pytorchcv

Computer vision models on PyTorch

This is a collection of image classification, segmentation, detection, pose estimation, video inpainting models. Many of them are pretrained on ImageNet-1K, CIFAR-10/100, SVHN, CUB-200-2011, Pascal VOC2012, ADE20K, Cityscapes, and COCO datasets and loaded automatically during use. All pretrained models require the same ordinary normalization. Scripts for training/evaluating/converting models are in the imgclsmob repo.

List of implemented models

• AlexNet ('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')
• RAFT ('RAFT: Recurrent All-Pairs Field Transforms for Optical Flow')
• ProPainter ('ProPainter: Improving Propagation and Transformer for Video Inpainting') (LICENSE)

Installation

To use the models in your project, simply install the pytorchcv:

pip install pytorchcv

To enable/disable different hardware supports such as GPUs, check out PyTorch installation instructions. Some models require additional specific packages to work. For example, ASR models require the librosa package.

Usage

Example 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 models

ImageNet-1K

Some 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.
• Remark Converted from GL model means that the model was trained on MXNet/Gluon and 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 OpenCV Resize transformation 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).

Model	Top1	Top5	Params	FLOPs/2	Remarks
AlexNet	38.50	16.64	62,378,344	1,132.33M	Converted from GL model (log)
AlexNet-b	39.74	17.47	61,100,840	714.83M	Converted from GL model (log)
ZFNet	39.79	17.27	62,357,608	1,170.33M	Converted from GL model (log)
ZFNet-b	36.37	14.90	107,627,624	2,479.13M	Converted from GL model (log)
VGG-11	29.90	10.36	132,863,336	7,615.87M	Converted from GL model (log)
VGG-13	28.76	9.75	133,047,848	11,317.65M	Converted from GL model (log)
VGG-16	26.98	8.65	138,357,544	15,480.10M	Converted from GL model (log)
VGG-19	25.74	7.90	143,667,240	19,642.55M	Converted from GL model (log)
BN-VGG-11	29.01	9.61	132,866,088	7,630.21M	Converted from GL model (log)
BN-VGG-13	27.83	9.13	133,050,792	11,341.62M	Converted from GL model (log)
BN-VGG-16	25.72	7.79	138,361,768	15,506.38M	Converted from GL model (log)
BN-VGG-19	24.13	7.12	143,672,744	19,671.15M	Converted from GL model (log)
BN-VGG-11b	29.56	9.96	132,868,840	7,630.72M	Converted from GL model (log)
BN-VGG-13b	28.47	9.24	133,053,736	11,342.14M	Converted from GL model (log)
BN-VGG-16b	25.97	7.95	138,365,992	15,507.20M	Converted from GL model (log)
BN-VGG-19b	25.13	7.46	143,678,248	19,672.26M	Converted from GL model (log)
BN-Inception	25.37	7.74	11,295,240	2,048.06M	Converted from GL model (log)
ResNet-10	32.43	12.53	5,418,792	894.04M	Converted from GL model (log)
ResNet-12	32.10	12.23	5,492,776	1,126.25M	Converted from GL model (log)
ResNet-14	30.51	11.09	5,788,200	1,357.94M	Converted from GL model (log)
ResNet-BC-14b	29.43	10.74	10,064,936	1,479.12M	Converted from GL model (log)
ResNet-16	28.74	10.09	6,968,872	1,589.34M	Converted from GL model (log)
ResNet-18 x0.25	39.62	17.85	3,937,400	270.94M	Converted from GL model (log)
