Package: gluoncv2
Computer vision models on MXNet/GluonThis is a collection of image classification, segmentation, detection, and pose estimation models. Many of them are pretrained onImageNet-1K,CIFAR-10/100,SVHN,CUB-200-2011,Pascal VOC2012,ADE20K,Cityscapes, andCOCOdatasets and loaded automatically
during use. All pretrained models require the same ordinary normalization. Scripts for training/evaluating/converting
models are in theimgclsmobrepo.List of implemented modelsAlexNet ('One weird trick for parallelizing convolutional neural networks')ZFNet ('Visualizing and Understanding Convolutional Networks')VGG/BN-VGG ('Very Deep Convolutional Networks for Large-Scale Image Recognition')BN-Inception ('Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift')ResNet ('Deep Residual Learning for Image Recognition')PreResNet ('Identity Mappings in Deep Residual Networks')ResNeXt ('Aggregated Residual Transformations for Deep Neural Networks')SENet/SE-ResNet/SE-PreResNet/SE-ResNeXt ('Squeeze-and-Excitation Networks')ResNeSt(A) ('ResNeSt: Split-Attention Networks')IBN-ResNet/IBN-ResNeXt/IBN-DenseNet ('Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net')AirNet/AirNeXt ('Attention Inspiring Receptive-Fields Network for Learning Invariant Representations')BAM-ResNet ('BAM: Bottleneck Attention Module')CBAM-ResNet ('CBAM: Convolutional Block Attention Module')ResAttNet ('Residual Attention Network for Image Classification')SKNet ('Selective Kernel Networks')SCNet ('Improving Convolutional Networks with Self-Calibrated Convolutions')RegNet ('Designing Network Design Spaces')DIA-ResNet ('DIANet: Dense-and-Implicit Attention Network')PyramidNet ('Deep Pyramidal Residual Networks')DiracNetV2 ('DiracNets: Training Very Deep Neural Networks Without Skip-Connections')ShaResNet ('ShaResNet: reducing residual network parameter number by sharing weights')CRU-Net ('Sharing Residual Units Through Collective Tensor Factorization To Improve Deep Neural Networks')DenseNet ('Densely Connected Convolutional Networks')CondenseNet ('CondenseNet: An Efficient DenseNet using Learned Group Convolutions')SparseNet ('Sparsely Aggregated Convolutional Networks')PeleeNet ('Pelee: A Real-Time Object Detection System on Mobile Devices')Oct-ResNet ('Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution')Res2Net ('Res2Net: A New Multi-scale Backbone Architecture')WRN ('Wide Residual Networks')WRN-1bit ('Training wide residual networks for deployment using a single bit for each weight')DRN-C/DRN-D ('Dilated Residual Networks')DPN ('Dual Path Networks')DarkNet Ref/Tiny/19 ('Darknet: Open source neural networks in c')DarkNet-53 ('YOLOv3: An Incremental Improvement')ChannelNet ('ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions')iSQRT-COV-ResNet ('Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization')i-RevNet ('i-RevNet: Deep Invertible Networks')BagNet ('Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet')DLA ('Deep Layer Aggregation')MSDNet ('Multi-Scale Dense Networks for Resource Efficient Image Classification')FishNet ('FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction')ESPNetv2 ('ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network')DiCENet ('DiCENet: Dimension-wise Convolutions for Efficient Networks')HRNet ('Deep High-Resolution Representation Learning for Visual Recognition')VoVNet ('An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection')SelecSLS ('XNect: Real-time Multi-person 3D Human Pose Estimation with a Single RGB Camera')HarDNet ('HarDNet: A Low Memory Traffic Network')X-DenseNet ('Deep Expander Networks: Efficient Deep Networks from Graph Theory')SqueezeNet/SqueezeResNet ('SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size')SqueezeNext ('SqueezeNext: Hardware-Aware Neural Network Design')ShuffleNet ('ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices')ShuffleNetV2 ('ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design')MENet ('Merging and Evolution: Improving Convolutional Neural Networks for Mobile Applications')MobileNet ('MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications')FD-MobileNet ('FD-MobileNet: Improved MobileNet with A Fast Downsampling Strategy')MobileNetV2 ('MobileNetV2: Inverted Residuals and Linear Bottlenecks')MobileNetV3 ('Searching for MobileNetV3')IGCV3 ('IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks')GhostNet ('GhostNet: More Features from Cheap Operations')MnasNet ('MnasNet: Platform-Aware Neural Architecture Search for Mobile')DARTS ('DARTS: Differentiable Architecture Search')ProxylessNAS ('ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware')FBNet ('FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search')Xception ('Xception: Deep Learning with Depthwise Separable Convolutions')InceptionV3 ('Rethinking the Inception Architecture for Computer Vision')InceptionV4/InceptionResNetV1/InceptionResNetV2 ('Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning')PolyNet ('PolyNet: A Pursuit of Structural Diversity in Very Deep Networks')NASNet ('Learning Transferable Architectures for Scalable Image Recognition')PNASNet ('Progressive Neural Architecture Search')SPNASNet ('Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours')EfficientNet ('EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks')MixNet ('MixConv: Mixed Depthwise Convolutional Kernels')NIN ('Network In Network')RoR-3 ('Residual Networks of Residual Networks: Multilevel Residual Networks')RiR ('Resnet in Resnet: Generalizing Residual Architectures')ResDrop-ResNet ('Deep Networks with Stochastic Depth')Shake-Shake-ResNet ('Shake-Shake regularization')ShakeDrop-ResNet ('ShakeDrop Regularization for Deep Residual Learning')FractalNet ('FractalNet: Ultra-Deep Neural Networks without Residuals')NTS-Net ('Learning to Navigate for Fine-grained Classification')FCN-8s ('Fully Convolutional Networks for Semantic Segmentation')PSPNet ('Pyramid Scene Parsing Network')DeepLabv3 ('Rethinking Atrous Convolution for Semantic Image Segmentation')ICNet ('ICNet for Real-Time Semantic Segmentation on High-Resolution Images')Fast-SCNN ('Fast-SCNN: Fast Semantic Segmentation Network')CGNet ('CGNet: A Light-weight Context Guided Network for Semantic Segmentation')DABNet ('DABNet: Depth-wise Asymmetric Bottleneck for Real-time Semantic Segmentation')SINet ('SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder')BiSeNet ('BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation')DANet ('Dual Attention Network for Scene Segmentation')FPENet ('Feature Pyramid Encoding Network for Real-time Semantic Segmentation')LEDNet ('LEDNet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation')CenterNet ('Objects as Points')LFFD ('LFFD: A Light and Fast Face Detector for Edge Devices')AlphaPose ('RMPE: Regional Multi-person Pose Estimation')SimplePose ('Simple Baselines for Human Pose Estimation and Tracking')Lightweight OpenPose ('Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose')IBPPose ('Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation')VisemeNet ('VisemeNet: Audio-Driven Animator-Centric Speech Animation')VOCA ('Capture, Learning, and Synthesis of 3D Speaking Styles')Neural Voice Puppetry Audio-to-Expression net ('Neural Voice Puppetry: Audio-driven Facial Reenactment')Jasper/JasperDR ('Jasper: An End-to-End Convolutional Neural Acoustic Model')QuartzNet ('QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions')InstallationTo use the models in your project, simply install thegluoncv2package withmxnet:pip install gluoncv2 mxnet>=1.2.1To enable different hardware supports such as GPUs, check outMXNet variants.
