2016│
│                                   ResAttNet-92 ('Residual Attention Network for Image Classification' (https://arxiv.org/abs/1704.06904))                                   │       51.3M        │    ?     │   19.5    │    4.8    │2017│
│                       CondenseNet (G=C=8) ('CondenseNet: An Efficient DenseNet using Learned Group Convolutions' (https://arxiv.org/abs/1711.09224))                        │        4.8M        │    ?     │   26.2    │    8.3    │2017│
│                                                      DPN-68 ('Dual Path Networks' (https://arxiv.org/abs/1707.01629))                                                       │     12,611,602     │2,351.84M │   23.24   │   6.79    │2017│
│               ShuffleNet x1.0 (g=1) ('ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices' (https://arxiv.org/abs/1707.01083))               │     1,531,936      │ 148.13M  │   34.93   │   13.89   │2017│
│                         DiracNetV2-18 ('DiracNets: Training Very Deep Neural Networks Without Skip-Connections' (https://arxiv.org/abs/1706.00388))                         │     11,511,784     │1,796.62M │   31.47   │   11.70   │2017│
│                         DiracNetV2-34 ('DiracNets: Training Very Deep Neural Networks Without Skip-Connections' (https://arxiv.org/abs/1706.00388))                         │     21,616,232     │3,646.93M │   28.75   │   9.93    │2017│
│                                               SENet-16 ('Squeeze-and-Excitation Networks' (https://arxiv.org/abs/1709.01507))                                               │     31,366,168     │5,081.30M │   25.65   │   8.20    │2017│
│                                              SENet-154 ('Squeeze-and-Excitation Networks' (https://arxiv.org/abs/1709.01507))                                               │    115,088,984     │20,745.78M│   18.62   │   4.61    │2017│
│                     MobileNet ('MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications' (https://arxiv.org/abs/1704.04861))                     │     4,231,976      │ 579.80M  │   26.61   │   8.95    │2017│
│                          NASNet-A 4@1056 ('Learning Transferable Architectures for Scalable Image Recognition' (https://arxiv.org/abs/1707.07012))                          │     5,289,978      │ 584.90M  │   25.68   │   8.16    │2017│
│                          NASNet-A 6@4032('Learning Transferable Architectures for Scalable Image Recognition' (https://arxiv.org/abs/1707.07012))                           │     88,753,150     │23,976.44M│   18.14   │   4.21    │2017│
│                                                    DLA-34 ('Deep Layer Aggregation' (https://arxiv.org/abs/1707.06484))                                                     │     15,742,104     │3,071.37M │   25.36   │   7.94    │2017│
│         AirNet50-1x64d (r=2) ('Attention Inspiring Receptive-Fields Network for Learning Invariant Representations' (https://ieeexplore.ieee.org/document/8510896))         │       27.43M       │    ?     │   22.48   │   6.21    │2018│
│                                            BAM-ResNet-50 ('BAM: Bottleneck Attention Module' (https://arxiv.org/abs/1807.06514))                                            │       25.92M       │    ?     │   23.68   │   6.96    │2018│
│                                      CBAM-ResNet-50 ('CBAM: Convolutional Block Attention Module' (https://arxiv.org/abs/1807.06521))                                       │       28.1M        │    ?     │   23.02   │   6.38    │2018│
│                                   1.0-SqNxt-23v5 ('SqueezeNext: Hardware-Aware Neural Network Design' (https://arxiv.org/abs/1803.10615))                                   │      921,816       │ 285.82M  │   40.77   │   17.85   │2018│
│                                   1.5-SqNxt-23v5 ('SqueezeNext: Hardware-Aware Neural Network Design' (https://arxiv.org/abs/1803.10615))                                   │     1,953,616      │ 550.97M  │   33.81   │   13.01   │2018│
│                                   2.0-SqNxt-23v5 ('SqueezeNext: Hardware-Aware Neural Network Design' (https://arxiv.org/abs/1803.10615))                                   │     3,366,344      │ 897.60M  │   29.63   │   10.66   │2018│
│                        ShuffleNetV2 ('ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design' (https://arxiv.org/abs/1807.11164))                        │     2,278,604      │ 149.72M  │   31.44   │   11.63   │2018│
│              456-MENet-24×1(g=3) ('Merging and Evolution: Improving Convolutional Neural Networks for Mobile Applications' (https://arxiv.org/abs/1803.09127))              │        5.3M        │    ?     │   28.4    │    9.8    │2018│
│                           FD-MobileNet ('FD-MobileNet: Improved MobileNet with A Fast Downsampling Strategy' (https://arxiv.org/abs/1802.03750))                            │     2,901,288      │ 147.46M  │   34.23   │   13.38   │2018│
│                                  MobileNetV2 ('MobileNetV2: Inverted Residuals and Linear Bottlenecks' (https://arxiv.org/abs/1801.04381))                                  │     3,504,960      │ 329.36M  │   26.97   │   8.87    │2018│
