Package: pytorchcv
Computer vision models on PyTorchThis 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')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')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')RevNet ('The Reversible Residual Network: Backpropagation Without Storing Activations')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')PSPNet ('Pyramid Scene Parsing Network')DeepLabv3 ('Rethinking Atrous Convolution for Semantic Image Segmentation')FCN-8s ('Fully Convolutional Networks for Semantic 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')ContextNet ('ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time')LEDNet ('LEDNet: A Lightweight Encoder-Decoder Network for Real-Time Semantic Segmentation')ESNet ('ESNet: An Efficient Symmetric Network for Real-time Semantic Segmentation')EDANet ('Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation')ENet ('ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation')ERFNet ('ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation')LinkNet ('LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation')SegNet ('SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation')U-Net ('U-Net: Convolutional Networks for Biomedical Image Segmentation')SQNet ('Speeding up Semantic Segmentation for Autonomous Driving')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')PFPCNet ('Production-Level Facial Performance Capture Using Deep Convolutional Neural Networks')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 thepytorchcvpackage withtorch(>=0.4.1 is recommended):pip install pytorchcv torch>=0.4.0To enable/disable different hardware supports such as GPUs, check out PyTorch installationinstructions.UsageExample of using a pretrained ResNet-18 model:from pytorchcv.model_provider import get_model as ptcv_get_model
import torch
from torch.autograd import Variable

net = ptcv_get_model("resnet18", pretrained=True)
x = Variable(torch.randn(1, 3, 224, 224))
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.RemarkConverted from GL modelmeans that the model was trained onMXNet/Gluonand then converted to PyTorch.You may notice that quality estimations are quite different from ones for the corresponding models in other frameworks. This
is due to the fact that the quality is estimated on the standard TorchVision stack of image transformations. Using
OpenCVResizetransformation instead of PIL one quality evaluation results will be similar to ones for the Gluon models.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/2RemarksAlexNet38.5016.6462,378,3441,132.33MConverted from GL model (log)AlexNet-b39.7417.4761,100,840714.83MConverted from GL model (log)ZFNet39.7917.2762,357,6081,170.33MConverted from GL model (log)ZFNet-b36.3714.90107,627,6242,479.13MConverted from GL model (log)VGG-1129.9010.36132,863,3367,615.87MConverted from GL model (log)VGG-1328.769.75133,047,84811,317.65MConverted from GL model (log)VGG-1626.988.65138,357,54415,480.10MConverted from GL model (log)VGG-1925.747.90143,667,24019,642.55MConverted from GL model (log)BN-VGG-1129.019.61132,866,0887,630.21MConverted from GL model (log)BN-VGG-1327.839.13133,050,79211,341.62MConverted from GL model (log)BN-VGG-1625.727.79138,361,76815,506.38MConverted from GL model (log)BN-VGG-1924.137.12143,672,74419,671.15MConverted from GL model (log)BN-VGG-11b29.569.96132,868,8407,630.72MConverted from GL model (log)BN-VGG-13b28.479.24133,053,73611,342.14MConverted from GL model (log)BN-VGG-16b25.977.95138,365,99215,507.20MConverted from GL model (log)BN-VGG-19b25.137.46143,678,24819,672.26MConverted from GL model (log)BN-Inception25.377.7411,295,2402,048.06MConverted from GL model (log)ResNet-1032.7612.935,418,792894.04MConverted from GL model (log)ResNet-1232.1012.235,492,7761,126.25MConverted from GL model (log)ResNet-1430.5111.095,788,2001,357.94MConverted from GL model (log)ResNet-BC-14b29.4310.7410,064,9361,479.12MConverted from GL model (log)ResNet-1628.7410.096,968,8721,589.34MConverted from GL model (log)ResNet-18 x0.2539.6217.853,937,400270.94MConverted from GL model (log)ResNet-18 x0.533.8013.275,804,296608.70MConverted from GL model (log)ResNet-18 x0.7530.4011.068,476,0561,129.45MConverted from GL model (log)ResNet-1826.948.9611,689,5121,820.41MConverted from GL model (log)ResNet-2626.168.4917,960,2322,746.79MConverted from GL model (log)ResNet-BC-26b25.097.9715,995,1762,356.67MConverted from GL model (log)ResNet-3424.847.8021,797,6723,672.68MConverted from GL model (log)ResNet-BC-38b23.697.0021,925,4163,234.21MConverted from GL model (log)ResNet-5022.286.3325,557,0323,877.95MConverted from GL model (log)ResNet-50b22.396.3825,557,0324,110.48MConverted from GL model (log)ResNet-10120.795.4044,549,1607,597.95MConverted from GL model (log)ResNet-101b20.595.3044,549,1607,830.48MConverted from GL model (log)ResNet-15219.294.6860,192,80811,321.85MConverted from GL model (log)ResNet-152b19.014.4560,192,80811,554.38MConverted from GL model (log)PreResNet-1035.1114.215,417,128894.19MConverted from GL model (log)PreResNet-1233.8613.485,491,1121,126.40MConverted from GL model (log)PreResNet-1432.6412.395,786,5361,358.09MConverted from GL model (log)PreResNet-BC-14b31.2911.8110,057,3841,476.62MConverted from GL model (log)PreResNet-1630.5311.086,967,2081,589.49MConverted from GL model (log)PreResNet-18 x0.2540.0618.113,935,960270.93MConverted from GL model (log)PreResNet-18 x0.534.0013.405,802,440608.73MConverted from GL model (log)PreResNet-18 