# 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’) | 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’) | 1,531,936 | 148.13M | 34.93 | 13.89 | 2017
DiracNetV2-18 (‘DiracNets: Training Very Deep Neural Networks Without Skip-Connections’) | 11,511,784 | 1,796.62M | 31.47 | 11.70 | 2017
DiracNetV2-34 (‘DiracNets: Training Very Deep Neural Networks Without Skip-Connections’) | 21,616,232 | 3,646.93M | 28.75 | 9.93 | 2017
SENet-16 (‘Squeeze-and-Excitation Networks’) | 31,366,168 | 5,081.30M | 25.65 | 8.20 | 2017
SENet-154 (‘Squeeze-and-Excitation Networks’) | 115,088,984 | 20,745.78M | 18.62 | 4.61 | 2017
MobileNet (‘MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications’) | 4,231,976 | 579.80M | 26.61 | 8.95 | 2017
NASNet-A 4@1056 (‘Learning Transferable Architectures for Scalable Image Recognition’) | 5,289,978 | 584.90M | 25.68 | 8.16 | 2017
NASNet-A 6@4032(‘Learning Transferable Architectures for Scalable Image Recognition’) | 88,753,150 | 23,976.44M | 18.14 | 4.21 | 2017
DLA-34 (‘Deep Layer Aggregation’) | 15,742,104 | 3,071.37M | 25.36 | 7.94 | 2017
AirNet50-1x64d (r=2) (‘Attention Inspiring Receptive-Fields Network for Learning Invariant Representations’) | 27.43M | ? | 22.48 | 6.21 | 2018
BAM-ResNet-50 (‘BAM: Bottleneck Attention Module’) | 25.92M | ? | 23.68 | 6.96 | 2018
CBAM-ResNet-50 (‘CBAM: Convolutional Block Attention Module’) | 28.1M | ? | 23.02 | 6.38 | 2018
1.0-SqNxt-23v5 (‘SqueezeNext: Hardware-Aware Neural Network Design’) | 921,816 | 285.82M | 40.77 | 17.85 | 2018
1.5-SqNxt-23v5 (‘SqueezeNext: Hardware-Aware Neural Network Design’) | 1,953,616 | 550.97M | 33.81 | 13.01 | 2018
2.0-SqNxt-23v5 (‘SqueezeNext: Hardware-Aware Neural Network Design’) | 3,366,344 | 897.60M | 29.63 | 10.66 | 2018
ShuffleNetV2 (‘ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design’) | 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’) | 5.3M | ? | 28.4 | 9.8 | 2018
FD-MobileNet (‘FD-MobileNet: Improved MobileNet with A Fast Downsampling Strategy’) | 2,901,288 | 147.46M | 34.23 | 13.38 | 2018
MobileNetV2 (‘MobileNetV2: Inverted Residuals and Linear Bottlenecks’) | 3,504,960 | 329.36M | 26.97 | 8.87 | 2018
IGCV3 (‘IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks’) | 3.5M | ? | 28.22 | 9.54 | 2018
DARTS (‘DARTS: Differentiable Architecture Search’) | 4.9M | ? | 26.9 | 9.0 | 2018
PNASNet-5 (‘Progressive Neural Architecture Search’) | 5.1M | ? | 25.8 | 8.1 | 2018
AmoebaNet-C (‘Regularized Evolution for Image Classifier Architecture Search’) | 5.1M | ? | 24.3 | 7.6 | 2018
MnasNet (‘MnasNet: Platform-Aware Neural Architecture Search for Mobile’) | 4,308,816 | 317.67M | 31.58 | 11.74 | 2018
IBN-Net50-a (‘Two at Once: Enhancing Learning andGeneralization Capacities via IBN-Net’) | ? | ? | 22.54 | 6.32 | 2018
MarginNet (‘Large Margin Deep Networks for Classification’) | ? | ? | 22.0 | ? | 2018
A^2 Net (‘A^2-Nets: Double Attention Networks’) | ? | ? | 23.0 | 6.5 | 2018
FishNeXt-150 (‘FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction’) | 26.2M | ? | 21.5 | ? | 2018
Shape-ResNet (‘IMAGENET-TRAINED CNNS ARE BIASED TOWARDS TEXTURE; INCREASING SHAPE BIAS IMPROVES ACCURACY AND ROBUSTNESS’) | 25.5M | ? | 23.28 | 6.72 | 2019