│                       IGCV3 ('IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks' (https://arxiv.org/abs/1806.00178))                        │        3.5M        │    ?     │   28.22   │   9.54    │2018│
│                                           DARTS ('DARTS: Differentiable Architecture Search' (https://arxiv.org/abs/1806.09055))                                            │        4.9M        │    ?     │   26.9    │    9.0    │2018│
│                                           PNASNet-5 ('Progressive Neural Architecture Search' (https://arxiv.org/abs/1712.00559))                                           │        5.1M        │    ?     │   25.8    │    8.1    │2018│
│                              AmoebaNet-C ('Regularized Evolution for Image Classifier Architecture Search' (https://arxiv.org/abs/1802.01548))                              │        5.1M        │    ?     │   24.3    │    7.6    │2018│
│                                MnasNet ('MnasNet: Platform-Aware Neural Architecture Search for Mobile' (https://arxiv.org/abs/1807.11626))                                 │     4,308,816      │ 317.67M  │   31.58   │   11.74   │2018│
│                         IBN-Net50-a ('Two at Once: Enhancing Learning andGeneralization Capacities via IBN-Net' (https://arxiv.org/abs/1807.09441))                         │         ?          │    ?     │   22.54   │   6.32    │2018│
│              MarginNet ('Large Margin Deep Networks for Classification' (http://papers.nips.cc/paper/7364-large-margin-deep-networks-for-classification.pdf))               │         ?          │    ?     │   22.0    │     ?     │2018│
│                          A^2 Net ('A^2-Nets: Double Attention Networks' (http://papers.nips.cc/paper/7318-a2-nets-double-attention-networks.pdf))                           │         ?          │    ?     │   23.0    │    6.5    │2018│
│                                        FishNeXt-150 ('FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction'                                          │       26.2M        │    ?     │   21.5    │     ?     │2018│
│                              (http://papers.nips.cc/paper/7356-fishnet-a-versatile-backbone-for-image-region-and-pixel-level-prediction.pdf))                               │                    │          │           │           │    │
│     Shape-ResNet ('IMAGENET-TRAINED CNNS ARE BIASED TOWARDS TEXTURE; INCREASING SHAPE BIAS IMPROVES ACCURACY AND ROBUSTNESS' (https://arxiv.org/pdf/1811.12231v2.pdf))      │       25.5M        │    ?     │   23.28   │   6.72    │2019│
│                                SimCNN(k=3 train) ('Greedy Layerwise Learning Can Scale to ImageNet' (https://arxiv.org/pdf/1812.11446.pdf))                                 │         ?          │    ?     │   28.4    │   10.2    │2019│
│                                                SKNet-50 ('Selective Kernel Networks' (https://arxiv.org/pdf/1903.06586.pdf))                                                │       27.5M        │    ?     │   20.79   │     ?     │2019│
│                     SRM-ResNet-50 ('SRM : A Style-based Recalibration Module for Convolutional Neural Networks' (https://arxiv.org/pdf/1903.10829.pdf))                     │       25.62M       │    ?     │   22.87   │   6.49    │2019│
│             EfficientNet-B0  ('EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks' (http://proceedings.mlr.press/v97/tan19a/tan19a.pdf))              │     5,288,548      │ 414.31M  │   24.77   │   7.52    │2019│
│             EfficientNet-B7b ('EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks' (http://proceedings.mlr.press/v97/tan19a/tan19a.pdf))              │     66,347,960     │39,010.98M│   15.94   │   3.22    │2019│
│                     ProxylessNAS ('PROXYLESSNAS: DIRECT NEURAL ARCHITECTURE SEARCH ON TARGET TASK AND HARDWARE' (https://arxiv.org/pdf/1812.00332.pdf))                     │         ?          │    ?     │   24.9    │    7.5    │2019│
│                                       MixNet-L ('MixNet: Mixed Depthwise Convolutional Kernels' ( https://arxiv.org/abs/1907.09595))                                        │        7.3M        │    ?     │   21.1    │    5.8    │2019│
│                     ECA-Net50 ('ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks' (https://arxiv.org/pdf/1910.03151v1.pdf))                      │       24.37M       │  3.86G   │   22.52   │   6.32    │2019│
│                     ECA-Net101 ('ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks' (https://arxiv.org/pdf/1910.03151v1.pdf))                     │        7.3M        │  7.35G   │   21.35   │   5.66    │2019│
│            ACNet-Densenet121 ('ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks' (https://arxiv.org/abs/1908.03930))            │         ?          │    ?     │   24.18   │   7.23    │2019│
│                                          LIP-ResNet-50 ('LIP: Local Importance-based Pooling' (https://arxiv.org/abs/1908.04156))                                           │       23.9M        │  5.33G   │   21.81   │   6.04    │2019│
