awesome-computer-vision-models
This is a list with popular classification and segmentation models with corresponding evaluation metrics.
You can check some of the models using tensorflow.js demo application .
Model
Number of parameters
FLOPS
Top-1 Error
Top-5 Error
DEMO
AlexNet
62.3M
1,132.33M
40.96
18.24
X
VGG-16
138.3M
?
26.78
8.69
X
ResNet-10
5.5M
894.04M
34.69
14.36
Try live
ResNet-18
11.7M
1,820.41M
28.53
9.82
Try live
ResNet-34
21.8M
3,672.68M
24.84
7.80
Try live
ResNet-50
25.5M
3,877.95M
22.28
6.33
Try live
Inception v3
23.8M
?
21.2
5.6
X
PreResNet-18
11.7M
1,820.56M
28.43
9.72
Try live
PreResNet-34
21.8M
3,672.83M
24.89
7.74
Try live
PreResNet-50
25.6M
3,875.44M
22.40
6.47
Try live
DenseNet-121
8.0M
2,872.13M
23.48
7.04
Try live
DenseNet-161
28.7M
7,793.16M
22.86
6.44
X
PyramidNet-101
42.5M
8,743.54M
21.98
6.20
X
ResNeXt-14(32x4d)
9.5M
1,603.46M
30.32
11.46
Try live
ResNeXt-26(32x4d)
15.4M
2,488.07M
24.14
7.46
Try live
WRN-50-2
68.9M
11,405.42M
22.53
6.41
X
Xception
22,855,952
8,403.63M
20.97
5.49
X
InceptionV4
42,679,816
12,304.93M
20.64
5.29
X
InceptionResNetV2
55,843,464
13,188.64M
19.93
4.90
X
PolyNet
95,366,600
34,821.34M
19.10
4.52
X
InceptionResNetV2
55,843,464
13,188.64M
19.93
4.90
X
DarkNet Ref
7,319,416
367.59M
38.58
17.18
Try live
DarkNet Tiny
1,042,104
500.85M
40.74
17.84
Try live
DarkNet 53
41,609,928
7,133.86M
21.75
5.64
Try live
Attention-92
51.3M
?
19.5
4.8
X
CondenseNet (G=C=8)
4.8M
?
26.2
8.3
X
DPN-68
12,611,602
2,351.84M
23.24
6.79
Try live
ShuffleNet x1.0 (g=1)
1,531,936
148.13M
34.93
13.89
Try live
DiracNetV2-18
11,511,784
1,796.62M
31.47
11.70
Try live
DiracNetV2-34
21,616,232
3,646.93M
28.75
9.93
Try live
SENet-16
31,366,168
5,081.30M
25.65
8.20
Try live
SENet-154
115,088,984
20,745.78M
18.62
4.61
X
MobileNet x1.0
4,231,976
579.80M
26.61
8.95
Try live
NASNet-A 4@1056
5,289,978
584.90M
25.68
8.16
Try live
NASNet-A 6@4032
88,753,150
23,976.44M
18.14
4.21
X
DLA-34
15,742,104
3,071.37M
25.36
7.94
Try live
AirNet50-1x64d (r=2)
27.43M
?
22.48
6.21
X
BAM-ResNet-50
25.92M
?
23.68
6.96
X
CBAM-ResNet-50
28.1M
?
23.02
6.38
X
SqueezeResNet1.1
1,235,496
352.02M
40.09
18.21
Try live
SqueezeNet1.1
1,235,496
352.02M
39.31
17.72
Try live
1.0-SqNxt-23v5
921,816
285.82M
40.77
17.85
X
1.5-SqNxt-23v5
1,953,616
550.97M
33.81
13.01
X
2.0-SqNxt-23v5
3,366,344
897.60M
29.63
10.66
X
ShuffleNetV2 x1.0
2,278,604
149.72M
31.44
11.63
Try live
456-MENet-24×1(g=3)
5.3M
?
28.4
9.8
X
FD-MobileNet x1.0
2,901,288
147.46M
34.23
13.38
Try live
MobileNetV2 x1.0
3,504,960
329.36M
26.97
8.87
Try live
IGCV3
3.5M
?
28.22
9.54
X
DARTS
4.9M
?