ResNet-18 x0.5	33.80	13.27	5,804,296	608.70M	Converted from GL model (log)
ResNet-18 x0.75	30.40	11.06	8,476,056	1,129.45M	Converted from GL model (log)
ResNet-18	26.94	8.96	11,689,512	1,820.41M	Converted from GL model (log)
ResNet-26	26.16	8.49	17,960,232	2,746.79M	Converted from GL model (log)
ResNet-BC-26b	25.09	7.97	15,995,176	2,356.67M	Converted from GL model (log)
ResNet-34	24.84	7.80	21,797,672	3,672.68M	Converted from GL model (log)
ResNet-BC-38b	23.69	7.00	21,925,416	3,234.21M	Converted from GL model (log)
ResNet-50	22.28	6.33	25,557,032	3,877.95M	Converted from GL model (log)
ResNet-50b	22.39	6.38	25,557,032	4,110.48M	Converted from GL model (log)
ResNet-101	20.79	5.40	44,549,160	7,597.95M	Converted from GL model (log)
ResNet-101b	20.59	5.30	44,549,160	7,830.48M	Converted from GL model (log)
ResNet-152	19.29	4.68	60,192,808	11,321.85M	Converted from GL model (log)
ResNet-152b	19.01	4.45	60,192,808	11,554.38M	Converted from GL model (log)
PreResNet-10	35.11	14.21	5,417,128	894.19M	Converted from GL model (log)
PreResNet-12	33.86	13.48	5,491,112	1,126.40M	Converted from GL model (log)
PreResNet-14	32.64	12.39	5,786,536	1,358.09M	Converted from GL model (log)
PreResNet-BC-14b	31.29	11.81	10,057,384	1,476.62M	Converted from GL model (log)
PreResNet-16	30.53	11.08	6,967,208	1,589.49M	Converted from GL model (log)
PreResNet-18 x0.25	40.06	18.11	3,935,960	270.93M	Converted from GL model (log)
PreResNet-18 x0.5	34.00	13.40	5,802,440	608.73M	Converted from GL model (log)
PreResNet-18 x0.75	30.23	11.05	8,473,784	1,129.51M	Converted from GL model (log)
PreResNet-18	28.43	9.72	11,687,848	1,820.56M	Converted from GL model (log)
PreResNet-26	26.33	8.51	17,958,568	2,746.94M	Converted from GL model (log)
PreResNet-BC-26b	25.48	8.03	15,987,624	2,354.16M	Converted from GL model (log)
PreResNet-34	24.89	7.74	21,796,008	3,672.83M	Converted from GL model (log)
PreResNet-BC-38b	22.92	6.57	21,917,864	3,231.70M	Converted from GL model (log)
PreResNet-50	22.40	6.47	25,549,480	3,875.44M	Converted from GL model (log)
PreResNet-50b	22.51	6.55	25,549,480	4,107.97M	Converted from GL model (log)
PreResNet-101	20.81	5.63	44,541,608	7,595.44M	Converted from GL model (log)
PreResNet-101b	21.04	5.56	44,541,608	7,827.97M	Converted from GL model (log)
PreResNet-152	19.36	4.64	60,185,256	11,319.34M	Converted from GL model (log)
PreResNet-152b	19.23	4.59	60,185,256	11,551.87M	Converted from GL model (log)
PreResNet-200b	19.07	4.68	64,666,280	15,068.63M	Converted from GL model (log)
PreResNet-269b	20.35	5.27	102,065,832	20,101.11M	Converted from GL model (log)
ResNeXt-14 (16x4d)	31.94	12.48	7,127,336	1,045.77M	Converted from GL model (log)
ResNeXt-14 (32x2d)	32.58	12.81	7,029,416	1,031.32M	Converted from GL model (log)
ResNeXt-14 (32x4d)	30.32	11.46	9,411,880	1,603.46M	Converted from GL model (log)
ResNeXt-26 (32x2d)	26.63	8.87	9,924,136	1,461.06M	Converted from GL model (log)
ResNeXt-26 (32x4d)	24.14	7.46	15,389,480	2,488.07M	Converted from GL model (log)
ResNeXt-50 (32x4d)	21.08	5.60	25,028,904	4,255.86M	Converted from GL model (log)
ResNeXt-101 (32x4d)	18.78	4.34	44,177,704	8,003.45M	Converted from GL model (log)