x0.7530.2311.058,473,7841,129.51MConverted from GL model (log)PreResNet-1828.439.7211,687,8481,820.56MConverted from GL model (log)PreResNet-2626.338.5117,958,5682,746.94MConverted from GL model (log)PreResNet-BC-26b25.488.0315,987,6242,354.16MConverted from GL model (log)PreResNet-3424.897.7421,796,0083,672.83MConverted from GL model (log)PreResNet-BC-38b22.926.5721,917,8643,231.70MConverted from GL model (log)PreResNet-5022.406.4725,549,4803,875.44MConverted from GL model (log)PreResNet-50b22.516.5525,549,4804,107.97MConverted from GL model (log)PreResNet-10120.815.6344,541,6087,595.44MConverted from GL model (log)PreResNet-101b21.045.5644,541,6087,827.97MConverted from GL model (log)PreResNet-15219.364.6460,185,25611,319.34MConverted from GL model (log)PreResNet-152b19.234.5960,185,25611,551.87MConverted from GL model (log)PreResNet-200b19.074.6864,666,28015,068.63MConverted from GL model (log)PreResNet-269b20.355.27102,065,83220,101.11MConverted from GL model (log)ResNeXt-14 (16x4d)31.9412.487,127,3361,045.77MConverted from GL model (log)ResNeXt-14 (32x2d)32.5812.817,029,4161,031.32MConverted from GL model (log)ResNeXt-14 (32x4d)30.3211.469,411,8801,603.46MConverted from GL model (log)ResNeXt-26 (32x2d)26.638.879,924,1361,461.06MConverted from GL model (log)ResNeXt-26 (32x4d)24.147.4615,389,4802,488.07MConverted from GL model (log)ResNeXt-50 (32x4d)21.085.6025,028,9044,255.86MConverted from GL model (log)ResNeXt-101 (32x4d)18.784.3444,177,7048,003.45MConverted from GL model (log)ResNeXt-101 (64x4d)19.084.5283,455,27215,500.27MConverted from GL model (log)SE-ResNet-1031.7612.025,463,332894.27MConverted from GL model (log)SE-ResNet-1231.9312.005,537,8961,126.58MConverted from GL model (log)SE-ResNet-1430.7311.285,835,5041,358.33MConverted from GL model (log)SE-ResNet-1628.729.987,024,6401,589.76MConverted from GL model (log)SE-ResNet-1828.189.6111,778,5921,820.88MConverted from GL model (log)SE-ResNet-2625.678.2418,093,8522,747.49MConverted from GL model (log)SE-ResNet-BC-26b23.597.0317,395,9762,359.58MConverted from GL model (log)SE-ResNet-BC-38b21.605.9524,026,6163,238.58MConverted from GL model (log)SE-ResNet-5021.225.7528,088,0243,883.25MConverted from GL model (log)SE-ResNet-50b20.795.3928,088,0244,115.78MConverted from GL model (log)SE-ResNet-10119.244.6049,326,8727,602.76MConverted from GL model (log)SE-ResNet-101b19.704.8749,326,8727,839.75MConverted from GL model (log)SE-ResNet-15218.724.3966,821,84811,328.52MConverted from GL model (log)SE-PreResNet-1032.8312.575,461,668894.42MConverted from GL model (log)SE-PreResNet-1232.0712.035,536,2321,126.73MConverted from GL model (log)SE-PreResNet-1628.779.757,022,9761,589.91MConverted from GL model (log)SE-PreResNet-1827.439.0911,776,9281,821.03MConverted from GL model (log)SE-PreResNet-2626.208.2218,092,1882,747.64MConverted from GL model (log)SE-PreResNet-BC-26b23.226.6017,388,4242,357.07MConverted from GL model (log)SE-PreResNet-BC-38b21.605.7824,019,0643,236.07MConverted from GL model (log)SE-PreResNet-50b20.855.4928,080,4724,113.27MConverted from GL model (log)SE-ResNeXt-50 (32x4d)18.944.5127,559,8964,261.16MConverted from GL model (log)SE-ResNeXt-101 (32x4d)19.364.6748,955,4168,012.73MConverted from GL model (log)SE-ResNeXt-101 (64x4d)18.664.2888,232,98415,509.54MConverted from GL model (log)SENet-1625.658.2031,366,1685,081.30MConverted from GL model (log)SENet-2821.945.9836,453,7685,732.71MConverted from GL model (log)SENet-15418.854.55115,088,98420,745.78MConverted from GL model (log)ResNeSt(A)-BC-1422.466.4710,611,6882,767.37MConverted from GL model (log)ResNeSt(A)-1823.687.0712,763,7842,587.50MConverted from GL model (log)ResNeSt(A)-BC-2619.564.7617,069,4483,646.57MConverted from GL model (log)ResNeSt(A)-5019.004.4927,483,2405,403.11MConverted from GL model (log)ResNeSt(A)-10117.834.0348,275,01610,247.88MFromdmlc/gluon-cv(log)ResNeSt(A)-15218.874.6365,316,04013,976.34MConverted from GL model (log)ResNeSt(A)-20016.873.3970,201,54422,857.88MFromdmlc/gluon-cv(log)ResNeSt(A)-26916.473.38110,929,48046,012.95MFromdmlc/gluon-cv(log)IBN-ResNet-5021.615.7625,557,0324,110.48MConverted from GL model (log)IBN-ResNet-10119.975.0744,549,1607,830.48MConverted from GL model (log)IBN(b)-ResNet-5021.795.9725,558,5684,112.89MConverted from GL model (log)IBN-ResNeXt-101 (32x4d)20.105.1244,177,7048,003.45MConverted from GL model (log)IBN-DenseNet-12123.586.737,978,8562,872.13MConverted from GL model (log)IBN-DenseNet-16922.426.1914,149,4803,403.89MConverted from GL model (log)AirNet50-1x64d (r=2)20.635.3227,425,8644,772.11MConverted from GL model (log)AirNet50-1x64d (r=16)21.245.6025,714,9524,399.97MConverted from GL model (log)AirNeXt50-32x4d (r=2)20.145.1527,604,2965,339.58MConverted from GL model (log)BAM-ResNet-5020.915.4725,915,0994,196.09MConverted from GL model (log)CBAM-ResNet-5020.175.0528,089,6244,116.97MConverted from GL model (log)SCNet-5021.225.4725,564,5843,951.06MConverted from GL model (log)SCNet-10119.914.8444,565,4167,204.24MConverted from GL model (log)SCNet(A)-5019.534.6825,583,8164,715.84MFromMCG-NKU/SCNet(log)RegNetX-200MF30.2010.662,684,792203.33MConverted from GL model (log)RegNetX-400MF26.448.665,157,512403.45MConverted from GL model (log)RegNetX-600MF24.967.916,196,040608.37MConverted from GL model (log)RegNetX-800MF24.217.407,259,656809.49MConverted from GL model (log)RegNetX-1.6GF22.446.379,190,1361,618.99MConverted from GL model (log)RegNetX-3.2GF21.485.9215,296,5523,199.55MConverted from