26.9
9.0
X
PNASNet-5
5.1M
?
25.8
8.1
X
AmoebaNet-C
5.1M
?
24.3
7.6
X
MnasNet
4,308,816
317.67M
31.58
11.74
Try live
IBN-Net50-a
?
?
22.54
6.32
X
MarginNet
?
?
22.0
?
X
A^2 Net
?
?
23.0
6.5
X
FishNeXt-150
26.2M
?
21.5
?
X
Shape-ResNet
25.5M
?
23.28
6.72
X
ResNet-50-Bin-5
?
?
23.0
?
X
SimCNN(k=3 train)
?
?
28.4
10.2
X
SKNet-50
27.5M
?
20.79
?
X
SRM-ResNet-50
25.62M
?
22.87
6.49
X
EfficientNet-B0
5,288,548
414.31M
24.77
7.52
Try live
EfficientNet-B7b
66,347,960
39,010.98M
15.94
3.22
X
ProxylessNAS
?
?
24.9
7.5
X
MixNet-L
7.3M
?
21.1
5.8
X
ECA-Net50
24.37M
3.86G
22.52
6.32
X
ECA-Net101
7.3M
7.35G
21.35
5.66
X
ACNet-Densenet121
?
?
24.18
7.23
X
LIP-ResNet-50
23.9M
5.33G
21.81
6.04
X
LIP-ResNet-101
42.9M
9.06G
20.67
5.40
X
LIP-DenseNet-BC-121
8.7M
4.13G
23.36
6.84
X
MuffNet_1.0
2.3M
146M
30.1
?
X
MuffNet_1.5
3.4M
300M
26.9
?
X
ResNet-34-Bin-5
21.8M
3,672.68M
25.80
?
X
ResNet-50-Bin-5
25.5M
3,877.95M
22.96
?
X
MobileNetV2-Bin-5
3,504,960
329.36M
27.50
?
X
FixRes ResNeXt101 WSL
829M
?
13.6
2.0
X
Noisy Student*(L2)
480M
?
12.6
1.8
X
Model
PASCAL-Context
Cityscapes (mIOU)
PASCAL VOC 2012 (mIOU)
COCO Stuff
ADE20K VAL (mIOU)
U-Net
?
?
?
?
?
DeconvNet
?
?
72.5
?
?
ParseNet
40.4
?
69.8
?
?
Piecewise
43.3
71.6
78.0
?
?
SegNet
?
56.1
?
?
?
FCN
37.8
65.3
62.2
22.7
29.39
ENet
?
58.3
?
?
?
DilatedNet
?
?
67.6
?
32.31
PixelNet
?
?
69.8
?
?
RefineNet
47.3
73.6
83.4
33.6
40.70
LRR
?
71.8
79.3
?
?
FRRN
?
71.8
?
?
?
MultiNet
?
?
?
?
?
DeepLab
45.7
64.8
79.7
?
?
LinkNet
?
?
?
?
?
Tiramisu
?
?
?
?
?
ICNet
?
70.6
?
?
?
ERFNet
?
68.0
?
?
?
PSPNet
47.8
80.2
85.4
?
44.94
GCN
?
76.9
82.2
?
?
Segaware
?
?
69.0
?
?
PixelDCN
?
?
73.0
?
?
DeepLabv3
?
?
85.7
?
?
DUC, HDC
?
77.1
?
?
?
ShuffleSeg
?
59.3
?
?
?
AdaptSegNet
?
46.7
?
?
?
TuSimple-DUC
80.1
?
83.1
?
?
R2U-Net
?
?
?
?
?
Attention U-Net
?
?
?
?
?
DANet
52.6
81.5
?
39.7
?
ENCNet
51.7
75.8
85.9
?
44.65
ShelfNet
48.4
75.8
84.2
?
?
LadderNet
?
?
?
?
?
CCC-ERFnet
?
69.01
?
?
?
DifNet-101
45.1
?
73.2
?
?
BiSeNet(Res18)
?
?
74.7
28.1
?
ESPNet
?
?
63.01
?
?
SPADE
?
62.3
?
37.4
38.5
SeamlessSeg
?
77.5
?
?
?
EMANet
?