ResNeXt-101 (64x4d)	19.08	4.52	83,455,272	15,500.27M	Converted from GL model (log)
SE-ResNet-10	31.76	12.02	5,463,332	894.27M	Converted from GL model (log)
SE-ResNet-12	31.93	12.00	5,537,896	1,126.58M	Converted from GL model (log)
SE-ResNet-14	30.73	11.28	5,835,504	1,358.33M	Converted from GL model (log)
SE-ResNet-16	28.72	9.98	7,024,640	1,589.76M	Converted from GL model (log)
SE-ResNet-18	28.18	9.61	11,778,592	1,820.88M	Converted from GL model (log)
SE-ResNet-26	25.67	8.24	18,093,852	2,747.49M	Converted from GL model (log)
SE-ResNet-BC-26b	23.59	7.03	17,395,976	2,359.58M	Converted from GL model (log)
SE-ResNet-BC-38b	21.60	5.95	24,026,616	3,238.58M	Converted from GL model (log)
SE-ResNet-50	21.22	5.75	28,088,024	3,883.25M	Converted from GL model (log)
SE-ResNet-50b	20.79	5.39	28,088,024	4,115.78M	Converted from GL model (log)
SE-ResNet-101	19.24	4.60	49,326,872	7,602.76M	Converted from GL model (log)
SE-ResNet-101b	19.70	4.87	49,326,872	7,839.75M	Converted from GL model (log)
SE-ResNet-152	18.72	4.39	66,821,848	11,328.52M	Converted from GL model (log)
SE-PreResNet-10	32.83	12.57	5,461,668	894.42M	Converted from GL model (log)
SE-PreResNet-12	32.07	12.03	5,536,232	1,126.73M	Converted from GL model (log)
SE-PreResNet-16	28.77	9.75	7,022,976	1,589.91M	Converted from GL model (log)
SE-PreResNet-18	27.43	9.09	11,776,928	1,821.03M	Converted from GL model (log)
SE-PreResNet-26	26.20	8.22	18,092,188	2,747.64M	Converted from GL model (log)
SE-PreResNet-BC-26b	23.22	6.60	17,388,424	2,357.07M	Converted from GL model (log)
SE-PreResNet-BC-38b	21.60	5.78	24,019,064	3,236.07M	Converted from GL model (log)
SE-PreResNet-50b	20.85	5.49	28,080,472	4,113.27M	Converted from GL model (log)
SE-ResNeXt-50 (32x4d)	18.94	4.51	27,559,896	4,261.16M	Converted from GL model (log)
SE-ResNeXt-101 (32x4d)	19.36	4.67	48,955,416	8,012.73M	Converted from GL model (log)
SE-ResNeXt-101 (64x4d)	18.66	4.28	88,232,984	15,509.54M	Converted from GL model (log)
SENet-16	25.65	8.20	31,366,168	5,081.30M	Converted from GL model (log)
SENet-28	21.94	5.98	36,453,768	5,732.71M	Converted from GL model (log)
SENet-154	18.85	4.55	115,088,984	20,745.78M	Converted from GL model (log)
ResNeSt(A)-BC-14	22.46	6.47	10,611,688	2,767.37M	Converted from GL model (log)
ResNeSt(A)-18	23.68	7.07	12,763,784	2,587.50M	Converted from GL model (log)
ResNeSt(A)-BC-26	19.56	4.76	17,069,448	3,646.57M	Converted from GL model (log)
ResNeSt(A)-50	19.00	4.49	27,483,240	5,403.11M	Converted from GL model (log)
ResNeSt(A)-101	17.83	4.03	48,275,016	10,247.88M	From dmlc/gluon-cv (log)
ResNeSt(A)-152	18.87	4.63	65,316,040	13,976.34M	Converted from GL model (log)
ResNeSt(A)-200	16.87	3.39	70,201,544	22,857.88M	From dmlc/gluon-cv (log)
ResNeSt(A)-269	16.47	3.38	110,929,480	46,012.95M	From dmlc/gluon-cv (log)
IBN-ResNet-50	21.61	5.76	25,557,032	4,110.48M	Converted from GL model (log)
IBN-ResNet-101	19.97	5.07	44,549,160	7,830.48M	Converted from GL model (log)
IBN(b)-ResNet-50	21.79	5.97	25,558,568	4,112.89M	Converted from GL model (log)
IBN-ResNeXt-101 (32x4d)	20.10	5.12	44,177,704	8,003.45M	Converted from GL model (log)
IBN-DenseNet-121	23.58	6.73	7,978,856	2,872.13M	Converted from GL model (log)