│                                          LIP-ResNet-101 ('LIP: Local Importance-based Pooling' (https://arxiv.org/abs/1908.04156))                                          │       42.9M        │  9.06G   │   20.67   │   5.40    │2019│
│                                       LIP-DenseNet-BC-121 ('LIP: Local Importance-based Pooling' (https://arxiv.org/abs/1908.04156))                                        │        8.7M        │  4.13G   │   23.36   │   6.84    │2019│
│                                              MuffNet_1.0 ('MuffNet: Multi-Layer Feature Federation for Mobile Deep Learning'                                                │        2.3M        │   146M   │   30.1    │     ?     │2019│
│          (http://openaccess.thecvf.com/content_ICCVW_2019/papers/CEFRL/Chen_MuffNet_Multi-Layer_Feature_Federation_for_Mobile_Deep_Learning_ICCVW_2019_paper.pdf))          │                    │          │           │           │    │
│                                              MuffNet_1.5 ('MuffNet: Multi-Layer Feature Federation for Mobile Deep Learning'                                                │        3.4M        │   300M   │   26.9    │     ?     │2019│
│          (http://openaccess.thecvf.com/content_ICCVW_2019/papers/CEFRL/Chen_MuffNet_Multi-Layer_Feature_Federation_for_Mobile_Deep_Learning_ICCVW_2019_paper.pdf))          │                    │          │           │           │    │
│                                 ResNet-34-Bin-5 ('Making Convolutional Networks Shift-Invariant Again' (https://arxiv.org/abs/1904.11486))                                  │       21.8M        │3,672.68M │   25.80   │     ?     │2019│
│                                 ResNet-50-Bin-5 ('Making Convolutional Networks Shift-Invariant Again' (https://arxiv.org/abs/1904.11486))                                  │       25.5M        │3,877.95M │   22.96   │     ?     │2019│
│                                MobileNetV2-Bin-5 ('Making Convolutional Networks Shift-Invariant Again' (https://arxiv.org/abs/1904.11486))                                 │     3,504,960      │ 329.36M  │   27.50   │     ?     │2019│
│                                  FixRes ResNeXt101 WSL ('Fixing the train-test resolution discrepancy' (https://arxiv.org/abs/1906.06423))                                  │        829M        │    ?     │   13.6    │    2.0    │2019│
│                         Noisy Student(L2) ('Self-training with Noisy Student improves ImageNet classification' (https://arxiv.org/abs/1911.04252))                          │        480M        │    ?     │   12.6    │    1.8    │2019│
│                                    TResNet-M ('TResNet: High Performance GPU-Dedicated Architecture' (https://arxiv.org/abs/2003.13630))                                    │       29.4M        │   5.5G   │   19.3    │     ?     │2020│
│                           DA-NAS-C ('DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search' (https://arxiv.org/abs/2003.12563v1))                           │         ?          │   467M   │   23.8    │     ?     │2020│
│                                             ResNeSt-50 ('ResNeSt: Split-Attention Networks' (https://arxiv.org/abs/2004.08955))                                             │       27.5M        │  5.39G   │   18.87   │     ?     │2020│
│                                            ResNeSt-101 ('ResNeSt: Split-Attention Networks' (https://arxiv.org/abs/2004.08955))                                             │       48.3M        │  10.2G   │   17.73   │     ?     │2020│
│                                      ResNet-50-FReLU ('Funnel Activation for Visual Recognition' (https://arxiv.org/abs/2007.11824v2))                                      │       25.5M        │  3.87G   │   22.40   │     ?     │2020│
│                                     ResNet-101-FReLU ('Funnel Activation for Visual Recognition' (https://arxiv.org/abs/2007.11824v2))                                      │       44.5M        │   7.6G   │   22.10   │     ?     │2020│
│               ResNet-50-MEALv2 ('MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks' (https://arxiv.org/abs/2009.08453v1))               │       25.6M        │    ?     │   19.33   │   4.91    │2020│
│          ResNet-50-MEALv2 + CutMix ('MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks' (https://arxiv.org/abs/2009.08453v1))           │       25.6M        │    ?     │   19.02   │   4.65    │2020│
│          MobileNet V3-Large-MEALv2 ('MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks' (https://arxiv.org/abs/2009.08453v1))           │       5.48M        │    ?     │   23.08   │   6.68    │2020│
│            EfficientNet-B0-MEALv2 ('MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks' (https://arxiv.org/abs/2009.08453v1))            │       5.29M        │    ?     │   21.71   │   6.05    │2020│
│                       T2T-ViT-7  ('Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet' (https://arxiv.org/abs/2101.11986v1))                        │        4.2M        │   0.6G   │   28.8    │     ?     │2021│
│                       T2T-ViT-14 ('Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet' (https://arxiv.org/abs/2101.11986v1))                        │       19.4M        │   4.8G   │   19.4    │     ?     │2021│