?
88.2
39.9
?
[R-CNN] Rich feature hierarchies for accurate object detection and semantic segmentation | Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik | [CVPR' 14] |[pdf]
[official code - caffe]
[2014]
[OverFeat] OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks | Pierre Sermanet, et al. | [ICLR' 14] |[pdf]
[official code - torch]
[2014]
[MultiBox] Scalable Object Detection using Deep Neural Networks | Dumitru Erhan, et al. | [CVPR' 14] |[pdf]
[2014]
[SPP-Net] Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition | Kaiming He, et al. | [ECCV' 14] |[pdf]
[official code - caffe]
[unofficial code - keras]
[unofficial code - tensorflow]
[2014]
[MR-CNN] Object detection via a multi-region & semantic segmentation-aware CNN model | Spyros Gidaris, Nikos Komodakis | [ICCV' 15] |[pdf]
[official code - caffe]
[2015]
[DeepBox] DeepBox: Learning Objectness with Convolutional Networks | Weicheng Kuo, Bharath Hariharan, Jitendra Malik | [ICCV' 15] |[pdf]
[official code - caffe]
[2015]
[AttentionNet] AttentionNet: Aggregating Weak Directions for Accurate Object Detection | Donggeun Yoo, et al. | [ICCV' 15] |[pdf]
[2015]
[Fast R-CNN] Fast R-CNN | Ross Girshick | [ICCV' 15] |[pdf]
[official code - caffe]
[2015]
[DeepProposal] DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers | Amir Ghodrati, et al. | [ICCV' 15] |[pdf]
[official code - matconvnet]
[2015]
[Faster R-CNN, RPN] Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks | Shaoqing Ren, et al. | [NIPS' 15] |[pdf]
[official code - caffe]
[unofficial code - tensorflow]
[unofficial code - pytorch]
[YOLO v1] You Only Look Once: Unified, Real-Time Object Detection | Joseph Redmon, et al. | [CVPR' 16] |[pdf]
[official code - c]
[2016]
[G-CNN] G-CNN: an Iterative Grid Based Object Detector | Mahyar Najibi, et al. | [CVPR' 16] |[pdf]
[2016]
[AZNet] Adaptive Object Detection Using Adjacency and Zoom Prediction | Yongxi Lu, Tara Javidi. | [CVPR' 16] |[pdf]
[2016]
[ION] Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks | Sean Bell, et al. | [CVPR' 16] |[pdf]
[2016]
[HyperNet] HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection | Tao Kong, et al. | [CVPR' 16] |[pdf]
[2016]
[OHEM] Training Region-based Object Detectors with Online Hard Example Mining | Abhinav Shrivastava, et al. | [CVPR' 16] |[pdf]
[official code - caffe]
[2016]
[CRAPF] CRAFT Objects from Images | Bin Yang, et al. | [CVPR' 16] |[pdf]
[official code - caffe]
[2016]
[MPN] A MultiPath Network for Object Detection | Sergey Zagoruyko, et al. | [BMVC' 16] |[pdf]
[official code - torch]
[2016]
[SSD] SSD: Single Shot MultiBox Detector | Wei Liu, et al. | [ECCV' 16] |[pdf]
[official code - caffe]
[unofficial code - tensorflow]
[unofficial code - pytorch]
[2016]
[GBDNet] Crafting GBD-Net for Object Detection | Xingyu Zeng, et al. | [ECCV' 16] |[pdf]
[official code - caffe]
[2016]
[CPF] Contextual Priming and Feedback for Faster R-CNN | Abhinav Shrivastava and Abhinav Gupta | [ECCV' 16] |[pdf]
[2016]
[MS-CNN] A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection | Zhaowei Cai, et al. | [ECCV' 16] |[pdf]
[official code - caffe]
[2016]