SimCNN(k=3 train) (‘Greedy Layerwise Learning Can Scale to ImageNet’) | ? | ? | 28.4 | 10.2 | 2019
SKNet-50 (‘Selective Kernel Networks’) | 27.5M | ? | 20.79 | ? | 2019
SRM-ResNet-50 (‘SRM : A Style-based Recalibration Module for Convolutional Neural Networks’) | 25.62M | ? | 22.87 | 6.49 | 2019
EfficientNet-B0 (‘EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks’) | 5,288,548 | 414.31M | 24.77 | 7.52 | 2019
EfficientNet-B7b (‘EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks’) | 66,347,960 | 39,010.98M | 15.94 | 3.22 | 2019
ProxylessNAS (‘PROXYLESSNAS: DIRECT NEURAL ARCHITECTURE SEARCH ON TARGET TASK AND HARDWARE’) | ? | ? | 24.9 | 7.5 | 2019
MixNet-L (‘MixNet: Mixed Depthwise Convolutional Kernels’) | 7.3M | ? | 21.1 | 5.8 | 2019
ECA-Net50 (‘ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks’) | 24.37M | 3.86G | 22.52 | 6.32 | 2019
ECA-Net101 (‘ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks’) | 7.3M | 7.35G | 21.35 | 5.66 | 2019
ACNet-Densenet121 (‘ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks’) | ? | ? | 24.18 | 7.23 | 2019
LIP-ResNet-50 (‘LIP: Local Importance-based Pooling’) | 23.9M | 5.33G | 21.81 | 6.04 | 2019
LIP-ResNet-101 (‘LIP: Local Importance-based Pooling’) | 42.9M | 9.06G | 20.67 | 5.40 | 2019
LIP-DenseNet-BC-121 (‘LIP: Local Importance-based Pooling’) | 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
MuffNet_1.5 (‘MuffNet: Multi-Layer Feature Federation for Mobile Deep Learning’) | 3.4M | 300M | 26.9 | ? | 2019
ResNet-34-Bin-5 (‘Making Convolutional Networks Shift-Invariant Again’) | 21.8M | 3,672.68M | 25.80 | ? | 2019
ResNet-50-Bin-5 (‘Making Convolutional Networks Shift-Invariant Again’) | 25.5M | 3,877.95M | 22.96 | ? | 2019
MobileNetV2-Bin-5 (‘Making Convolutional Networks Shift-Invariant Again’) | 3,504,960 | 329.36M | 27.50 | ? | 2019
FixRes ResNeXt101 WSL (‘Fixing the train-test resolution discrepancy’) | 829M | ? | 13.6 | 2.0 | 2019
Noisy Student*(L2) (‘Self-training with Noisy Student improves ImageNet classification’) | 480M | ? | 12.6 | 1.8 | 2019
TResNet-M (‘TResNet: High Performance GPU-Dedicated Architecture’) | 29.4M | 5.5G | 19.3 | ? | 2020
DA-NAS-C (‘DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Search’) | ? | 467M | 23.8 | ? | 2020
ResNeSt-50 (‘ResNeSt: Split-Attention Networks’) | 27.5M | 5.39G | 18.87 | ? | 2020
ResNeSt-101 (‘ResNeSt: Split-Attention Networks’) | 48.3M | 10.2G | 17.73 | ? | 2020
ResNet-50-FReLU (‘Funnel Activation for Visual Recognition’) | 25.5M | 3.87G | 22.40 | ? | 2020
ResNet-101-FReLU (‘Funnel Activation for Visual Recognition’) | 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’) | 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’) | 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’) | 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’) | 5.29M | ? | 21.71 | 6.05 | 2020
T2T-ViT-7 (‘Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet’) | 4.2M | 0.6G | 28.8 | ? | 2021
T2T-ViT-14 (‘Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet’) | 19.4M | 4.8G | 19.4 | ? | 2021
T2T-ViT-19 (‘Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet’) | 39.0M | 8.0G | 18.8 | ? | 2021