│                       T2T-ViT-19 ('Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet' (https://arxiv.org/abs/2101.11986v1))                        │       39.0M        │   8.0G   │   18.8    │     ?     │2021│
│                            NFNet-F0 ('High-Performance Large-Scale Image Recognition Without Normalization' (https://arxiv.org/abs/2102.06171))                             │       71.5M        │  12.38G  │   16.4    │    3.2    │2021│
│                            NFNet-F1 ('High-Performance Large-Scale Image Recognition Without Normalization' (https://arxiv.org/abs/2102.06171))                             │       132.6M       │  35.54G  │   15.4    │    2.9    │2021│
│                          NFNet-F6+SAM ('High-Performance Large-Scale Image Recognition Without Normalization' (https://arxiv.org/abs/2102.06171))                           │       438.4M       │ 377.28G  │   13.5    │    2.1    │2021│
│                                 EfficientNetV2-S ('EfficientNetV2: Smaller Models and Faster Training' (https://arxiv.org/abs/2104.00298))                                  │        24M         │   8.8G   │   16.1    │     ?     │2021│
│                                 EfficientNetV2-M ('EfficientNetV2: Smaller Models and Faster Training' (https://arxiv.org/abs/2104.00298))                                  │        55M         │   24G    │   14.9    │     ?     │2021│
│                                 EfficientNetV2-L ('EfficientNetV2: Smaller Models and Faster Training' (https://arxiv.org/abs/2104.00298))                                  │        121M        │   53G    │   14.3    │     ?     │2021│
│                              EfficientNetV2-S (21k) ('EfficientNetV2: Smaller Models and Faster Training' (https://arxiv.org/abs/2104.00298))                               │        24M         │   8.8G   │   15.0    │     ?     │2021│
│                              EfficientNetV2-M (21k) ('EfficientNetV2: Smaller Models and Faster Training' (https://arxiv.org/abs/2104.00298))                               │        55M         │   24G    │   13.9    │     ?     │2021│
│                              EfficientNetV2-L (21k) ('EfficientNetV2: Smaller Models and Faster Training' (https://arxiv.org/abs/2104.00298))                               │        121M        │   53G    │   13.2    │     ?     │2021│


Segmentation models

│                                                                     Model                                                                      │Year│PASCAL-Context│Cityscapes (mIOU)│PASCAL VOC 2012 (mIOU)│COCO Stuff│ADE20K VAL (mIOU)│
├────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┼────┼──────────────┼─────────────────┼──────────────────────┼──────────┼─────────────────┤
│                U-Net ('U-Net: Convolutional Networks for Biomedical Image Segmentation' (https://arxiv.org/pdf/1505.04597.pdf))                │2015│      ?       │        ?        │          ?           │    ?     │        ?        │
│                 DeconvNet ('Learning Deconvolution Network for Semantic Segmentation' (https://arxiv.org/pdf/1505.04366.pdf))                  │2015│      ?       │        ?        │         72.5         │    ?     │        ?        │
│                             ParseNet ('ParseNet: Looking Wider to See Better' (https://arxiv.org/abs/1506.04579))                              │2015│     40.4     │        ?        │         69.8         │    ?     │        ?        │
│       Piecewise ('Efficient piecewise training of deep structured models for semantic segmentation' (https://arxiv.org/abs/1504.01013))        │2015│     43.3     │      71.6       │         78.0         │    ?     │        ?        │
│       SegNet ('SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation' (https://arxiv.org/pdf/1511.00561.pdf))       │2016│      ?       │      56.1       │          ?           │    ?     │        ?        │
│                     FCN ('Fully Convolutional Networks for Semantic Segmentation' (https://arxiv.org/pdf/1605.06211.pdf))                      │2016│     37.8     │      65.3       │         62.2         │   22.7   │      29.39      │
│          ENet ('ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation' (https://arxiv.org/pdf/1606.02147.pdf))          │2016│      ?       │      58.3       │          ?           │    ?     │        ?        │
│                 DilatedNet ('MULTI-SCALE CONTEXT AGGREGATION BY DILATED CONVOLUTIONS' (https://arxiv.org/pdf/1511.07122.pdf))                  │2016│      ?       │        ?        │         67.6         │    ?     │      32.31      │
│                    PixelNet ('PixelNet: Towards a General Pixel-Level Architecture' (https://arxiv.org/pdf/1609.06694.pdf))                    │2016│      ?       │        ?        │         69.8         │    ?     │        ?        │
│    RefineNet ('RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation' (https://arxiv.org/pdf/1611.06612.pdf))    │2016│     47.3     │      73.6       │         83.4         │   33.6   │      40.70      │