[R-FCN] R-FCN: Object Detection via Region-based Fully Convolutional Networks | Jifeng Dai, et al. | [NIPS' 16] |[pdf]
[official code - caffe]
[unofficial code - caffe]
[2016]
[PVANET] PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection | Kye-Hyeon Kim, et al. | [NIPSW' 16] |[pdf]
[official code - caffe]
[2016]
[DeepID-Net] DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection | Wanli Ouyang, et al. | [PAMI' 16] |[pdf]
[2016]
[NoC] Object Detection Networks on Convolutional Feature Maps | Shaoqing Ren, et al. | [TPAMI' 16] |[pdf]
[DSSD] DSSD : Deconvolutional Single Shot Detector | Cheng-Yang Fu1, et al. | [arXiv' 17] |[pdf]
[official code - caffe]
[2017]
[TDM] Beyond Skip Connections: Top-Down Modulation for Object Detection | Abhinav Shrivastava, et al. | [CVPR' 17] |[pdf]
[2017]
[FPN] Feature Pyramid Networks for Object Detection | Tsung-Yi Lin, et al. | [CVPR' 17] |[pdf]
[unofficial code - caffe]
[2017]
[YOLO v2] YOLO9000: Better, Faster, Stronger | Joseph Redmon, Ali Farhadi | [CVPR' 17] |[pdf]
[official code - c]
[unofficial code - caffe]
[unofficial code - tensorflow]
[unofficial code - tensorflow]
[unofficial code - pytorch]
[2017]
[RON] RON: Reverse Connection with Objectness Prior Networks for Object Detection | Tao Kong, et al. | [CVPR' 17] |[pdf]
[official code - caffe]
[unofficial code - tensorflow]
[2017]
[DCN] Deformable Convolutional Networks | Jifeng Dai, et al. | [ICCV' 17] |[pdf]
[official code - mxnet]
[unofficial code - tensorflow]
[unofficial code - pytorch]
[2017]
[DeNet] DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling | Lachlan Tychsen-Smith, Lars Petersson | [ICCV' 17] |[pdf]
[official code - theano]
[2017]
[CoupleNet] CoupleNet: Coupling Global Structure with Local Parts for Object Detection | Yousong Zhu, et al. | [ICCV' 17] |[pdf]
[official code - caffe]
[2017]
[RetinaNet] Focal Loss for Dense Object Detection | Tsung-Yi Lin, et al. | [ICCV' 17] |[pdf]
[official code - keras]
[unofficial code - pytorch]
[unofficial code - mxnet]
[unofficial code - tensorflow]
[2017]
[Mask R-CNN] Mask R-CNN | Kaiming He, et al. | [ICCV' 17] |[pdf]
[official code - caffe2]
[unofficial code - tensorflow]
[unofficial code - tensorflow]
[unofficial code - pytorch]
[2017]
[DSOD] DSOD: Learning Deeply Supervised Object Detectors from Scratch | Zhiqiang Shen, et al. | [ICCV' 17] |[pdf]
[official code - caffe]
[unofficial code - pytorch]
[2017]
[SMN] Spatial Memory for Context Reasoning in Object Detection | Xinlei Chen, Abhinav Gupta | [ICCV' 17] |[pdf]
[2017]
[YOLO v3] YOLOv3: An Incremental Improvement | Joseph Redmon, Ali Farhadi | [arXiv' 18] |[pdf]
[official code - c]
[unofficial code - pytorch]
[unofficial code - pytorch]
[unofficial code - keras]
[unofficial code - tensorflow]
[2018]
[ZIP] Zoom Out-and-In Network with Recursive Training for Object Proposal | Hongyang Li, et al. | [IJCV' 18] |[pdf]
[official code - caffe]
[2018]
[SIN] Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships | Yong Liu, et al. | [CVPR' 18] |[pdf]
[official code - tensorflow]
[2018]
[STDN] Scale-Transferrable Object Detection | Peng Zhou, et al. | [CVPR' 18] |[pdf]
