Awesome Computer Vision Models

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

Contents

• Classification models
• Segmentation 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') | 62.3M | 1,132.33M | 40.96 | 18.24 | 2014 | | VGG-16 ('Very Deep Convolutional Networks for Large-Scale Image Recognition') | 138.3M | ? | 26.78 | 8.69 | 2014 | | ResNet-10 ('Deep Residual Learning for Image Recognition') | 5.5M | 894.04M | 34.69 | 14.36 | 2015 | | ResNet-18 ('Deep Residual Learning for Image Recognition') | 11.7M | 1,820.41M | 28.53 | 9.82 | 2015 | | ResNet-34 ('Deep Residual Learning for Image Recognition') | 21.8M | 3,672.68M | 24.84 | 7.80 | 2015 | | ResNet-50 ('Deep Residual Learning for Image Recognition') | 25.5M | 3,877.95M | 22.28 | 6.33 | 2015 | | InceptionV3 ('Rethinking the Inception Architecture for Computer Vision') | 23.8M | ? | 21.2 | 5.6 | 2015 | | PreResNet-18 ('Identity Mappings in Deep Residual Networks') | 11.7M | 1,820.56M | 28.43 | 9.72 | 2016 | | PreResNet-34 ('Identity Mappings in Deep Residual Networks') | 21.8M | 3,672.83M | 24.89 | 7.74 | 2016 | | PreResNet-50 ('Identity Mappings in Deep Residual Networks') | 25.6M | 3,875.44M | 22.40 | 6.47 | 2016 | | DenseNet-121 ('Densely Connected Convolutional Networks') | 8.0M | 2,872.13M | 23.48 | 7.04 | 2016 | | DenseNet-161 ('Densely Connected Convolutional Networks') | 28.7M | 7,793.16M | 22.86 | 6.44 | 2016 | | PyramidNet-101 ('Deep Pyramidal Residual Networks') | 42.5M | 8,743.54M | 21.98 | 6.20 | 2016 | | ResNeXt-14(32x4d) ('Aggregated Residual Transformations for Deep Neural Networks') | 9.5M | 1,603.46M | 30.32 | 11.46 | 2016 | | ResNeXt-26(32x4d) ('Aggregated Residual Transformations for Deep Neural Networks') | 15.4M | 2,488.07M | 24.14 | 7.46 | 2016 | | WRN-50-2 ('Wide Residual Networks') | 68.9M | 11,405.42M | 22.53 | 6.41 | 2016 | | Xception ('Xception: Deep Learning with Depthwise Separable Convolutions') | 22,855,952 | 8,403.63M | 20.97 | 5.49 | 2016 | | InceptionV4 ('Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning') | 42,679,816 | 12,304.93M | 20.64 | 5.29 | 2016 | | InceptionResNetV2 ('Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning') | 55,843,464 | 13,188.64M | 19.93 | 4.90 | 2016 | | PolyNet ('PolyNet: A Pursuit of Structural Diversity in Very Deep Networks') | 95,366,600 | 34,821.34M | 19.10 | 4.52 | 2016 | | DarkNet Ref ('Darknet: Open source neural networks in C') | 7,319,416 | 367.59M | 38.58 | 17.18 | 2016 | | DarkNet Tiny ('Darknet: Open source neural networks in C') | 1,042,104 | 500.85M | 40.74 | 17.84 | 2016 | | DarkNet 53 ('Darknet: Open source neural networks in C') | 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') | 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') | 1,235,496 | 352.02M | 39.31 | 17.72 | 2016 | | ResAttNet-92 ('Residual Attention Network for Image Classification') | 51.3M | ? | 19.5 | 4.8 | 2017 | | CondenseNet (G=C=8) ('CondenseNet: An Efficient DenseNet using Learned Group Convolutions') | 4.8M | ? | 26.2 | 8.3 | 2017 | | DPN-68 (['Dual Path Networks'](https://arxiv.org/