│            LRR ('Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation' (https://arxiv.org/pdf/1605.02264.pdf))            │2016│      ?       │      71.8       │         79.3         │    ?     │        ?        │
│          FRRN ('Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes' (https://arxiv.org/pdf/1611.08323.pdf))          │2016│      ?       │      71.8       │          ?           │    ?     │        ?        │
│            MultiNet ('MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving' (https://arxiv.org/pdf/1612.07695.pdf))             │2016│      ?       │        ?        │          ?           │    ?     │        ?        │
│          DeepLab ('DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs'            │2017│     45.7     │      64.8       │         79.7         │    ?     │        ?        │
│                                                    (https://arxiv.org/pdf/1606.00915.pdf))                                                     │    │              │                 │                      │          │                 │
│       LinkNet ('LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation' (https://arxiv.org/pdf/1707.03718.pdf))       │2017│      ?       │        ?        │          ?           │    ?     │        ?        │
│  Tiramisu ('The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation' (https://arxiv.org/pdf/1611.09326.pdf))  │2017│      ?       │        ?        │          ?           │    ?     │        ?        │
│              ICNet ('ICNet for Real-Time Semantic Segmentation on High-Resolution Images' (https://arxiv.org/pdf/1704.08545.pdf))              │2017│      ?       │      70.6       │          ?           │    ?     │        ?        │
│   ERFNet ('Efficient ConvNet for Real-time Semantic Segmentation' (http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17iv.pdf))    │2017│      ?       │      68.0       │          ?           │    ?     │        ?        │
│                                PSPNet ('Pyramid Scene Parsing Network' (https://arxiv.org/pdf/1612.01105.pdf))                                 │2017│     47.8     │      80.2       │         85.4         │    ?     │      44.94      │
│      GCN ('Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network' (https://arxiv.org/pdf/1703.02719.pdf))       │2017│      ?       │      76.9       │         82.2         │    ?     │        ?        │
│           Segaware ('Segmentation-Aware Convolutional Networks Using Local Attention Masks' (https://arxiv.org/pdf/1708.04607.pdf))            │2017│      ?       │        ?        │         69.0         │    ?     │        ?        │
│                               PixelDCN ('PIXEL DECONVOLUTIONAL NETWORKS' (https://arxiv.org/pdf/1705.06820.pdf))                               │2017│      ?       │        ?        │         73.0         │    ?     │        ?        │
│               DeepLabv3 ('Rethinking Atrous Convolution for Semantic Image Segmentation' (https://arxiv.org/pdf/1706.05587.pdf))               │2017│      ?       │        ?        │         85.7         │    ?     │        ?        │
│                    DUC, HDC ('Understanding Convolution for Semantic Segmentation' (https://arxiv.org/pdf/1702.08502.pdf))                     │2018│      ?       │      77.1       │          ?           │    ?     │        ?        │
│                   ShuffleSeg ('SHUFFLESEG: REAL-TIME SEMANTIC SEGMENTATION NETWORK' (https://arxiv.org/pdf/1803.03816.pdf))                    │2018│      ?       │      59.3       │          ?           │    ?     │        ?        │
│           AdaptSegNet ('Learning to Adapt Structured Output Space for Semantic Segmentation' (https://arxiv.org/pdf/1802.10349.pdf))           │2018│      ?       │      46.7       │          ?           │    ?     │        ?        │
│                  TuSimple-DUC ('Understanding Convolution for Semantic Segmentation' (https://arxiv.org/pdf/1702.08502.pdf))                   │2018│     80.1     │        ?        │         83.1         │    ?     │        ?        │
│              R2U-Net ('Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation'                │2018│      ?       │        ?        │          ?           │    ?     │        ?        │
│                                                    (https://arxiv.org/pdf/1802.06955.pdf))                                                     │    │              │                 │                      │          │                 │
│              Attention U-Net ('Attention U-Net: Learning Where to Look for the Pancreas' (https://arxiv.org/pdf/1804.03999.pdf))               │2018│      ?       │        ?        │          ?           │    ?     │        ?        │
│                         DANet ('Dual Attention Network for Scene Segmentation' (https://arxiv.org/pdf/1809.02983.pdf))                         │2018│     52.6     │      81.5       │          ?           │   39.7   │        ?        │
│                            ENCNet ('Context Encoding for Semantic Segmentation' (https://arxiv.org/abs/1803.08904))                            │2018│     51.7     │      75.8       │         85.9         │    ?     │      44.65      │