IBN-DenseNet-169	22.42	6.19	14,149,480	3,403.89M	Converted from GL model (log)
AirNet50-1x64d (r=2)	20.63	5.32	27,425,864	4,772.11M	Converted from GL model (log)
AirNet50-1x64d (r=16)	21.24	5.60	25,714,952	4,399.97M	Converted from GL model (log)
AirNeXt50-32x4d (r=2)	20.14	5.15	27,604,296	5,339.58M	Converted from GL model (log)
BAM-ResNet-50	20.91	5.47	25,915,099	4,196.09M	Converted from GL model (log)
CBAM-ResNet-50	20.17	5.05	28,089,624	4,116.97M	Converted from GL model (log)
SCNet-50	21.22	5.47	25,564,584	3,951.06M	Converted from GL model (log)
SCNet-101	19.91	4.84	44,565,416	7,204.24M	Converted from GL model (log)
SCNet(A)-50	19.53	4.68	25,583,816	4,715.84M	From MCG-NKU/SCNet (log)
RegNetX-200MF	30.20	10.66	2,684,792	203.33M	Converted from GL model (log)
RegNetX-400MF	26.44	8.66	5,157,512	403.45M	Converted from GL model (log)
RegNetX-600MF	24.96	7.91	6,196,040	608.37M	Converted from GL model (log)
RegNetX-800MF	24.21	7.40	7,259,656	809.49M	Converted from GL model (log)
RegNetX-1.6GF	22.44	6.37	9,190,136	1,618.99M	Converted from GL model (log)
RegNetX-3.2GF	21.48	5.92	15,296,552	3,199.55M	Converted from GL model (log)
RegNetX-4.0GF	19.69	4.88	22,118,248	3,986.29M	Converted from GL model (log)
RegNetX-6.4GF	19.50	4.68	26,209,256	6,491.01M	Converted from GL model (log)
RegNetX-8.0GF	19.75	4.86	39,572,648	8,017.94M	Converted from GL model (log)
RegNetX-12GF	20.02	5.32	46,106,056	12,124.22M	Converted from GL model (log)
RegNetX-16GF	19.29	4.77	54,278,536	15,986.64M	Converted from GL model (log)
RegNetX-32GF	18.00	4.13	107,811,560	31,790.24M	Converted from GL model (log)
RegNetY-200MF	28.66	9.80	3,162,996	203.99M	Converted from GL model (log)
RegNetY-400MF	25.02	7.69	4,344,144	410.35M	Converted from GL model (log)
RegNetY-600MF	23.79	7.12	6,055,160	610.37M	Converted from GL model (log)
RegNetY-800MF	22.67	6.60	6,263,168	808.62M	Converted from GL model (log)
RegNetY-1.6GF	21.41	5.81	11,202,430	1,629.48M	Converted from GL model (log)
RegNetY-3.2GF	18.04	4.04	19,436,338	3,199.15M	From rwightman/pyt...models (log)
RegNetY-4.0GF	19.56	4.70	20,646,656	3,999.16M	Converted from GL model (log)
RegNetY-6.4GF	19.03	4.56	30,583,252	6,388.91M	Converted from GL model (log)
RegNetY-8.0GF	18.91	4.48	39,180,068	7,996.54M	Converted from GL model (log)
RegNetY-12GF	18.67	4.42	51,822,544	12,132.55M	Converted from GL model (log)
RegNetY-16GF	18.82	4.44	83,590,140	15,944.53M	Converted from GL model (log)
RegNetY-32GF	17.92	3.83	145,046,770	32,317.66M	Converted from GL model (log)
PyramidNet-101 (a=360)	20.53	5.43	42,455,070	8,743.54M	Converted from GL model (log)
DiracNetV2-18	31.47	11.70	11,511,784	1,796.62M	From szagoruyko/diracnets (log)
DiracNetV2-34	28.75	9.93	21,616,232	3,646.93M	From szagoruyko/diracnets (log)
DenseNet-121	23.48	7.04	7,978,856	2,872.13M	Converted from GL model (log)
DenseNet-161	21.91	6.06	28,681,000	7,793.16M	Converted from GL model (log)
DenseNet-169	22.42	6.29	14,149,480	3,403.89M	Converted from GL model (log)
DenseNet-201	21.78	6.12	20,013,928	4,347.15M	Converted from GL model (log)
CondenseNet-74 (C=G=4)	26.25	8.28	4,773,944	546.06M	From ShichenLiu/CondenseNet (log)