GL model (log)RegNetX-4.0GF19.694.8822,118,2483,986.29MConverted from GL model (log)RegNetX-6.4GF19.504.6826,209,2566,491.01MConverted from GL model (log)RegNetX-8.0GF19.754.8639,572,6488,017.94MConverted from GL model (log)RegNetX-12GF20.025.3246,106,05612,124.22MConverted from GL model (log)RegNetX-16GF19.294.7754,278,53615,986.64MConverted from GL model (log)RegNetX-32GF18.004.13107,811,56031,790.24MConverted from GL model (log)RegNetY-200MF28.669.803,162,996203.99MConverted from GL model (log)RegNetY-400MF25.027.694,344,144410.35MConverted from GL model (log)RegNetY-600MF23.797.126,055,160610.37MConverted from GL model (log)RegNetY-800MF22.676.606,263,168808.62MConverted from GL model (log)RegNetY-1.6GF21.415.8111,202,4301,629.48MConverted from GL model (log)RegNetY-3.2GF18.044.0419,436,3383,199.15MFromrwightman/pyt...models(log)RegNetY-4.0GF19.564.7020,646,6563,999.16MConverted from GL model (log)RegNetY-6.4GF19.034.5630,583,2526,388.91MConverted from GL model (log)RegNetY-8.0GF18.914.4839,180,0687,996.54MConverted from GL model (log)RegNetY-12GF18.674.4251,822,54412,132.55MConverted from GL model (log)RegNetY-16GF18.824.4483,590,14015,944.53MConverted from GL model (log)RegNetY-32GF17.923.83145,046,77032,317.66MConverted from GL model (log)PyramidNet-101 (a=360)20.535.4342,455,0708,743.54MConverted from GL model (log)DiracNetV2-1831.4711.7011,511,7841,796.62MFromszagoruyko/diracnets(log)DiracNetV2-3428.759.9321,616,2323,646.93MFromszagoruyko/diracnets(log)DenseNet-12123.487.047,978,8562,872.13MConverted from GL model (log)DenseNet-16121.916.0628,681,0007,793.16MConverted from GL model (log)DenseNet-16922.426.2914,149,4803,403.89MConverted from GL model (log)DenseNet-20121.786.1220,013,9284,347.15MConverted from GL model (log)CondenseNet-74 (C=G=4)26.258.284,773,944546.06MFromShichenLiu/CondenseNet(log)CondenseNet-74 (C=G=8)28.9310.062,935,416291.52MFromShichenLiu/CondenseNet(log)PeleeNet29.4110.042,802,248514.87MConverted from GL model (log)WRN-50-222.256.2668,849,12811,405.42MConverted from GL model (log)DRN-C-2624.617.2321,126,58416,993.90MConverted from GL model (log)DRN-C-4222.376.2831,234,74425,093.75MConverted from GL model (log)DRN-C-5820.655.2740,542,00832,489.94MConverted from GL model (log)DRN-D-2224.867.5816,393,75213,051.33MConverted from GL model (log)DRN-D-3822.946.4326,501,91221,151.19MConverted from GL model (log)DRN-D-5420.405.1735,809,17628,547.38MConverted from GL model (log)DRN-D-10520.625.4854,801,30443,442.43MFromfyu/drn(log)DPN-6823.246.7912,611,6022,351.84MConverted from GL model (log)DPN-9818.444.3061,570,72811,716.51MConverted from GL model (log)DPN-13119.625.0379,254,50416,076.15MConverted from GL model (log)DarkNet Tiny40.7417.841,042,104500.85MConverted from GL model (log)DarkNet Ref38.5817.187,319,416367.59MConverted from GL model (log)DarkNet-5321.335.5841,609,9287,133.86MConverted from GL model (log)i-RevNet-30125.988.41125,120,35614,453.87MFromjhjacobsen/pytorch-i-revnet(log)BagNet-949.1525.7615,688,74416,049.19MConverted from GL model (log)BagNet-1736.8815.5116,213,03215,768.77MConverted from GL model (log)BagNet-3329.8210.7018,310,18416,371.52MConverted from GL model (log)DLA-3424.597.2415,742,1043,071.37MConverted from GL model (log)DLA-46-C34.2813.231,301,400585.45MConverted from GL model (log)DLA-X-46-C33.2612.691,068,440546.72MConverted from GL model (log)DLA-6021.475.7022,036,6324,255.49MConverted from GL model (log)DLA-X-6021.075.7517,352,3443,543.68MConverted from GL model (log)DLA-X-60-C30.9810.911,319,832596.06MConverted from GL model (log)DLA-10220.695.3733,268,8887,190.95MConverted from GL model (log)DLA-X-10219.704.8826,309,2725,884.94MConverted from GL model (log)DLA-X2-10218.844.3741,282,2009,340.61MConverted from GL model (log)DLA-16919.394.7153,389,72011,593.20MConverted from GL model (log)FishNet-15019.264.7524,959,4006,435.05MConverted from GL model (log)ESPNetv2 x0.542.3220.151,241,33235.36MFromsacmehta/ESPNetv2(log)ESPNetv2 x1.033.9213.451,670,07298.09MFromsacmehta/ESPNetv2(log)ESPNetv2 x1.2532.0612.181,965,440138.18MFromsacmehta/ESPNetv2(log)ESPNetv2 x1.530.8311.292,314,856185.77MFromsacmehta/ESPNetv2(log)ESPNetv2 x2.027.949.613,498,136306.93MFromsacmehta/ESPNetv2(log)DiCENet x0.253.9129.381,130,70418.76MFromsacmehta/EdgeNets(log)DiCENet x0.546.1622.581,214,12030.48MFromsacmehta/EdgeNets(log)DiCENet x0.7537.0615.741,495,67655.80MFromsacmehta/EdgeNets(log)DiCENet x1.033.5913.251,805,60482.17MFromsacmehta/EdgeNets(log)DiCENet x1.2532.2612.402,162,888111.87MFromsacmehta/EdgeNets(log)DiCENet x1.530.5511.232,652,200151.81MFromsacmehta/EdgeNets(log)DiCENet x1.7529.7610.623,264,932201.26MFromsacmehta/EdgeNets(log)DiCENet x2.028.9510.193,979,044257.95MFromsacmehta/EdgeNets(log)HRNet-W18 Small V126.489.0113,187,4641,615.00MConverted from GL model (log)HRNet-W18 Small V221.936.1815,597,4642,618.84MConverted from GL model (log)HRNetV2-W1820.205.1221,299,0044,323.07MConverted from GL model (log)HRNetV2-W3020.355.2137,712,2208,156.82MConverted from GL model (log)HRNetV2-W3219.965.0641,232,6808,974.04MConverted from GL model (log)HRNetV2-W4019.844.9357,557,16012,752.26MConverted from GL model (log)HRNetV2-W4419.745.0167,064,98414,946.96MConverted from GL model (log)HRNetV2-W4819.675.0077,469,86417,345.39MConverted from GL model (log)HRNetV2-W6419.674.87128,059,94428,976.42MConverted from GL model (log)VoVNet-27-slim29.789.973,525,7362,187.25MConverted from GL model (log)VoVNet-3921.715.6422,600,2967,086.16MConverted from