│                        ShelfNet ('ShelfNet for Real-time Semantic Segmentation' (https://arxiv.org/pdf/1811.11254.pdf))                        │2018│     48.4     │      75.8       │         84.2         │    ?     │        ?        │
│       LadderNet ('LADDERNET: MULTI-PATH NETWORKS BASED ON U-NET FOR MEDICAL IMAGE SEGMENTATION' (https://arxiv.org/pdf/1810.07810.pdf))        │2018│      ?       │        ?        │          ?           │    ?     │        ?        │
│     CCC-ERFnet ('Concentrated-Comprehensive Convolutions for lightweight semantic segmentation' (https://arxiv.org/pdf/1812.04920v1.pdf))      │2018│      ?       │      69.01      │          ?           │    ?     │        ?        │
│                                       DifNet-101 ('DifNet: Semantic Segmentation by Diffusion Networks'                                        │2018│     45.1     │        ?        │         73.2         │    ?     │        ?        │
│                           (http://papers.nips.cc/paper/7435-difnet-semantic-segmentation-by-diffusion-networks.pdf))                           │    │              │                 │                      │          │                 │
│     BiSeNet(Res18) ('BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation' (https://arxiv.org/pdf/1808.00897.pdf))      │2018│      ?       │        ?        │         74.7         │   28.1   │        ?        │
│     ESPNet ('ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation' (https://arxiv.org/pdf/1803.06815.pdf))      │2018│      ?       │        ?        │        63.01         │    ?     │        ?        │
│                SPADE ('Semantic Image Synthesis with Spatially-Adaptive Normalization' (https://arxiv.org/pdf/1903.07291.pdf))                 │2019│      ?       │      62.3       │          ?           │   37.4   │      38.5       │
│                              SeamlessSeg ('Seamless Scene Segmentation' (https://arxiv.org/pdf/1905.01220v1.pdf))                              │2019│      ?       │      77.5       │          ?           │    ?     │        ?        │
│            EMANet ('Expectation-Maximization Attention Networks for Semantic Segmentation' (https://arxiv.org/pdf/1907.13426.pdf))             │2019│      ?       │        ?        │         88.2         │   39.9   │        ?        │

Detection models

│                                                                                      Model                                                                                       │Year│VOC07 (mAP@IoU=0.5)│VOC12 (mAP@IoU=0.5)│COCO (mAP)│
├──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┼────┼───────────────────┼───────────────────┼──────────┤
│                         R-CNN ('Rich feature hierarchies for accurate object detection and semantic segmentation' (https://arxiv.org/pdf/1311.2524.pdf))                         │2014│       58.5        │         ?         │    ?     │
│                   OverFeat ('OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks' (https://arxiv.org/pdf/1312.6229.pdf))                   │2014│         ?         │         ?         │    ?     │
│                                                        MultiBox ('Scalable Object Detection using Deep Neural Networks'                                                          │2014│       29.0        │         ?         │    ?     │
│                             (https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Erhan_Scalable_Object_Detection_2014_CVPR_paper.pdf))                             │    │                   │                   │          │
│                         SPP-Net ('Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition' (https://arxiv.org/pdf/1406.4729.pdf))                          │2014│       59.2        │         ?         │    ?     │
│                                              MR-CNN ('Object detection via a multi-region & semantic segmentation-aware CNN model'                                               │2015│       78.2        │       73.9        │    ?     │
│                              (https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Gidaris_Object_Detection_via_ICCV_2015_paper.pdf))                               │    │                   │                   │          │
│                         AttentionNet ('AttentionNet: Aggregating Weak Directions for Accurate Object Detection' (https://arxiv.org/pdf/1506.07704.pdf))                          │2015│         ?         │         ?         │    ?     │
│                                                         Fast R-CNN ('Fast R-CNN' (https://arxiv.org/pdf/1504.08083.pdf))                                                         │2015│       70.0        │       68.4        │    ?     │
│                                          Fast R-CNN ('Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks'                                            │2015│       73.2        │       70.4        │   36.8   │
│                              (https://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf))                              │    │                   │                   │          │