CondenseNet-74 (C=G=8)	28.93	10.06	2,935,416	291.52M	From ShichenLiu/CondenseNet (log)
PeleeNet	29.41	10.04	2,802,248	514.87M	Converted from GL model (log)
WRN-50-2	22.25	6.26	68,849,128	11,405.42M	Converted from GL model (log)
DRN-C-26	24.61	7.23	21,126,584	16,993.90M	Converted from GL model (log)
DRN-C-42	22.37	6.28	31,234,744	25,093.75M	Converted from GL model (log)
DRN-C-58	20.65	5.27	40,542,008	32,489.94M	Converted from GL model (log)
DRN-D-22	24.86	7.58	16,393,752	13,051.33M	Converted from GL model (log)
DRN-D-38	22.94	6.43	26,501,912	21,151.19M	Converted from GL model (log)
DRN-D-54	20.40	5.17	35,809,176	28,547.38M	Converted from GL model (log)
DRN-D-105	19.87	5.01	54,801,304	43,442.43M	Converted from GL model (log)
DPN-68	23.24	6.79	12,611,602	2,351.84M	Converted from GL model (log)
DPN-98	18.44	4.30	61,570,728	11,716.51M	Converted from GL model (log)
DPN-131	19.62	5.03	79,254,504	16,076.15M	Converted from GL model (log)
DarkNet Tiny	40.74	17.84	1,042,104	500.85M	Converted from GL model (log)
DarkNet Ref	38.58	17.18	7,319,416	367.59M	Converted from GL model (log)
DarkNet-53	21.33	5.58	41,609,928	7,133.86M	Converted from GL model (log)
i-RevNet-301	24.31	7.52	125,120,356	14,453.87M	Converted from GL model (log)
BagNet-9	49.15	25.76	15,688,744	16,049.19M	Converted from GL model (log)
BagNet-17	36.88	15.51	16,213,032	15,768.77M	Converted from GL model (log)
BagNet-33	29.82	10.70	18,310,184	16,371.52M	Converted from GL model (log)
DLA-34	24.59	7.24	15,742,104	3,071.37M	Converted from GL model (log)
DLA-46-C	34.28	13.23	1,301,400	585.45M	Converted from GL model (log)
DLA-X-46-C	33.26	12.69	1,068,440	546.72M	Converted from GL model (log)
DLA-60	21.47	5.70	22,036,632	4,255.49M	Converted from GL model (log)
DLA-X-60	21.07	5.75	17,352,344	3,543.68M	Converted from GL model (log)
DLA-X-60-C	30.98	10.91	1,319,832	596.06M	Converted from GL model (log)
DLA-102	20.69	5.37	33,268,888	7,190.95M	Converted from GL model (log)
DLA-X-102	19.70	4.88	26,309,272	5,884.94M	Converted from GL model (log)
DLA-X2-102	18.84	4.37	41,282,200	9,340.61M	Converted from GL model (log)
DLA-169	19.39	4.71	53,389,720	11,593.20M	Converted from GL model (log)
FishNet-150	19.26	4.75	24,959,400	6,435.05M	Converted from GL model (log)
ESPNetv2 x0.5	42.32	20.15	1,241,332	35.36M	From sacmehta/ESPNetv2 (log)
ESPNetv2 x1.0	33.92	13.45	1,670,072	98.09M	From sacmehta/ESPNetv2 (log)
ESPNetv2 x1.25	32.06	12.18	1,965,440	138.18M	From sacmehta/ESPNetv2 (log)
ESPNetv2 x1.5	30.19	11.08	2,314,856	185.77M	Converted from GL model (log)
ESPNetv2 x2.0	27.94	9.61	3,498,136	306.93M	From sacmehta/ESPNetv2 (log)
DiCENet x0.2	53.91	29.38	1,130,704	18.76M	From sacmehta/EdgeNets (log)
DiCENet x0.5	46.16	22.58	1,214,120	30.48M	From sacmehta/EdgeNets (log)
DiCENet x0.75	37.06	15.74	1,495,676	55.80M	From sacmehta/EdgeNets (log)
DiCENet x1.0	33.59	13.25	1,805,604	82.17M	From sacmehta/EdgeNets (log)
DiCENet x1.25	32.26	12.40	2,162,888	111.87M	From sacmehta/EdgeNets (log)
DiCENet x1.5	30.55	11.23	2,652,200	151.81M	From sacmehta/EdgeNets (log)
DiCENet x1.75	29.76	10.62	3,264,932	201.26M	From sacmehta/EdgeNets (log)
DiCENet x2.0	27.93	9.45	3,979,044	257.95M	Converted from GL model (log)