NFNet-F0 (‘High-Performance Large-Scale Image Recognition Without Normalization’) | 71.5M | 12.38G | 16.4 | 3.2 | 2021
NFNet-F1 (‘High-Performance Large-Scale Image Recognition Without Normalization’) | 132.6M | 35.54G | 15.4 | 2.9 | 2021
NFNet-F6+SAM (‘High-Performance Large-Scale Image Recognition Without Normalization’) | 438.4M | 377.28G | 13.5 | 2.1 | 2021
EfficientNetV2-S (‘EfficientNetV2: Smaller Models and Faster Training’) | 24M | 8.8G | 16.1 | ? | 2021
EfficientNetV2-M (‘EfficientNetV2: Smaller Models and Faster Training’) | 55M | 24G | 14.9 | ? | 2021
EfficientNetV2-L (‘EfficientNetV2: Smaller Models and Faster Training’) | 121M | 53G | 14.3 | ? | 2021
EfficientNetV2-S (21k) (‘EfficientNetV2: Smaller Models and Faster Training’) | 24M | 8.8G | 15.0 | ? | 2021
EfficientNetV2-M (21k) (‘EfficientNetV2: Smaller Models and Faster Training’) | 55M | 24G | 13.9 | ? | 2021
EfficientNetV2-L (21k) (‘EfficientNetV2: Smaller Models and Faster Training’) | 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’) | 2015 | ? | ? | ? | ? | ?
DeconvNet (‘Learning Deconvolution Network for Semantic Segmentation’) | 2015 | ? | ? | 72.5 | ? | ?
ParseNet (‘ParseNet: Looking Wider to See Better’) | 2015 | 40.4 | ? | 69.8 | ? | ?
Piecewise (‘Efficient piecewise training of deep structured models for semantic segmentation’) | 2015 | 43.3 | 71.6 | 78.0 | ? | ?
SegNet (‘SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation’) | 2016 | ? | 56.1 | ? | ? | ?
FCN (‘Fully Convolutional Networks for Semantic Segmentation’) | 2016 | 37.8 | 65.3 | 62.2 | 22.7 | 29.39
ENet (‘ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation’) | 2016 | ? | 58.3 | ? | ? | ?
DilatedNet (‘MULTI-SCALE CONTEXT AGGREGATION BY DILATED CONVOLUTIONS’) | 2016 | ? | ? | 67.6 | ? | 32.31
PixelNet (‘PixelNet: Towards a General Pixel-Level Architecture’) | 2016 | ? | ? | 69.8 | ? | ?
RefineNet (‘RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation’) | 2016 | 47.3 | 73.6 | 83.4 | 33.6 | 40.70
LRR (‘Laplacian Pyramid Reconstruction and Refinement for Semantic Segmentation’) | 2016 | ? | 71.8 | 79.3 | ? | ?
FRRN (‘Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes’) | 2016 | ? | 71.8 | ? | ? | ?
MultiNet (‘MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving’) | 2016 | ? | ? | ? | ? | ?
DeepLab (‘DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs’) | 2017 | 45.7 | 64.8 | 79.7 | ? | ?
LinkNet (‘LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation’) | 2017 | ? | ? | ? | ? | ?
Tiramisu (‘The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation’) | 2017 | ? | ? | ? | ? | ?
ICNet (‘ICNet for Real-Time Semantic Segmentation on High-Resolution Images’) | 2017 | ? | 70.6 | ? | ? | ?
ERFNet (‘Efficient ConvNet for Real-time Semantic Segmentation’) | 2017 | ? | 68.0 | ? | ? | ?
PSPNet (‘Pyramid Scene Parsing Network’) | 2017 | 47.8 | 80.2 | 85.4 | ? | 44.94
GCN (‘Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network’) | 2017 | ? | 76.9 | 82.2 | ? | ?
Segaware (‘Segmentation-Aware Convolutional Networks Using Local Attention Masks’) | 2017 | ? | ? | 69.0 | ? | ?