│                                    YOLO v1 ('You Only Look Once: Unified, Real-Time Object Detection' (https://arxiv.org/pdf/1506.02640.pdf))                                    │2016│       66.4        │       57.9        │    ?     │
│                                         G-CNN ('G-CNN: an Iterative Grid Based Object Detector' (https://arxiv.org/pdf/1512.07729.pdf))                                          │2016│       66.8        │       66.4        │    ?     │
│                                  AZNet ('Adaptive Object Detection Using Adjacency and Zoom Prediction' (https://arxiv.org/pdf/1512.07711.pdf))                                  │2016│       70.4        │         ?         │   22.3   │
│                 ION ('Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks' (https://arxiv.org/pdf/1512.04143.pdf))                  │2016│       80.1        │       77.9        │   33.1   │
│                       HyperNet ('HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection' (https://arxiv.org/pdf/1604.00600.pdf))                       │2016│       76.3        │       71.4        │    ?     │
│                              OHEM ('Training Region-based Object Detectors with Online Hard Example Mining' (https://arxiv.org/pdf/1604.03540.pdf))                              │2016│       78.9        │       76.3        │   22.4   │
│                                             MPN ('A MultiPath Network for Object Detection' (https://arxiv.org/pdf/1604.02135.pdf))                                              │2016│         ?         │         ?         │   33.2   │
│                                                SSD ('SSD: Single Shot MultiBox Detector' (https://arxiv.org/pdf/1512.02325.pdf))                                                 │2016│       76.8        │       74.9        │   31.2   │
│                                             GBDNet ('Crafting GBD-Net for Object Detection' (https://arxiv.org/pdf/1610.02579.pdf))                                              │2016│       77.2        │         ?         │   27.0   │
│                    CPF ('Contextual Priming and Feedback for Faster R-CNN' (https://pdfs.semanticscholar.org/40e7/4473cb82231559cbaeaa44989e9bbfe7ec3f.pdf))                     │2016│       76.4        │       72.6        │    ?     │
│                       MS-CNN  ('A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection' (https://arxiv.org/pdf/1607.07155.pdf))                       │2016│         ?         │         ?         │    ?     │
│                              R-FCN ('R-FCN: Object Detection via Region-based Fully Convolutional Networks' (https://arxiv.org/pdf/1605.06409.pdf))                              │2016│       79.5        │       77.6        │   29.9   │
│                          PVANET ('PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection' (https://arxiv.org/pdf/1608.08021.pdf))                           │2016│         ?         │         ?         │    ?     │
│                       DeepID-Net ('DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection' (https://arxiv.org/pdf/1412.5661.pdf))                        │2016│       69.0        │         ?         │    ?     │
│                                      NoC ('Object Detection Networks on Convolutional Feature Maps' (https://arxiv.org/pdf/1504.06066.pdf))                                      │2016│       71.6        │       68.8        │   27.2   │
│                                           DSSD ('DSSD : Deconvolutional Single Shot Detector' (https://arxiv.org/pdf/1701.06659.pdf))                                            │2017│       81.5        │       80.0        │    ?     │
│                                 TDM ('Beyond Skip Connections: Top-Down Modulation for Object Detection' (https://arxiv.org/pdf/1612.06851.pdf))                                 │2017│         ?         │         ?         │   37.3   │
│          FPN ('Feature Pyramid Networks for Object Detection' (http://openaccess.thecvf.com/content_cvpr_2017/papers/Lin_Feature_Pyramid_Networks_CVPR_2017_paper.pdf))          │2017│         ?         │         ?         │   36.2   │
│                                              YOLO v2 ('YOLO9000: Better, Faster, Stronger' (https://arxiv.org/pdf/1612.08242.pdf))                                               │2017│       78.6        │       73.4        │   21.6   │
│                            RON ('RON: Reverse Connection with Objectness Prior Networks for Object Detection' (https://arxiv.org/pdf/1707.01691.pdf))                            │2017│       77.6        │       75.4        │    ?     │
│           DCN ('Deformable Convolutional Networks' (http://openaccess.thecvf.com/content_ICCV_2017/papers/Dai_Deformable_Convolutional_Networks_ICCV_2017_paper.pdf))            │2017│         ?         │         ?         │    ?     │
│                            DeNet ('DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling' (https://arxiv.org/pdf/1703.10295.pdf))                             │2017│       77.1        │       73.9        │   33.8   │