[RefineDet] Single-Shot Refinement Neural Network for Object Detection | Shifeng Zhang, et al. | [CVPR' 18] |[pdf]
[official code - caffe]
[unofficial code - chainer]
[unofficial code - pytorch]
[2018]
[MegDet] MegDet: A Large Mini-Batch Object Detector | Chao Peng, et al. | [CVPR' 18] |[pdf]
[2018]
[DA Faster R-CNN] Domain Adaptive Faster R-CNN for Object Detection in the Wild | Yuhua Chen, et al. | [CVPR' 18] |[pdf]
[official code - caffe]
[2018]
[SNIP] An Analysis of Scale Invariance in Object Detection – SNIP | Bharat Singh, Larry S. Davis | [CVPR' 18] |[pdf]
[2018]
[Relation-Network] Relation Networks for Object Detection | Han Hu, et al. | [CVPR' 18] |[pdf]
[official code - mxnet]
[2018]
[Cascade R-CNN] Cascade R-CNN: Delving into High Quality Object Detection | Zhaowei Cai, et al. | [CVPR' 18] |[pdf]
[official code - caffe]
[2018]
Finding Tiny Faces in the Wild with Generative Adversarial Network | Yancheng Bai, et al. | [CVPR' 18] |[pdf]
[2018]
[STDnet] STDnet: A ConvNet for Small Target Detection | Brais Bosquet, et al. | [BMVC' 18] |[pdf]
[2018]
[RFBNet] Receptive Field Block Net for Accurate and Fast Object Detection | Songtao Liu, et al. | [ECCV' 18] |[pdf]
[official code - pytorch]
[2018]
Zero-Annotation Object Detection with Web Knowledge Transfer | Qingyi Tao, et al. | [ECCV' 18] |[pdf]
[2018]
[CornerNet] CornerNet: Detecting Objects as Paired Keypoints | Hei Law, et al. | [ECCV' 18] |[pdf]
[official code - pytorch]
[2018]
[Pelee] Pelee: A Real-Time Object Detection System on Mobile Devices | Jun Wang, et al. | [NIPS' 18] |[pdf]
[official code - caffe]
[2018]
[HKRM] Hybrid Knowledge Routed Modules for Large-scale Object Detection | ChenHan Jiang, et al. | [NIPS' 18] |[pdf]
[2018]
[MetaAnchor] MetaAnchor: Learning to Detect Objects with Customized Anchors | Tong Yang, et al. | [NIPS' 18] |[pdf]
[2018]
[M2Det] M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network | Jun Wang, et al. | [AAAI' 19] |[pdf]
[2018]
[Libra RetinaNet] Libra R-CNN: Towards Balanced Learning for Object Detection | Jiangmiao Pang, et al. | [pdf]
[2019]
[YOLACT-700] YOLACT Real-time Instance Segmentation | [pdf]
[2019]
[DetNASNet] DetNAS: Backbone Search for Object Detection | [pdf]
[2019]
Detector
VOC07 (mAP@IoU=0.5)
VOC12 (mAP@IoU=0.5)
COCO (mAP)
R-CNN
58.5
-
-
OverFeat
-
-
-
MultiBox
29.0
-
-
SPP-Net
59.2
-
-
MR-CNN
78.2
73.9
-
AttentionNet
-
-
-
Fast R-CNN
70.0
68.4
-
Faster R-CNN
73.2
70.4
36.8
YOLO v1
66.4
57.9
-
G-CNN
66.8
66.4
-
AZNet
70.4
-
22.3
ION
80.1
77.9
33.1
HyperNet
76.3
71.4
-
OHEM
78.9
76.3
22.4
MPN
-
-
33.2
SSD
76.8
74.9
31.2
GBDNet
77.2
-
27.0
CPF
76.4
72.6
-
MS-CNN
-
-
-
R-FCN
79.5
77.6
29.9
PVANET
-
-
-
DeepID-Net
69.0
-
-
NoC
71.6
68.8
27.2
DSSD
81.5
80.0
-
TDM
-
-
37.3
FPN
-
-
36.2
YOLO v2
78.6
73.4
21.6
RON
77.6
75.4
-
DCN
-
-
-
DeNet
77.1
73.9
33.8
CoupleNet
82.7
80.4
34.4
RetinaNet
-
-
39.1
Mask R-CNN
-
-
39.8
DSOD
77.7
76.3
-
SMN
70.0
-
-
YOLO v3
-
-
33.0
SIN
76.0
73.1
23.2
STDN
80.9
-
-
RefineDet
83.8
83.5
41.8
MegDet
-
-
-
RFBNet
82.2
-
-
CornerNet
-
-
42.1
LibraRetinaNet
-
-
43.0
YOLACT-700
-
-
31.2
DetNASNet(3.8)
-
-
42.0