PixelDCN (‘PIXEL DECONVOLUTIONAL NETWORKS’) | 2017 | ? | ? | 73.0 | ? | ?
DeepLabv3 (‘Rethinking Atrous Convolution for Semantic Image Segmentation’) | 2017 | ? | ? | 85.7 | ? | ?
DUC, HDC (‘Understanding Convolution for Semantic Segmentation’) | 2018 | ? | 77.1 | ? | ? | ?
ShuffleSeg (‘SHUFFLESEG: REAL-TIME SEMANTIC SEGMENTATION NETWORK’) | 2018 | ? | 59.3 | ? | ? | ?
AdaptSegNet (‘Learning to Adapt Structured Output Space for Semantic Segmentation’) | 2018 | ? | 46.7 | ? | ? | ?
TuSimple-DUC (‘Understanding Convolution for Semantic Segmentation’) | 2018 | 80.1 | ? | 83.1 | ? | ?
R2U-Net (‘Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation’) | 2018 | ? | ? | ? | ? | ?
Attention U-Net (‘Attention U-Net: Learning Where to Look for the Pancreas’) | 2018 | ? | ? | ? | ? | ?
DANet (‘Dual Attention Network for Scene Segmentation’) | 2018 | 52.6 | 81.5 | ? | 39.7 | ?
ENCNet (‘Context Encoding for Semantic Segmentation’) | 2018 | 51.7 | 75.8 | 85.9 | ? | 44.65
ShelfNet (‘ShelfNet for Real-time Semantic Segmentation’) | 2018 | 48.4 | 75.8 | 84.2 | ? | ?
LadderNet (‘LADDERNET: MULTI-PATH NETWORKS BASED ON U-NET FOR MEDICAL IMAGE SEGMENTATION’) | 2018 | ? | ? | ? | ? | ?
CCC-ERFnet (‘Concentrated-Comprehensive Convolutions for lightweight semantic segmentation’) | 2018 | ? | 69.01 | ? | ? | ?
DifNet-101 (‘DifNet: Semantic Segmentation by Diffusion Networks’) | 2018 | 45.1 | ? | 73.2 | ? | ?
BiSeNet(Res18) (‘BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation’) | 2018 | ? | ? | 74.7 | 28.1 | ?
ESPNet (‘ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation’) | 2018 | ? | ? | 63.01 | ? | ?
SPADE (‘Semantic Image Synthesis with Spatially-Adaptive Normalization’) | 2019 | ? | 62.3 | ? | 37.4 | 38.5
SeamlessSeg (‘Seamless Scene Segmentation’) | 2019 | ? | 77.5 | ? | ? | ?
EMANet (‘Expectation-Maximization Attention Networks for Semantic Segmentation’) | 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’) | 2014 | 58.5 | ? | ?
OverFeat (‘OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks’) | 2014 | ? | ? | ?
MultiBox (‘Scalable Object Detection using Deep Neural Networks’) | 2014 | 29.0 | ? | ?
SPP-Net (‘Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition’) | 2014 | 59.2 | ? | ?
MR-CNN (‘Object detection via a multi-region & semantic segmentation-aware CNN model’) | 2015 | 78.2 | 73.9 | ?
AttentionNet (‘AttentionNet: Aggregating Weak Directions for Accurate Object Detection’) | 2015 | ? | ? | ?
Fast R-CNN (‘Fast R-CNN’) | 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
YOLO v1 (‘You Only Look Once: Unified, Real-Time Object Detection’) | 2016 | 66.4 | 57.9 | ?
G-CNN (‘G-CNN: an Iterative Grid Based Object Detector’) | 2016 | 66.8 | 66.4 | ?
AZNet (‘Adaptive Object Detection Using Adjacency and Zoom Prediction’) | 2016 | 70.4 | ? | 22.3
ION (‘Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks’) | 2016 | 80.1 | 77.9 | 33.1
HyperNet (‘HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection’) | 2016 | 76.3 | 71.4 | ?