│                         CoupleNet ('CoupleNet: Coupling Global Structure with Local Parts for Object Detection' (https://arxiv.org/pdf/1708.02863.pdf))                          │2017│       82.7        │       80.4        │   34.4   │
│                                            RetinaNet ('Focal Loss for Dense Object Detection' (https://arxiv.org/pdf/1708.02002.pdf))                                            │2017│         ?         │         ?         │   39.1   │
│                               Mask R-CNN ('Mask R-CNN' (http://openaccess.thecvf.com/content_ICCV_2017/papers/He_Mask_R-CNN_ICCV_2017_paper.pdf))                                │2017│         ?         │         ?         │   39.8   │
│                                  DSOD ('DSOD: Learning Deeply Supervised Object Detectors from Scratch' (https://arxiv.org/pdf/1708.01241.pdf))                                  │2017│       77.7        │       76.3        │    ?     │
│       SMN ('Spatial Memory for Context Reasoning in Object Detection' (http://openaccess.thecvf.com/content_ICCV_2017/papers/Chen_Spatial_Memory_for_ICCV_2017_paper.pdf))       │2017│       70.0        │         ?         │    ?     │
│                                       YOLO v3 ('YOLOv3: An Incremental Improvement' (https://pjreddie.com/media/files/papers/YOLOv3.pdf))                                        │2018│         ?         │         ?         │   33.0   │
│                                   SIN ('Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships'                                    │2018│       76.0        │       73.1        │   23.2   │
│                                     (http://openaccess.thecvf.com/content_cvpr_2018/papers/Liu_Structure_Inference_Net_CVPR_2018_paper.pdf))                                     │    │                   │                   │          │
│       STDN ('Scale-Transferrable Object Detection' (http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhou_Scale-Transferrable_Object_Detection_CVPR_2018_paper.pdf))        │2018│       80.9        │         ?         │    ?     │
│                                                     RefineDet ('Single-Shot Refinement Neural Network for Object Detection'                                                      │2018│       83.8        │       83.5        │   41.8   │
│                                 (http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Single-Shot_Refinement_Neural_CVPR_2018_paper.pdf))                                 │    │                   │                   │          │
│              MegDet ('MegDet: A Large Mini-Batch Object Detector' (http://openaccess.thecvf.com/content_cvpr_2018/papers/Peng_MegDet_A_Large_CVPR_2018_paper.pdf))               │2018│         ?         │         ?         │    ?     │
│                                RFBNet ('Receptive Field Block Net for Accurate and Fast Object Detection' (https://arxiv.org/pdf/1711.07767.pdf))                                │2018│       82.2        │         ?         │    ?     │
│                                      CornerNet ('CornerNet: Detecting Objects as Paired Keypoints' (https://arxiv.org/pdf/1808.01244.pdf))                                       │2018│         ?         │         ?         │   42.1   │
│                             LibraRetinaNet ('Libra R-CNN: Towards Balanced Learning for Object Detection' (https://arxiv.org/pdf/1904.02701v1.pdf))                              │2019│         ?         │         ?         │   43.0   │
│                                          YOLACT-700 ('YOLACT Real-time Instance Segmentation' (https://arxiv.org/pdf/1904.02689v1.pdf))                                          │2019│         ?         │         ?         │   31.2   │
│                                     DetNASNet(3.8) ('DetNAS: Backbone Search for Object Detection' (https://arxiv.org/pdf/1903.10979v2.pdf))                                     │2019│         ?         │         ?         │   42.0   │
│                                     YOLOv4 ('YOLOv4: Optimal Speed and Accuracy of Object Detection' (https://arxiv.org/pdf/2004.10934.pdf))                                     │2020│         ?         │         ?         │   46.7   │
│                                             SOLO ('SOLO: Segmenting Objects by Locations' (https://arxiv.org/pdf/1912.04488v3.pdf))                                              │2020│         ?         │         ?         │   37.8   │
│                                            D-SOLO ('SOLO: Segmenting Objects by Locations' (https://arxiv.org/pdf/1912.04488v3.pdf))                                             │2020│         ?         │         ?         │   40.5   │
│                             SNIPER ('Scale Normalized Image Pyramids with AutoFocus for Object Detection' (https://arxiv.org/pdf/2102.05646v1.pdf))                              │2021│       86.6        │         ?         │   47.9   │
│                            AutoFocus ('Scale Normalized Image Pyramids with AutoFocus for Object Detection' (https://arxiv.org/pdf/2102.05646v1.pdf))                            │2021│       85.8        │         ?         │   47.9   │