GL model (log)VoVNet-5720.335.1836,640,2968,943.09MConverted from GL model (log)SelecSLS-42b21.936.1132,458,2482,980.62MConverted from GL model (log)SelecSLS-6020.355.2930,670,7683,591.78MConverted from GL model (log)SelecSLS-60b20.855.5932,774,0643,629.14MConverted from GL model (log)HarDNet-39DS26.838.813,488,228437.52MConverted from GL model (log)HarDNet-68DS24.557.564,180,602788.86MConverted from GL model (log)HarDNet-6823.516.9917,565,3484,256.32MFromPingoLH/Pytorch-HarDNet(log)HarDNet-8522.005.8636,670,2129,088.58MConverted from GL model (log)SqueezeNet v1.039.2917.661,248,424823.67MConverted from GL model (log)SqueezeNet v1.139.3117.721,235,496352.02MConverted from GL model (log)SqueezeResNet v1.039.7718.091,248,424823.67MConverted from GL model (log)SqueezeResNet v1.140.0918.211,235,496352.02MConverted from GL model (log)1.0-SqNxt-2342.5119.06724,056287.28MConverted from GL model (log)1.0-SqNxt-23v540.7717.85921,816285.82MConverted from GL model (log)1.5-SqNxt-2334.8913.501,511,824552.39MConverted from GL model (log)1.5-SqNxt-23v533.8113.011,953,616550.97MConverted from GL model (log)2.0-SqNxt-2330.6211.002,583,752898.48MConverted from GL model (log)2.0-SqNxt-23v529.6310.663,366,344897.60MConverted from GL model (log)ShuffleNet x0.25 (g=1)62.4437.29209,74612.35MConverted from GL model (log)ShuffleNet x0.25 (g=3)61.7436.53305,90213.09MConverted from GL model (log)ShuffleNet x0.5 (g=1)46.5922.61534,48441.16MConverted from GL model (log)ShuffleNet x0.5 (g=3)44.1620.80718,32441.70MConverted from GL model (log)ShuffleNet x0.75 (g=1)39.5817.11975,21486.42MConverted from GL model (log)ShuffleNet x0.75 (g=3)38.2016.501,238,26685.82MConverted from GL model (log)ShuffleNet x1.0 (g=1)34.9313.891,531,936148.13MConverted from GL model (log)ShuffleNet x1.0 (g=2)34.2513.631,733,848147.60MConverted from GL model (log)ShuffleNet x1.0 (g=3)34.3913.481,865,728145.46MConverted from GL model (log)ShuffleNet x1.0 (g=4)34.1913.351,968,344143.33MConverted from GL model (log)ShuffleNet x1.0 (g=8)34.0613.422,434,768150.76MConverted from GL model (log)ShuffleNetV2 x0.540.9918.651,366,79243.31MConverted from GL model (log)ShuffleNetV2 x1.031.4411.632,278,604149.72MConverted from GL model (log)ShuffleNetV2 x1.527.479.424,406,098320.77MConverted from GL model (log)ShuffleNetV2 x2.025.948.457,601,686595.84MConverted from GL model (log)ShuffleNetV2b x0.540.2918.221,366,79243.31MConverted from GL model (log)ShuffleNetV2b x1.030.6211.252,279,760150.62MConverted from GL model (log)ShuffleNetV2b x1.527.319.114,410,194323.98MConverted from GL model (log)ShuffleNetV2b x2.025.588.347,611,290603.37MConverted from GL model (log)108-MENet-8x1 (g=3)43.9420.76654,51642.68MConverted from GL model (log)128-MENet-8x1 (g=4)42.4319.59750,79645.98MConverted from GL model (log)160-MENet-8x1 (g=8)43.8420.84850,12045.63MConverted from GL model (log)228-MENet-12x1 (g=3)34.1113.161,806,568152.93MConverted from GL model (log)256-MENet-12x1 (g=4)32.6512.521,888,240150.65MConverted from GL model (log)348-MENet-12x1 (g=3)28.249.583,368,128312.00MConverted from GL model (log)352-MENet-12x1 (g=8)31.5612.002,272,872157.35MConverted from GL model (log)456-MENet-24x1 (g=3)25.327.995,304,784567.90MConverted from GL model (log)MobileNet x0.2546.2622.49470,07244.09MConverted from GL model (log)MobileNet x0.534.1513.551,331,592155.42MConverted from GL model (log)MobileNet x0.7530.1410.762,585,560333.99MConverted from GL model (log)MobileNet x1.026.618.954,231,976579.80MConverted from GL model (log)MobileNetb x0.2545.5122.01467,59242.88MConverted from GL model (log)MobileNetb x0.533.4213.101,326,632153.00MConverted from GL model (log)MobileNetb x0.7529.5510.372,578,120330.37MConverted from GL model (log)MobileNetb x1.025.458.164,222,056574.97MConverted from GL model (log)FD-MobileNet x0.2555.8630.98383,16012.95MConverted from GL model (log)FD-MobileNet x0.543.1320.15993,92841.84MConverted from GL model (log)FD-MobileNet x0.7538.4216.411,833,30486.68MConverted from GL model (log)FD-MobileNet x1.034.2313.382,901,288147.46MConverted from GL model (log)MobileNetV2 x0.2548.3424.511,516,39234.24MConverted from GL model (log)MobileNetV2 x0.535.9814.931,964,736100.13MConverted from GL model (log)MobileNetV2 x0.7530.1710.822,627,592198.50MConverted from GL model (log)MobileNetV2 x1.026.978.873,504,960329.36MConverted from GL model (log)MobileNetV2b x0.2547.0523.681,516,31233.18MConverted from GL model (log)MobileNetV2b x0.534.7714.081,964,44896.42MConverted from GL model (log)MobileNetV2b x0.7530.7411.052,626,968190.52MConverted from GL model (log)MobileNetV2b x1.027.709.123,503,872315.51MConverted from GL model (log)MobileNetV3 L/224/1.024.557.445,481,752227.09MConverted from GL model (log)IGCV3 x0.2553.7028.711,534,02041.29MConverted from GL model (log)IGCV3 x0.539.7517.321,985,528111.12MConverted from GL model (log)IGCV3 x0.7531.0511.402,638,084210.95MConverted from GL model (log)IGCV3 x1.027.919.203,491,688340.79MConverted from GL model (log)MnasNet-B124.967.404,383,312326.30MConverted from GL model (log)MnasNet-A124.397.203,887,038326.07MConverted from GL model (log)DARTS25.317.754,718,752539.86MConverted from GL model (log)ProxylessNAS CPU24.717.614,361,648459.96MFromMIT-HAN-LAB/ProxylessNAS(log)ProxylessNAS GPU24.797.457,119,848476.08MConverted from GL model (log)ProxylessNAS Mobile25.417.804,080,512332.46MFromMIT-HAN-LAB/ProxylessNAS(log)ProxylessNAS Mob-1423.296.626,857,568597.10MConverted from GL model (log)FBNet-Cb24.897.625,572,200399.26MFromrwightman/pyt...models(log)Xception20.355.1622,855,9528,403.63MConverted from GL model (log)InceptionV320.445.3323,834,5685,743.06MConverted from GL model (log)InceptionV419.834.8842,679,81612,304.93MConverted from GL model (log)InceptionResNetV119.634.8023,995,6246,329.60MConverted from GL model (log)InceptionResNetV219.404.7455,843,46413,188.64MConverted from GL model (log)PolyNet19.104.5295,366,60034,821.34MFromCadene/pretrained...pytorch(log)NASNet-A 4@105625.328.035,289,978584.90MConverted from GL model (log)NASNet-A 6@403218.144.2188,753,15023,976.44MFromCadene/pretrained...pytorch(log)PNASNet-5-Large17.884.2886,057,66825,140.77MFromCadene/pretrained...pytorch(log)SPNASNet25.417.984,421,616346.73MConverted from GL model (log)EfficientNet-B024.777.525,288,548414.31MConverted from GL model (log)EfficientNet-B123.086.387,794,184732.54MConverted from GL model (log)EfficientNet-B0b23.887.025,288,548414.31MFromrwightman/pyt...models(log)EfficientNet-B1b21.605.947,794,184732.54MFromrwightman/pyt...models(log)EfficientNet-B2b20.315.279,109,9941,051.98MFromrwightman/pyt...models(log)EfficientNet-B3b18.834.4512,233,2321,928.55MFromrwightman/pyt...models(log)EfficientNet-B4b17.453.8919,341,6164,607.46MFromrwightman/pyt...models(log)EfficientNet-B5b16.563.3730,389,78410,695.20MFromrwightman/pyt...models(log)EfficientNet-B6b16.293.2343,040,70419,796.24MFromrwightman/pyt...models(log)EfficientNet-B7b15.943.2266,347,96039,010.98MFromrwightman/pyt...models(log)EfficientNet-B0c*22.926.755,288,548414.31MFromrwightman/pyt...models(log)EfficientNet-B1c*20.735.697,794,184732.54MFromrwightman/pyt...models(log)EfficientNet-B2c*19.855.039,109,9941,051.98MFromrwightman/pyt...models(log)EfficientNet-B3c*18.264.4212,233,2321,928.55MFromrwightman/pyt...models(log)EfficientNet-B4c*16.823.6919,341,6164,607.46MFromrwightman/pyt...models(log)EfficientNet-B5c*15.913.1030,389,78410,695.20MFromrwightman/pyt...models(log)EfficientNet-B6c*15.472.9643,040,70419,796.24MFromrwightman/pyt...models(log)EfficientNet-B7c*15.132.8866,347,96039,010.98MFromrwightman/pyt...models(log)EfficientNet-B8c*14.852.7687,413,14264,541.66MFromrwightman/pyt...models(log)EfficientNet-Edge-Small-b*22.746.405,438,3922,378.12MFromrwightman/pyt...models(log)EfficientNet-Edge-Medium-b*21.185.636,899,4963,700.12MFromrwightman/pyt...models(log)EfficientNet-Edge-Large-b*19.664.9110,589,7129,747.66MFromrwightman/pyt...models(log)MixNet-S23.927.174,134,606260.76MConverted from GL model (log)MixNet-M22.456.475,014,382366.68MConverted from GL model (log)MixNet-L21.545.717,329,252591.34MConverted from GL model (log)ResNet(A)-1031.2911.905,438,0241,135.86MConverted from GL model (log)ResNet(A)-BC-1428.069.9010,084,1681,721.57MConverted from GL model (log)ResNet(A)-1825.548.3111,708,7442,062.24MConverted from GL model (log)ResNet(A)-50b21.035.5625,576,2644,352.93MConverted from GL model (log)ResNet(A)-101b19.134.5344,568,3928,072.93MConverted from GL model (log)ResNet(A)-152b18.804.4160,212,04011,796.83MConverted from GL model (log)ResNet(D)-50b21.045.6525,680,80820,497.60MFromdmlc/gluon-cv(log)ResNet(D)-101b19.594.7344,672,93635,392.65MFromdmlc/gluon-cv(log)ResNet(D)-152b19.424.8260,316,58447,662.18MFromdmlc/gluon-cv(log)CIFAR-10ModelError, %ParamsFLOPs/2RemarksNIN7.43966,986222.97MConverted from GL model (log)ResNet-205.97272,47441.29MConverted from GL model (log)ResNet-564.52855,770127.06MConverted from GL model (log)ResNet-1103.691,730,714255.70MConverted from GL model (log)ResNet-164(BN)3.681,704,154255.31MConverted from GL model (log)ResNet-272(BN)3.332,816,986420.61MConverted from GL model (log)ResNet-542(BN)3.435,599,066833.87MConverted from GL model (log)ResNet-10013.2810,328,6021,536.40MConverted from GL model (log)ResNet-12023.5319,424,0262,857.17MConverted from GL model (log)PreResNet-206.51272,28241.27MConverted from GL model (log)PreResNet-564.49855,578127.03MConverted from GL model (log)PreResNet-1103.861,730,522255.68MConverted from GL model (log)PreResNet-164(BN)3.641,703,258255.08MConverted from GL model (log)PreResNet-272(BN)3.252,816,090420.38MConverted from GL model (log)PreResNet-542(BN)3.145,598,170833.64MConverted from GL model (log)PreResNet-10012.6510,327,7061,536.18MConverted from GL model (log)PreResNet-12023.3919,423,8342,857.14MConverted from GL model (log)ResNeXt-29 (32x4d)3.154,775,754780.55MConverted from GL model (log)ResNeXt-29 (16x64d)2.4168,155,21010,709.34MConverted from GL model (log)ResNeXt-272 (1x64d)2.5544,540,7466,565.15MConverted from GL model (log)ResNeXt-272 (2x32d)2.7432,928,5864,867.11MConverted from GL model (log)SE-ResNet-206.01274,84741.34MConverted from GL model (log)SE-ResNet-564.13862,889127.19MConverted from GL model (log)SE-ResNet-1103.631,744,952255.98MConverted from GL model (log)SE-ResNet-164(BN)3.391,906,258256.55MConverted from GL model (log)SE-ResNet-272(BN)3.393,153,826422.68MConverted from GL model (log)SE-ResNet-542(BN)3.476,272,746838.01MConverted from GL model (log)SE-PreResNet-206.18274,55941.35MConverted from GL model (log)SE-PreResNet-564.51862,601127.20MConverted from GL model (log)SE-PreResNet-1104.541,744,664255.98MConverted from GL model (log)SE-PreResNet-164(BN)3.731,904,882256.32MConverted from GL model (log)SE-PreResNet-272(BN)3.393,152,450422.45MConverted from GL model (log)SE-PreResNet-542(BN)3.086,271,370837.78MConverted from GL model (log)PyramidNet-110 (a=48)3.721,772,706408.37MConverted from GL model (log)PyramidNet-110 (a=84)2.983,904,446778.15MConverted from GL model (log)PyramidNet-110 (a=270)2.5128,485,4774,730.60MConverted from GL model (log)PyramidNet-164 (a=270, BN)2.4227,216,0214,608.81MConverted from GL model (log)PyramidNet-200 (a=240, BN)2.4426,752,7024,563.40MConverted from GL model (log)PyramidNet-236 (a=220, BN)2.4726,969,0464,631.32MConverted from GL model (log)PyramidNet-272 (a=200, BN)2.3926,210,8424,541.36MConverted from GL model (log)DenseNet-40 (k=12)5.61599,050210.80MConverted from GL model (log)DenseNet-BC-40 (k=12)6.43176,12274.89MConverted from GL model (log)DenseNet-BC-40 (k=24)4.52690,346293.09MConverted from GL model (log)DenseNet-BC-40 (k=36)4.041,542,682654.60MConverted from GL model (log)DenseNet-100 (k=12)3.664,068,4901,353.55MConverted from GL model (log)DenseNet-100 (k=24)3.1316,114,1385,354.19MConverted from GL model (log)DenseNet-BC-100 (k=12)4.16769,162298.45MConverted from GL model (log)DenseNet-BC-190 (k=40)2.5225,624,4309,400.45MConverted from GL model (log)DenseNet-BC-250 (k=24)2.6715,324,4065,519.54MConverted from GL model (log)X-DenseNet-BC-40-2 (k=24)5.31690,346293.09MConverted from GL model (log)X-DenseNet-BC-40-2 (k=36)4.371,542,682654.60MConverted from GL model (log)WRN-16-102.9317,116,6342,414.04MConverted from GL model (log)WRN-28-102.3936,479,1945,246.98MConverted from GL model (log)WRN-40-82.3735,748,3145,176.90MConverted from GL model (log)WRN-20-10-1bit3.2626,737,1404,019.14MConverted from GL model (log)WRN-20-10-32bit3.1426,737,1404,019.14MConverted from GL model (log)RoR-3-565.43762,746113.43MConverted from GL model (log)RoR-3-1104.351,637,690242.07MConverted from GL model (log)RoR-3-1643.932,512,634370.72MConverted from GL model (log)RiR3.289,492,9801,281.08MConverted from GL model (log)Shake-Shake-ResNet-20-2x16d5.15541,08281.78MConverted from GL model (log)Shake-Shake-ResNet-26-2x32d3.172,923,162428.89MConverted from GL model (log)DIA-ResNet-206.22286,86641.54MConverted from GL model (log)DIA-ResNet-565.05870,162129.31MConverted from GL model (log)DIA-ResNet-1104.101,745,106264.71MConverted from GL model (log)DIA-ResNet-164(BN)3.501,923,002343.60MConverted from GL model (log)DIA-PreResNet-206.42286,67441.52MConverted from GL model (log)DIA-PreResNet-564.83869,970129.28MConverted from GL model (log)DIA-PreResNet-1104.251,744,914264.69MConverted from GL model (log)DIA-PreResNet-164(BN)3.561,922,106343.37MConverted from GL model (log)CIFAR-100ModelError, %ParamsFLOPs/2RemarksNIN28.39984,356224.08MConverted from GL model (log)ResNet-2029.64278,32441.30MConverted from GL model (log)ResNet-5624.88861,620127.06MConverted from GL model (log)ResNet-11022.801,736,564255.71MConverted from GL model (log)ResNet-164(BN)20.441,727,284255.33MConverted from GL model (log)ResNet-272(BN)20.072,840,116420.63MConverted from GL model (log)ResNet-542(BN)19.325,622,196833.89MConverted from GL model (log)ResNet-100119.7910,351,7321,536.43MConverted from GL model (log)ResNet-120221.5619,429,8762,857.17MConverted from GL model (log)PreResNet-2030.22278,13241.28MConverted from GL model (log)PreResNet-5625.05861,428127.04MConverted from GL model (log)PreResNet-11022.671,736,372255.68MConverted from GL model (log)PreResNet-164(BN)20.181,726,388255.10MConverted from GL model (log)PreResNet-272(BN)19.632,839,220420.40MConverted from GL model (log)PreResNet-542(BN)18.715,621,300833.66MConverted from GL model (log)PreResNet-100118.4110,350,8361,536.20MConverted from GL model (log)ResNeXt-29 (32x4d)19.504,868,004780.64MConverted from GL model (log)ResNeXt-29 (16x64d)16.9368,247,46010,709.43MConverted from GL model (log)ResNeXt-272 (1x64d)19.1144,632,9966,565.25MConverted from GL model (log)ResNeXt-272 (2x32d)18.3433,020,8364,867.20MConverted from GL model (log)SE-ResNet-2028.54280,69741.35MConverted from GL model (log)SE-ResNet-5622.94868,739127.07MConverted from GL model (log)SE-ResNet-11020.861,750,802255.98MConverted from GL model (log)SE-ResNet-164(BN)19.951,929,388256.57MConverted from GL model (log)SE-ResNet-272(BN)19.073,176,956422.70MConverted from GL model (log)SE-ResNet-542(BN)18.876,295,876838.03MConverted from GL model (log)SE-PreResNet-2028.31280,40941.35MConverted from GL model (log)SE-PreResNet-5623.05868,451127.21MConverted from GL model (log)SE-PreResNet-11022.611,750,514255.99MConverted from GL model (log)SE-PreResNet-164(BN)20.051,928,012256.34MConverted from GL model (log)SE-PreResNet-272(BN)19.133,175,580422.47MConverted from GL model (log)SE-PreResNet-542(BN)19.456,294,500837.80MConverted from GL model (log)PyramidNet-110 (a=48)20.951,778,556408.38MConverted from GL model (log)PyramidNet-110 (a=84)18.873,913,536778.16MConverted from GL model (log)PyramidNet-110 (a=270)17.1028,511,3074,730.62MConverted from GL model (log)PyramidNet-164 (a=270, BN)16.7027,319,0714,608.91MConverted from GL model (log)PyramidNet-200 (a=240, BN)16.0926,844,9524,563.49MConverted from GL model (log)PyramidNet-236 (a=220, BN)16.3427,054,0964,631.41MConverted from GL model (log)PyramidNet-272 (a=200, BN)16.1926,288,6924,541.43MConverted from GL model (log)DenseNet-40 (k=12)24.90622,360210.82MConverted from GL model (log)DenseNet-BC-40 (k=12)28.41188,09274.90MConverted from GL model (log)DenseNet-BC-40 (k=24)22.67714,196293.11MConverted from GL model (log)DenseNet-BC-40 (k=36)20.501,578,412654.64MConverted from GL model (log)DenseNet-100 (k=12)19.644,129,6001,353.62MConverted from GL model (log)DenseNet-100 (k=24)18.0816,236,2685,354.32MConverted from GL model (log)DenseNet-BC-100 (k=12)21.19800,032298.48MConverted from GL model (log)DenseNet-BC-250 (k=24)17.3915,480,5565,519.69MConverted from GL model (log)X-DenseNet-BC-40-2 (k=24)23.96714,196293.11MConverted from GL model (log)X-DenseNet-BC-40-2 (k=36)21.651,578,412654.64MConverted from GL model (log)WRN-16-1018.9517,174,3242,414.09MConverted from GL model (log)WRN-28-1017.8836,536,8845,247.04MConverted from GL model (log)WRN-40-818.0335,794,4845,176.95MConverted from GL model (log)WRN-20-10-1bit19.0426,794,9204,022.81MConverted from GL model (log)WRN-20-10-32bit18.1226,794,9204,022.81MConverted from GL model (log)RoR-3-5625.49768,596113.43MConverted from GL model (log)RoR-3-11023.641,643,540242.08MConverted from GL model (log)RoR-3-16422.342,518,484370.72MConverted from GL model (log)RiR19.239,527,7201,283.29MConverted from GL model (log)Shake-Shake-ResNet-20-2x16d29.22546,93281.79MConverted from GL model (log)Shake-Shake-ResNet-26-2x32d18.802,934,772428.90MConverted from GL model (log)DIA-ResNet-2027.71292,71641.55MConverted from GL model (log)DIA-ResNet-5624.35876,012129.32MConverted from GL model (log)DIA-ResNet-11022.111,750,956264.72MConverted from GL model (log)DIA-ResNet-164(BN)19.531,946,132343.62MConverted from GL model (log)DIA-PreResNet-2028.37292,52441.53MConverted from GL model (log)DIA-PreResNet-5625.05875,820129.29MConverted from GL model (log)DIA-PreResNet-11022.691,750,764264.69MConverted from GL model (log)DIA-PreResNet-164(BN)19.991,945,236343.39MConverted from GL model (log)SVHNModelError, %ParamsFLOPs/2RemarksNIN3.76966,986222.97MConverted from GL model (log)ResNet-203.43272,47441.29MConverted from GL model (log)ResNet-562.75855,770127.06MConverted from GL model (log)ResNet-1102.451,730,714255.70MConverted from GL model (log)ResNet-164(BN)2.421,704,154255.31MConverted from GL model (log)ResNet-272(BN)2.432,816,986420.61MConverted from GL model (log)ResNet-542(BN)2.345,599,066833.87MConverted from GL model (log)ResNet-10012.4110,328,6021,536.40MConverted from GL model (log)PreResNet-203.22272,28241.27MConverted from GL model (log)PreResNet-562.80855,578127.03MConverted from GL model (log)PreResNet-1102.791,730,522255.68MConverted from GL model (log)PreResNet-164(BN)2.581,703,258255.08MConverted from GL model (log)PreResNet-272(BN)2.342,816,090420.38MConverted from GL model (log)PreResNet-542(BN)2.365,598,170833.64MConverted from GL model (log)ResNeXt-29 (32x4d)2.804,775,754780.55MConverted from GL model (log)ResNeXt-29 (16x64d)2.6868,155,21010,709.34MConverted from GL model (log)ResNeXt-272 (1x64d)2.3544,540,7466,565.15MConverted from GL model (log)ResNeXt-272 (2x32d)2.4432,928,5864,867.11MConverted from GL model (log)SE-ResNet-203.23274,84741.34MConverted from GL model (log)SE-ResNet-562.64862,889127.19MConverted from GL model (log)SE-ResNet-1102.351,744,952255.98MConverted from GL model (log)SE-ResNet-164(BN)2.451,906,258256.55MConverted from GL model (log)SE-ResNet-272(BN)2.383,153,826422.68MConverted from GL model (log)SE-ResNet-542(BN)2.266,272,746838.01MConverted from GL model (log)SE-PreResNet-203.24274,55941.35MConverted from GL model (log)SE-PreResNet-562.71862,601127.20MConverted from GL model (log)SE-PreResNet-1102.591,744,664255.98MConverted from GL model (log)SE-PreResNet-164(BN)2.561,904,882256.32MConverted from GL model (log)SE-PreResNet-272(BN)2.493,152,450422.45MConverted from GL model (log)SE-PreResNet-542(BN)2.476,271,370837.78MConverted from GL model (log)PyramidNet-110 (a=48)2.471,772,706408.37MConverted from GL model (log)PyramidNet-110 (a=84)2.433,904,446778.15MConverted from GL model (log)PyramidNet-110 (a=270)2.3828,485,4774,730.60MConverted from GL model (log)PyramidNet-164 (a=270, BN)2.3327,216,0214,608.81MConverted from GL model (log)PyramidNet-200 (a=240, BN)2.3226,752,7024,563.40MConverted from GL model (log)PyramidNet-236 (a=220, BN)2.3526,969,0464,631.32MConverted from GL model (log)PyramidNet-272 (a=200, BN)2.4026,210,8424,541.36MConverted from GL model (log)DenseNet-40 (k=12)3.05599,050210.80MConverted from GL model (log)DenseNet-BC-40 (k=12)3.20176,12274.89MConverted from GL model (log)DenseNet-BC-40 (k=24)2.90690,346293.09MConverted from GL model (log)DenseNet-BC-40 (k=36)2.601,542,682654.60MConverted from GL model (log)DenseNet-100 (k=12)2.604,068,4901,353.55MConverted from GL model (log)X-DenseNet-BC-40-2 (k=24)2.87690,346293.09MConverted from GL model (log)X-DenseNet-BC-40-2 (k=36)2.741,542,682654.60MConverted from GL model (log)WRN-16-102.7817,116,6342,414.04MConverted from GL model (log)WRN-28-102.7136,479,1945,246.98MConverted from GL model (log)WRN-40-82.5435,748,3145,176.90MConverted from GL model (log)WRN-20-10-1bit2.7326,737,1404,019.14MConverted from GL model (log)WRN-20-10-32bit2.5926,737,1404,019.14MConverted from GL model (log)RoR-3-562.69762,746113.43MConverted from GL model (log)RoR-3-1102.571,637,690242.07MConverted from GL model (log)RoR-3-1642.732,512,634370.72MConverted from GL model (log)RiR2.689,492,9801,281.08MConverted from GL model (log)Shake-Shake-ResNet-20-2x16d3.17541,08281.78MConverted from GL model (log)Shake-Shake-ResNet-26-2x32d2.622,923,162428.89MConverted from GL model (log)DIA-ResNet-203.23286,86641.54MConverted from GL model (log)DIA-ResNet-562.68870,162129.31MConverted from GL model (log)DIA-ResNet-1102.471,745,106264.71MConverted from GL model (log)DIA-ResNet-164(BN)2.441,923,002343.60MConverted from GL model (log)DIA-PreResNet-203.03286,67441.52MConverted from GL model (log)DIA-PreResNet-562.80869,970129.28MConverted