OHEM (‘Training Region-based Object Detectors with Online Hard Example Mining’) | 2016 | 78.9 | 76.3 | 22.4
MPN (‘A MultiPath Network for Object Detection’) | 2016 | ? | ? | 33.2
SSD (‘SSD: Single Shot MultiBox Detector’) | 2016 | 76.8 | 74.9 | 31.2
GBDNet (‘Crafting GBD-Net for Object Detection’) | 2016 | 77.2 | ? | 27.0
CPF (‘Contextual Priming and Feedback for Faster R-CNN’) | 2016 | 76.4 | 72.6 | ?
MS-CNN (‘A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection’) | 2016 | ? | ? | ?
R-FCN (‘R-FCN: Object Detection via Region-based Fully Convolutional Networks’) | 2016 | 79.5 | 77.6 | 29.9
PVANET (‘PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection’) | 2016 | ? | ? | ?
DeepID-Net (‘DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection’) | 2016 | 69.0 | ? | ?
NoC (‘Object Detection Networks on Convolutional Feature Maps’) | 2016 | 71.6 | 68.8 | 27.2
DSSD (‘DSSD : Deconvolutional Single Shot Detector’) | 2017 | 81.5 | 80.0 | ?
TDM (‘Beyond Skip Connections: Top-Down Modulation for Object Detection’) | 2017 | ? | ? | 37.3
FPN (‘Feature Pyramid Networks for Object Detection’) | 2017 | ? | ? | 36.2
YOLO v2 (‘YOLO9000: Better, Faster, Stronger’) | 2017 | 78.6 | 73.4 | 21.6
RON (‘RON: Reverse Connection with Objectness Prior Networks for Object Detection’) | 2017 | 77.6 | 75.4 | ?
DCN (‘Deformable Convolutional Networks’) | 2017 | ? | ? | ?
DeNet (‘DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling’) | 2017 | 77.1 | 73.9 | 33.8
CoupleNet (‘CoupleNet: Coupling Global Structure with Local Parts for Object Detection’) | 2017 | 82.7 | 80.4 | 34.4
RetinaNet (‘Focal Loss for Dense Object Detection’) | 2017 | ? | ? | 39.1
Mask R-CNN (‘Mask R-CNN’) | 2017 | ? | ? | 39.8
DSOD (‘DSOD: Learning Deeply Supervised Object Detectors from Scratch’) | 2017 | 77.7 | 76.3 | ?
SMN (‘Spatial Memory for Context Reasoning in Object Detection’) | 2017 | 70.0 | ? | ?
YOLO v3 (‘YOLOv3: An Incremental Improvement’) | 2018 | ? | ? | 33.0
SIN (‘Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships’) | 2018 | 76.0 | 73.1 | 23.2
STDN (‘Scale-Transferrable Object Detection’) | 2018 | 80.9 | ? | ?
RefineDet (‘Single-Shot Refinement Neural Network for Object Detection’) | 2018 | 83.8 | 83.5 | 41.8
MegDet (‘MegDet: A Large Mini-Batch Object Detector’) | 2018 | ? | ? | ?
RFBNet (‘Receptive Field Block Net for Accurate and Fast Object Detection’) | 2018 | 82.2 | ? | ?
CornerNet (‘CornerNet: Detecting Objects as Paired Keypoints’) | 2018 | ? | ? | 42.1
LibraRetinaNet (‘Libra R-CNN: Towards Balanced Learning for Object Detection’) | 2019 | ? | ? | 43.0
YOLACT-700 (‘YOLACT Real-time Instance Segmentation’) | 2019 | ? | ? | 31.2
DetNASNet(3.8) (‘DetNAS: Backbone Search for Object Detection’) | 2019 | ? | ? | 42.0
YOLOv4 (‘YOLOv4: Optimal Speed and Accuracy of Object Detection’) | 2020 | ? | ? | 46.7
SOLO (‘SOLO: Segmenting Objects by Locations’) | 2020 | ? | ? | 37.8
D-SOLO (‘SOLO: Segmenting Objects by Locations’) | 2020 | ? | ? | 40.5
SNIPER (‘Scale Normalized Image Pyramids with AutoFocus for Object Detection’) | 2021 | 86.6 | ? | 47.9
AutoFocus (‘Scale Normalized Image Pyramids with AutoFocus for Object Detection’) | 2021 | 85.8 | ? | 47.9