# 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'](http://papers.nips.cc/paper/7356-fishnet-a-versatile-backbone-for-image-region-and-pixel-level-prediction.pdf))                                   |        26.2M         |         ?       |        21.5      |       ?        |  2018 |
| 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'](http://openaccess.thecvf.com/content_ICCVW_2019/papers/CEFRL/Chen_MuffNet_Multi-Layer_Feature_Federation_for_Mobile_Deep_Learning_ICCVW_2019_paper.pdf))      |         2.3M         |         146M    |        30.1      |       ?        |  2019 |
| MuffNet_1.5 (['MuffNet: Multi-Layer Feature Federation for Mobile Deep Learning'](http://openaccess.thecvf.com/content_ICCVW_2019/papers/CEFRL/Chen_MuffNet_Multi-Layer_Feature_Federation_for_Mobile_Deep_Learning_ICCVW_2019_paper.pdf))      |         3.4M         |         300M    |        26.9      |       ?        |  2019 |
| 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'](https://arxiv.org/pdf/1606.00915.pdf))                                                                             | 2017  |        45.7         |        64.8         |           79.7         |       ?      |            ?           |
| 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'](https://arxiv.org/pdf/1802.06955.pdf))                                                                                     | 2018  |          ?          |          ?          |             ?          |       ?      |            ?           |
| 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'](http://papers.nips.cc/paper/7435-difnet-semantic-segmentation-by-diffusion-networks.pdf))                                                                                   | 2018  |        45.1         |          ?          |           73.2         |       ?      |            ?           |
| 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'](https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Erhan_Scalable_Object_Detection_2014_CVPR_paper.pdf))                                                      | 2014  |        29.0         |          ?          |      ?     |
| 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'](https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Gidaris_Object_Detection_via_ICCV_2015_paper.pdf))                                    | 2015  |        78.2         |        73.9         |      ?     |
| 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'](https://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf))                            | 2015  |        73.2         |        70.4         |    36.8    |
| 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'](http://openaccess.thecvf.com/content_cvpr_2018/papers/Liu_Structure_Inference_Net_CVPR_2018_paper.pdf))                           | 2018  |        76.0         |        73.1         |    23.2    |
| 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'](http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Single-Shot_Refinement_Neural_CVPR_2018_paper.pdf))                                                       | 2018  |        83.8         |        83.5         |    41.8    |
| 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    |

[computervisionmodels.md Github](https://github.com/nerox8664/awesome-computer-vision-models
)