HRNet-W18 Small V1	26.48	9.01	13,187,464	1,615.00M	Converted from GL model (log)
HRNet-W18 Small V2	21.93	6.18	15,597,464	2,618.84M	Converted from GL model (log)
HRNetV2-W18	20.20	5.12	21,299,004	4,323.07M	Converted from GL model (log)
HRNetV2-W30	20.35	5.21	37,712,220	8,156.82M	Converted from GL model (log)
HRNetV2-W32	19.96	5.06	41,232,680	8,974.04M	Converted from GL model (log)
HRNetV2-W40	19.84	4.93	57,557,160	12,752.26M	Converted from GL model (log)
HRNetV2-W44	19.74	5.01	67,064,984	14,946.96M	Converted from GL model (log)
HRNetV2-W48	19.67	5.00	77,469,864	17,345.39M	Converted from GL model (log)
HRNetV2-W64	19.67	4.87	128,059,944	28,976.42M	Converted from GL model (log)
VoVNet-27-slim	29.78	9.97	3,525,736	2,187.25M	Converted from GL model (log)
VoVNet-39	21.71	5.64	22,600,296	7,086.16M	Converted from GL model (log)
VoVNet-57	20.33	5.18	36,640,296	8,943.09M	Converted from GL model (log)
SelecSLS-42b	21.93	6.11	32,458,248	2,980.62M	Converted from GL model (log)
SelecSLS-60	20.35	5.29	30,670,768	3,591.78M	Converted from GL model (log)
SelecSLS-60b	20.85	5.59	32,774,064	3,629.14M	Converted from GL model (log)
HarDNet-39DS	26.83	8.81	3,488,228	437.52M	Converted from GL model (log)
HarDNet-68DS	24.55	7.56	4,180,602	788.86M	Converted from GL model (log)
HarDNet-68	23.51	6.99	17,565,348	4,256.32M	From PingoLH/Pytorch-HarDNet (log)
HarDNet-85	22.00	5.86	36,670,212	9,088.58M	Converted from GL model (log)
SqueezeNet v1.0	39.29	17.66	1,248,424	823.67M	Converted from GL model (log)
SqueezeNet v1.1	39.31	17.72	1,235,496	352.02M	Converted from GL model (log)
SqueezeResNet v1.0	39.77	18.09	1,248,424	823.67M	Converted from GL model (log)
SqueezeResNet v1.1	40.09	18.21	1,235,496	352.02M	Converted from GL model (log)
1.0-SqNxt-23	42.51	19.06	724,056	287.28M	Converted from GL model (log)
1.0-SqNxt-23v5	40.77	17.85	921,816	285.82M	Converted from GL model (log)
1.5-SqNxt-23	34.89	13.50	1,511,824	552.39M	Converted from GL model (log)
1.5-SqNxt-23v5	33.81	13.01	1,953,616	550.97M	Converted from GL model (log)
2.0-SqNxt-23	30.62	11.00	2,583,752	898.48M	Converted from GL model (log)
2.0-SqNxt-23v5	29.63	10.66	3,366,344	897.60M	Converted from GL model (log)
ShuffleNet x0.25 (g=1)	62.44	37.29	209,746	12.35M	Converted from GL model (log)
ShuffleNet x0.25 (g=3)	61.74	36.53	305,902	13.09M	Converted from GL model (log)
ShuffleNet x0.5 (g=1)	46.59	22.61	534,484	41.16M	Converted from GL model (log)
ShuffleNet x0.5 (g=3)	44.16	20.80	718,324	41.70M	Converted from GL model (log)
ShuffleNet x0.75 (g=1)	39.58	17.11	975,214	86.42M	Converted from GL model (log)
ShuffleNet x0.75 (g=3)	38.20	16.50	1,238,266	85.82M	Converted from GL model (log)
ShuffleNet x1.0 (g=1)	34.93	13.89	1,531,936	148.13M	Converted from GL model (log)
ShuffleNet x1.0 (g=2)	34.25	13.63	1,733,848	147.60M	Converted from GL model (log)
ShuffleNet x1.0 (g=3)	34.39	13.48	1,865,728	145.46M	Converted from GL model (log)
ShuffleNet x1.0 (g=4)	34.19	13.35	1,968,344	143.33M	Converted from GL model (log)
ShuffleNet x1.0 (g=8)	34.06	13.42	2,434,768	150.76M	Converted from GL model (log)
ShuffleNetV2 x0.5	40.99	18.65	1,366,792	43.31M	Converted from GL model (log)
