gmalivenko / awesome-computer-vision-models

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

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