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A list of popular deep learning models related to classification, segmentation and detection problems

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Awesome Computer Vision Models Awesome

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

Contents

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