from GL model (log)DIA-PreResNet-1102.421,744,914264.69MConverted from GL model (log)DIA-PreResNet-164(BN)2.561,922,106343.37MConverted from GL model (log)CUB-200-2011ModelError, %ParamsFLOPs/2RemarksResNet-1027.775,008,392893.63MConverted from GL model (log)ResNet-1227.275,082,3761,125.84MConverted from GL model (log)ResNet-1424.775,377,8001,357.53MConverted from GL model (log)ResNet-1623.656,558,4721,588.93MConverted from GL model (log)ResNet-1823.3311,279,1121,820.00MConverted from GL model (log)ResNet-2623.1617,549,8322,746.38MConverted from GL model (log)SE-ResNet-1027.725,052,932893.86MConverted from GL model (log)SE-ResNet-1226.515,127,4961,126.17MConverted from GL model (log)SE-ResNet-1424.165,425,1041,357.92MConverted from GL model (log)SE-ResNet-1623.326,614,2401,589.35MConverted from GL model (log)SE-ResNet-1823.5211,368,1921,820.47MConverted from GL model (log)SE-ResNet-2622.9917,683,4522,747.08MConverted from GL model (log)MobileNet x1.023.773,411,976578.98MConverted from GL model (log)ProxylessNAS Mobile22.663,055,712331.44MConverted from GL model (log)NTS-Net12.7728,623,33333,361.79MFromyangze0930/NTS-Net(log)Pascal VOC20102ModelExtractorPix.Acc.,%mIoU,%ParamsFLOPs/2RemarksPSPNetResNet(D)-101b98.0981.4465,708,501230,771.01MFromdmlc/gluon-cv(log)DeepLabv3ResNet(D)-101b97.9580.2458,754,77347,625.34MFromdmlc/gluon-cv(log)DeepLabv3ResNet(D)-152b98.1181.2074,398,42159,894.87MFromdmlc/gluon-cv(log)FCN-8s(d)ResNet(D)-101b97.8080.4052,072,917196,562.96MFromdmlc/gluon-cv(log)ADE20KModelExtractorPix.Acc.,%mIoU,%ParamsFLOPs/2RemarksPSPNetResNet(D)-50b79.3736.8746,782,550162,595.14MFromdmlc/gluon-cv(log)PSPNetResNet(D)-101b79.9337.9765,774,678231,008.79MFromdmlc/gluon-cv(log)DeepLabv3ResNet(D)-50b79.7237.1339,795,79832,756.18MFromdmlc/gluon-cv(log)DeepLabv3ResNet(D)-101b80.2137.8458,787,92647,651.23MFromdmlc/gluon-cv(log)FCN-8s(d)ResNet(D)-50b76.9233.3933,146,966128,387.08MFromdmlc/gluon-cv(log)FCN-8s(d)ResNet(D)-101b79.0135.8852,139,094196,800.73MFromdmlc/gluon-cv(log)CityscapesModelExtractorPix.Acc.,%mIoU,%ParamsFLOPs/2RemarksPSPNetResNet(D)-101b96.1771.7265,707,475230,767.33MFromdmlc/gluon-cv(log)ICNetResNet(D)-50b95.5064.0247,489,18414,253.43MFromdmlc/gluon-cv(log)SINet-94.0861.72119,4181,419.90MFromclovaai/c3_sinet(log)Fast-SCNN-95.1465.761,138,0513493.33MFromdmlc/gluon-cv(log)DANetResNet(D)-50b95.9167.9947,586,427180,397.43MFromdmlc/gluon-cv(log)DANetResNet(D)-101b96.0368.1066,578,555248,811.08MFromdmlc/gluon-cv(log)COCO Semantic SegmentationModelExtractorPix.Acc.,%mIoU,%ParamsFLOPs/2RemarksPSPNetResNet(D)-101b92.0567.4165,708,501230,771.01MFromdmlc/gluon-cv(log)DeepLabv3ResNet(D)-101b92.1967.7358,754,77347,625.34MFromdmlc/gluon-cv(log)DeepLabv3ResNet(D)-152b92.2468.9974,398,421275,087.91MFromdmlc/gluon-cv(log)FCN-8s(d)ResNet(D)-101b91.4460.1152,072,917196,562.96MFromdmlc/gluon-cv(log)CelebAMask-HQModelExtractorParamsFLOPs/2RemarksBiSeNetResNet-1813,300,416-Fromzllrunning/face...Torch(log)COCO Keypoints DetectionModelExtractorOKS AP, %ParamsFLOPs/2RemarksAlphaPoseFast-SE-ResNet-101b74.15/91.59/80.6859,569,8739,553.89MFromdmlc/gluon-cv(log)SimplePoseResNet-1866.31/89.20/73.4115,376,7211,799.25MFromdmlc/gluon-cv(log)SimplePoseResNet-50b71.02/91.23/78.5733,999,6974,041.06MFromdmlc/gluon-cv(log)SimplePoseResNet-101b72.44/92.18/79.7652,991,8257,685.04MFromdmlc/gluon-cv(log)SimplePoseResNet-152b72.53/92.14/79.6168,635,47311,332.86MFromdmlc/gluon-cv(log)SimplePoseResNet(A)-50b71.70/91.31/78.6634,018,9294,278.56MFromdmlc/gluon-cv(log)SimplePoseResNet(A)-101b72.97/92.24/80.8153,011,0577,922.54MFromdmlc/gluon-cv(log)SimplePoseResNet(A)-152b73.44/92.27/80.7268,654,70511,570.36MFromdmlc/gluon-cv(log)SimplePose(Mobile)ResNet-1866.25/89.17/74.3212,858,2081,960.96MFromdmlc/gluon-cv(log)SimplePose(Mobile)ResNet-50b71.10/91.28/78.6725,582,9444,221.30MFromdmlc/gluon-cv(log)SimplePose(Mobile)1.0 MobileNet-22464.10/88.06/71.235,019,744751.36MFromdmlc/gluon-cv(log)SimplePose(Mobile)1.0 MobileNetV2b-22463.74/88.12/71.064,102,176495.95MFromdmlc/gluon-cv(log)SimplePose(Mobile)MobileNetV3 Small 224/1.054.34/83.67/59.352,625,088236.51MFromdmlc/gluon-cv(log)SimplePose(Mobile)MobileNetV3 Large 224/1.063.67/88.91/70.824,768,336403.97MFromdmlc/gluon-cv(log)Lightweight OpenPose 2DMobileNet39.99/65.95/40.704,091,6988,948.96MFromDaniil-Osokin/lighw...ch(log)Lightweight OpenPose 3DMobileNet39.99/65.95/40.705,085,98311,049.43MFromDaniil-Osokin/li...3d...ch(log)IBPPose-64.87/83.62/70.1395,827,78457,195.91MFromjialee93/Improved...Parts(log)Mozilla Common Voice (Corpus 6.1, dev subset)Some remarks:NR means Noise Reduction.LS means trained on LibriSpeech dataset.ModelLangWER, %ParamsFLOPs/2RemarksJasper DR 10x5En21.92332,632,34985,142.96MFromNVIDIA/NeMo(log)Jasper DR 10x5 NREn17.92332,632,34985,142.96MFromNVIDIA/NeMo(log)QuartzNet 5x5 LSEn44.696,713,1811,717.12MFromNVIDIA/NeMo(log)QuartzNet 15x5En16.7918,924,3814,840.29MFromNVIDIA/NeMo(log)QuartzNet 15x5 NREn17.7618,924,3814,840.29MFromNVIDIA/NeMo(log)QuartzNet 15x5De11.6718,927,4564,841.08MFromNVIDIA/NeMo(log)QuartzNet 15x5Fr13.8818,938,7314,843.96MFromNVIDIA/NeMo(log)QuartzNet 15x5It15.0218,934,6314,842.91MFromNVIDIA/NeMo(log)QuartzNet 15x5Es12.9518,931,5564,842.13MFromNVIDIA/NeMo(log)QuartzNet 15x5Ca8.4218,934,6314,842.91MFromNVIDIA/NeMo(log)QuartzNet 15x5Pl13.5918,929,5064,841.60MFromNVIDIA/NeMo(log)QuartzNet 15x5Ru16.4818,930,5314,841.87MFromNVIDIA/NeMo(log)QuartzNet 15x5Ru/349.6918,929,5064,841.60MFromsberdevices/golos(log)