For example, you can install with CUDA-9.2 supported MXNet:pip install gluoncv2 mxnet-cu92>=1.2.1UsageExample of using a pretrained ResNet-18 model:from gluoncv2.model_provider import get_model as glcv2_get_model
import mxnet as mx

net = glcv2_get_model("resnet18", pretrained=True)
x = mx.nd.zeros((1, 3, 224, 224), ctx=mx.cpu())
y = net(x)Pretrained modelsImageNet-1KSome remarks:Top1/Top5 are the standard 1-crop Top-1/Top-5 errors (in percents) on the validation subset of the ImageNet-1K dataset.FLOPs/2 is the number of FLOPs divided by two to be similar to the number of MACs.ResNet/PreResNet with b-suffix is a version of the networks with the stride in the second convolution of the
bottleneck block. Respectively a network without b-suffix has the stride in the first convolution.ResNet/PreResNet models do not use biases in convolutions at all.CondenseNet models are only so-called converted versions.ShuffleNetV2 and ShuffleNetV2b are different implementations of the same architecture.ResNet(A) is an average downsampled ResNet intended for use as an feature extractor in some pose estimation networks.ResNet(D) is a dilated ResNet intended for use as an feature extractor in some segmentation networks.Models with *-suffix use non-standard preprocessing (see the training log).ModelTop1Top5ParamsFLOPs/2RemarksAlexNet38.0716.1062,378,3441,132.33MTraining (log)AlexNet-b39.3017.0561,100,840714.83MTraining (log)ZFNet39.2116.7862,357,6081,170.33MTraining (log)ZFNet-b35.8114.59107,627,6242,479.13MTraining (log)VGG-1129.5910.16132,863,3367,615.87MTraining (log)VGG-1328.379.50133,047,84811,317.65MTraining (log)VGG-1626.618.32138,357,54415,480.10MTraining (log)VGG-1925.587.67143,667,24019,642.55MTraining (log)BN-VGG-1128.569.34132,866,0887,630.21MTraining (log)BN-VGG-1327.688.87133,050,79211,341.62MTraining (log)BN-VGG-1625.507.57138,361,76815,506.38MTraining (log)BN-VGG-1923.916.89143,672,74419,671.15MTraining (log)BN-VGG-11b29.249.75132,868,8407,630.72MTraining (log)BN-VGG-13b28.239.12133,053,73611,342.14MTraining (log)BN-VGG-16b25.837.75138,365,99215,507.20MTraining (log)BN-VGG-19b24.797.35143,678,24819,672.26MTraining (log)BN-Inception25.127.5411,295,2402,048.06MTraining (log)ResNet-1032.5412.535,418,792894.04MTraining (log)ResNet-1231.6812.035,492,7761,126.25MTraining (log)ResNet-1430.3810.865,788,2001,357.94MTraining (log)ResNet-BC-14b29.2210.3310,064,9361,479.12MTraining (log)ResNet-1628.539.786,968,8721,589.34MTraining (log)ResNet-18 x0.2539.3117.403,937,400270.94MTraining (log)ResNet-18 x0.533.4112.845,804,296608.70MTraining (log)ResNet-18 x0.7530.0010.668,476,0561,129.45MTraining (log)ResNet-1826.798.6711,689,5121,820.41MTraining (log)ResNet-2625.968.2317,960,2322,746.79MTraining (log)ResNet-BC-26b24.867.5815,995,1762,356.67MTraining (log)ResNet-3424.537.4321,797,6723,672.68MTraining (log)ResNet-BC-38b23.506.7221,925,4163,234.21MTraining (log)ResNet-5022.156.0425,557,0323,877.95MTraining (log)ResNet-50b22.066.1125,557,0324,110.48MTraining (log)ResNet-10120.525.1644,549,1607,597.95MTraining (log)ResNet-101b20.265.1244,549,1607,830.48MTraining (log)ResNet-15219.204.4460,192,80811,321.85MTraining (log)ResNet-152b18.844.2960,192,80811,554.38MTraining (log)PreResNet-1034.6514.015,417,128894.19MTraining (log)PreResNet-1233.5713.215,491,1121,126.40MTraining (log)PreResNet-1432.2912.185,786,5361,358.09MTraining (log)PreResNet-BC-14b30.6711.5110,057,3841,476.62MTraining (log)PreResNet-1630.2110.816,967,2081,589.49MTraining (log)PreResNet-18 x0.2539.6217.783,935,960270.93MTraining (log)PreResNet-18 x0.533.6713.195,802,440608.73MTraining (log)PreResNet-18 x0.7529.9610.688,473,7841,129.51MTraining (log)PreResNet-1828.169.5111,687,8481,820.56MTraining (log)PreResNet-2626.038.3417,958,5682,746.94MTraining (log)PreResNet-BC-26b25.217.8615,987,6242,354.16MTraining (log)PreResNet-3424.557.5121,796,0083,672.83MTraining (log)PreResNet-BC-38b22.676.3321,917,8643,231.70MTraining (log)PreResNet-5022.276.2025,549,4803,875.44MTraining (log)PreResNet-50b22.366.3225,549,4804,107.97MTraining (log)PreResNet-10120.605.3444,541,6087,595.44MTraining (log)PreResNet-101b20.855.4044,541,6087,827.97MTraining (log)PreResNet-15219.174.4660,185,25611,319.34MTraining (log)PreResNet-152b19.014.3860,185,25611,551.87MTraining (log)PreResNet-200b18.964.4664,666,28015,068.63MTraining (log)PreResNet-269b20.175.01102,065,83220,101.11MTraining (log)ResNeXt-14 (16x4d)31.6612.237,127,3361,045.77MTraining (log)ResNeXt-14 (32x2d)32.1612.477,029,4161,031.32MTraining (log)ResNeXt-14 (32x4d)29.9511.109,411,8801,603.46MTraining (log)ResNeXt-26 (32x2d)26.348.509,924,1361,461.06MTraining (log)ResNeXt-26 (32x4d)23.937.2115,389,4802,488.07MTraining (log)ResNeXt-50 (32x4d)20.845.4525,028,9044,255.86MTraining (log)ResNeXt-101 (32x4d)18.464.1844,177,7048,003.45MTraining (log)ResNeXt-101 (64x4d)18.804.3983,455,27215,500.27MTraining (log)SE-ResNet-1031.3611.695,463,332894.08MTraining (log)SE-ResNet-1231.6411.765,537,8961,126.29MTraining (log)SE-ResNet-1430.3410.955,835,5041,357.99MTraining (log)SE-ResNet-1628.419.727,024,6401,589.40MTraining (log)SE-ResNet-1827.959.2011,778,5921,820.51MTraining (log)SE-ResNet-2625.428.0318,093,8522,746.93MTraining (log)SE-ResNet-BC-26b23.446.8217,395,9762,358.07MTraining (log)SE-ResNet-BC-38b21.445.7524,026,6163,236.32MTraining (log)SE-ResNet-5021.075.6028,088,0243,880.49MTraining (log)SE-ResNet-50b20.585.3328,088,0244,113.02MTraining (log)SE-ResNet-10119.004.4149,326,8727,602.76MTraining (log)SE-ResNet-101b19.464.6249,326,8727,835.29MTraining (log)SE-ResNet-15218.594.3066,821,84811,328.52MTraining (log)SE-PreResNet-1032.3712.215,461,668894.23MTraining (log)SE-PreResNet-1231.5811.805,536,2321,126.44MTraining (log)SE-PreResNet-1628.399.567,022,9761,589.55MTraining (log)SE-PreResNet-1827.168.8111,776,9281,820.66MTraining (log)SE-PreResNet-2625.958.0418,092,1882,747.08MTraining (log)SE-PreResNet-BC-26b22.956.3617,388,4242,355.57MTraining (log)SE-PreResNet-BC-38b21.425.6324,019,0643,233.81MTraining (log)SE-PreResNet-50b20.675.3228,080,4724,110.51MTraining (log)SE-ResNeXt-50 (32x4d)18.744.3327,559,8964,258.40MTraining (log)SE-ResNeXt-101 (32x4d)19.064.4448,955,4168,008.26MTraining (log)SE-ResNeXt-101 (64x4d)18.434.0888,232,98415,505.08MTraining (log)SENet-1625.348.0631,366,1685,080.55MTraining (log)SENet-2821.685.9136,453,7685,731.20MTraining (log)SENet-15418.844.40115,088,98420,745.78MTraining (log)ResNeSt(A)-BC-1422.276.3410,611,6882,766.86MTraining (log)ResNeSt(A)-1823.436.8912,763,7842,587.11MTraining (log)ResNeSt(A)-BC-2619.574.7017,069,4483,645.87MTraining (log)ResNeSt(A)-5018.924.3827,483,2405,402.09MTraining (log)ResNeSt(A)-10117.743.9948,275,01610,246.42MFromdmlc/gluon-cv(log)ResNeSt(A)-15218.724.5165,316,04013,974.33MTraining (log)ResNeSt(A)-20016.783.4070,201,54422,854.22MFromdmlc/gluon-cv(log)ResNeSt(A)-26916.383.36110,929,48046,005.88MFromdmlc/gluon-cv(log)IBN-ResNet-5021.465.5925,557,0324,110.48MTraining (log)IBN-ResNet-10119.694.8944,549,1607,830.48MTraining (log)IBN(b)-ResNet-5021.705.7925,558,5684,112.89MTraining (log)IBN-ResNeXt-101 (32x4d)19.764.8744,177,7048,003.45MTraining (log)IBN-DenseNet-12123.336.467,978,8562,872.13MTraining (log)IBN-DenseNet-16922.146.0814,149,4803,403.89MTraining (log)AirNet50-1x64d (r=2)20.455.2327,425,8644,772.11MTraining (log)AirNet50-1x64d (r=16)21.115.4425,714,9524,399.97MTraining (log)AirNeXt50-32x4d (r=2)19.845.0427,604,2965,339.58MTraining (log)BAM-ResNet-5020.595.3825,915,0994,196.09MTraining (log)CBAM-ResNet-5019.944.8828,089,6244,116.97MTraining (log)SCNet-5020.535.1125,564,5843,951.01MTraining (log)SCNet-10119.214.4644,565,4167,204.19MTraining (log)SCNet(A)-5019.014.5925,583,8164,715.79MTraining (log)RegNetX-200MF29.9110.382,684,792203.32MTraining (log)RegNetX-400MF26.258.555,157,512403.44MTraining (log)RegNetX-600MF24.717.566,196,040608.36MTraining (log)RegNetX-800MF24.097.247,259,656809.47MTraining (log)RegNetX-1.6GF22.126.139,190,1361,618.97MTraining (log)RegNetX-3.2GF21.285.6815,296,5523,199.52MTraining (log)RegNetX-4.0GF19.514.6922,118,2483,986.26MTraining (log)RegNetX-6.4GF19.224.5826,209,2566,490.97MTraining (log)RegNetX-8.0GF19.624.6639,572,6488,017.90MTraining (log)RegNetX-12GF19.995.1846,106,05612,124.16MTraining (log)RegNetX-16GF19.124.5654,278,53615,986.59MTraining (log)RegNetX-32GF17.833.94107,811,56031,790.18MTraining (log)RegNetY-200MF28.509.533,162,996203.80MTraining (log)RegNetY-400MF24.857.474,344,144409.95MTraining (log)RegNetY-600MF23.596.976,055,160609.91MTraining (log)RegNetY-800MF22.546.456,263,168808.07MTraining (log)RegNetY-1.6GF21.235.6811,202,4301,628.43MTraining (log)RegNetY-3.2GF18.324.1319,436,3383,197.70MFromrwightman/pyt...models(log)RegNetY-4.0GF19.564.6720,646,6563,997.63MTraining (log)RegNetY-6.4GF18.954.4530,583,2526,386.79MTraining (log)RegNetY-8.0GF18.784.3639,180,0687,994.33MTraining (log)RegNetY-12GF18.514.3151,822,54412,129.89MTraining (log)RegNetY-16GF18.634.3083,590,14015,941.65MTraining (log)RegNetY-32GF17.833.73145,046,77032,313.76MTraining (log)PyramidNet-101 (a=360)20.415.2042,455,0708,743.54MTraining (log)DiracNetV2-1830.6111.1711,511,7841,796.62MFromszagoruyko/diracnets(log)DiracNetV2-3427.939.4621,616,2323,646.93MFromszagoruyko/diracnets(log)CRU-Net-5620.645.3625,609,3845,660.66MTraining (log)DenseNet-12123.256.857,978,8562,872.13MTraining (log)DenseNet-16121.825.9228,681,0007,793.16MTraining (log)DenseNet-16922.106.0514,149,4803,403.89MTraining (log)DenseNet-20121.565.9020,013,9284,347.15MTraining (log)CondenseNet-74 (C=G=4)26.828.644,773,944546.06MFromShichenLiu/CondenseNet(log)CondenseNet-74 (C=G=8)29.7610.492,935,416291.52MFromShichenLiu/CondenseNet(log)PeleeNet29.389.792,802,248514.87MTraining (log)WRN-50-222.026.0668,849,12811,405.42MTraining (log)DRN-C-2624.327.1121,126,58416,993.90MTraining (log)DRN-C-4222.256.1431,234,74425,093.75MTraining (log)DRN-C-5820.475.1540,542,00832,489.94MTraining (log)DRN-D-2224.677.4416,393,75213,051.33MTraining (log)DRN-D-3822.836.2426,501,91221,151.19MTraining (log)DRN-D-5420.294.9735,809,17628,547.38MTraining (log)DRN-D-10521.315.8154,801,30443,442.43MFromfyu/drn(log)DPN-6822.876.5812,611,6022,351.84MTraining (log)DPN-9818.294.2261,570,72811,716.51MTraining (log)DPN-13119.424.7779,254,50416,076.15MTraining (log)DarkNet Tiny40.3117.461,042,104500.85MTraining (log)DarkNet Ref38.0016.687,319,416367.59MTraining (log)DarkNet-5321.275.5041,609,9287,133.86MTraining 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