ShuffleNetV2 x1.0	31.44	11.63	2,278,604	149.72M	Converted from GL model (log)
ShuffleNetV2 x1.5	27.47	9.42	4,406,098	320.77M	Converted from GL model (log)
ShuffleNetV2 x2.0	25.94	8.45	7,601,686	595.84M	Converted from GL model (log)
ShuffleNetV2b x0.5	40.29	18.22	1,366,792	43.31M	Converted from GL model (log)
ShuffleNetV2b x1.0	30.62	11.25	2,279,760	150.62M	Converted from GL model (log)
ShuffleNetV2b x1.5	27.31	9.11	4,410,194	323.98M	Converted from GL model (log)
ShuffleNetV2b x2.0	25.58	8.34	7,611,290	603.37M	Converted from GL model (log)
108-MENet-8x1 (g=3)	43.94	20.76	654,516	42.68M	Converted from GL model (log)
128-MENet-8x1 (g=4)	42.43	19.59	750,796	45.98M	Converted from GL model (log)
160-MENet-8x1 (g=8)	43.84	20.84	850,120	45.63M	Converted from GL model (log)
228-MENet-12x1 (g=3)	34.11	13.16	1,806,568	152.93M	Converted from GL model (log)
256-MENet-12x1 (g=4)	32.65	12.52	1,888,240	150.65M	Converted from GL model (log)
348-MENet-12x1 (g=3)	28.24	9.58	3,368,128	312.00M	Converted from GL model (log)
352-MENet-12x1 (g=8)	31.56	12.00	2,272,872	157.35M	Converted from GL model (log)
456-MENet-24x1 (g=3)	25.32	7.99	5,304,784	567.90M	Converted from GL model (log)
MobileNet x0.25	46.26	22.49	470,072	44.09M	Converted from GL model (log)
MobileNet x0.5	34.15	13.55	1,331,592	155.42M	Converted from GL model (log)
MobileNet x0.75	30.14	10.76	2,585,560	333.99M	Converted from GL model (log)
MobileNet x1.0	26.61	8.95	4,231,976	579.80M	Converted from GL model (log)
MobileNetb x0.25	45.51	22.01	467,592	42.88M	Converted from GL model (log)
MobileNetb x0.5	33.42	13.10	1,326,632	153.00M	Converted from GL model (log)
MobileNetb x0.75	29.55	10.37	2,578,120	330.37M	Converted from GL model (log)
MobileNetb x1.0	25.45	8.16	4,222,056	574.97M	Converted from GL model (log)
FD-MobileNet x0.25	55.86	30.98	383,160	12.95M	Converted from GL model (log)
FD-MobileNet x0.5	43.13	20.15	993,928	41.84M	Converted from GL model (log)
FD-MobileNet x0.75	38.42	16.41	1,833,304	86.68M	Converted from GL model (log)
FD-MobileNet x1.0	34.23	13.38	2,901,288	147.46M	Converted from GL model (log)
MobileNetV2 x0.25	48.34	24.51	1,516,392	34.24M	Converted from GL model (log)
MobileNetV2 x0.5	35.98	14.93	1,964,736	100.13M	Converted from GL model (log)
MobileNetV2 x0.75	30.17	10.82	2,627,592	198.50M	Converted from GL model (log)
MobileNetV2 x1.0	26.97	8.87	3,504,960	329.36M	Converted from GL model (log)
MobileNetV2b x0.25	47.05	23.68	1,516,312	33.18M	Converted from GL model (log)
MobileNetV2b x0.5	34.77	14.08	1,964,448	96.42M	Converted from GL model (log)
MobileNetV2b x0.75	30.74	11.05	2,626,968	190.52M	Converted from GL model (log)
MobileNetV2b x1.0	27.70	9.12	3,503,872	315.51M	Converted from GL model (log)
MobileNetV3 L/224/1.0	24.55	7.44	5,481,752	227.09M	Converted from GL model (log)
IGCV3 x0.25	53.70	28.71	1,534,020	41.29M	Converted from GL model (log)
IGCV3 x0.5	39.75	17.32	1,985,528	111.12M	Converted from GL model (log)
IGCV3 x0.75	31.05	11.40	2,638,084	210.95M	Converted from GL model (log)
IGCV3 x1.0	27.91	9.20	3,491,688	340.79M	Converted from GL model (log)
MnasNet-B1	24.96	7.40	4,383,312	326.30M	Converted from GL model (log)
MnasNet-A1	24.39	7.20	3,887,038	326.07M	Converted from GL model (log)
DARTS	25.31	7.75	4,718,752	539.86M	Converted from GL model (log)
ProxylessNAS CPU	24.71	7.61	4,361,648	459.96M	From MIT-HAN-LAB/ProxylessNAS (log)
ProxylessNAS GPU	24.79	7.45	7,119,848	476.08M	Converted from GL model (log)
ProxylessNAS Mobile	25.41	7.80	4,080,512	332.46M	From MIT-HAN-LAB/ProxylessNAS (log)
ProxylessNAS Mob-14	23.29	6.62	6,857,568	597.10M	Converted from GL model (log)
FBNet-Cb	24.89	7.62	5,572,200	399.26M	From rwightman/pyt...models (log)
Xception	20.35	5.16	22,855,952	8,403.63M	Converted from GL model (log)
InceptionV3	20.44	5.33	23,834,568	5,743.06M	Converted from GL model (log)
InceptionV4	19.83	4.88	42,679,816	12,304.93M	Converted from GL model (log)
InceptionResNetV1	19.63	4.80	23,995,624	6,329.60M	Converted from GL model (log)
InceptionResNetV2	19.40	4.74	55,843,464	13,188.64M	Converted from GL model (log)
PolyNet	19.10	4.52	95,366,600	34,821.34M	From Cadene/pretrained...pytorch (log)
NASNet-A 4@1056	25.32	8.03	5,289,978	584.90M	Converted from GL model (log)
NASNet-A 6@4032	18.14	4.21	88,753,150	23,976.44M	From Cadene/pretrained...pytorch (log)
PNASNet-5-Large	17.88	4.28	86,057,668	25,140.77M	From Cadene/pretrained...pytorch (log)
SPNASNet	25.41	7.98	4,421,616	346.73M	Converted from GL model (log)
EfficientNet-B0	24.77	7.52	5,288,548	414.31M	Converted from GL model (log)
EfficientNet-B1	23.08	6.38	7,794,184	732.54M	Converted from GL model (log)
EfficientNet-B0b	23.88	7.02	5,288,548	414.31M	From rwightman/pyt...models (log)
EfficientNet-B1b	21.60	5.94	7,794,184	732.54M	From rwightman/pyt...models (log)
EfficientNet-B2b	20.31	5.27	9,109,994	1,051.98M	From rwightman/pyt...models (log)
EfficientNet-B3b	18.83	4.45	12,233,232	1,928.55M	From rwightman/pyt...models (log)
EfficientNet-B4b	17.45	3.89	19,341,616	4,607.46M	From rwightman/pyt...models (log)
EfficientNet-B5b	16.56	3.37	30,389,784	10,695.20M	From rwightman/pyt...models (log)
EfficientNet-B6b	16.29	3.23	43,040,704	19,796.24M	From rwightman/pyt...models (log)
EfficientNet-B7b	15.94	3.22	66,347,960	39,010.98M	From rwightman/pyt...models (log)
EfficientNet-B0c*	22.92	6.75	5,288,548	414.31M	From rwightman/pyt...models (log)
EfficientNet-B1c*	20.73	5.69	7,794,184	732.54M	From rwightman/pyt...models (log)
EfficientNet-B2c*	19.85	5.03	9,109,994	1,051.98M	From rwightman/pyt...models (log)
EfficientNet-B3c*	18.26	4.42	12,233,232	1,928.55M	From rwightman/pyt...models (log)
EfficientNet-B4c*	16.82	3.69	19,341,616	4,607.46M	From rwightman/pyt...models (log)
EfficientNet-B5c*	15.91	3.10	30,389,784	10,695.20M	From rwightman/pyt...models (log)
EfficientNet-B6c*	15.47	2.96	43,040,704	19,796.24M	From rwightman/pyt...models (log)
EfficientNet-B7c*	15.13	2.88	66,347,960	39,010.98M	From rwightman/pyt...models (log)
EfficientNet-B8c*	14.85	2.76	87,413,142	64,541.66M	From rwightman/pyt...models (log)
EfficientNet-Edge-Small-b*	22.74	6.40	5,438,392	2,378.12M	From rwightman/pyt...models (log)
EfficientNet-Edge-Medium-b*	21.18	5.63	6,899,496	3,700.12M	From rwightman/pyt...models (log)
EfficientNet-Edge-Large-b*	19.66	4.91	10,589,712	9,747.66M	From rwightman/pyt...models (log)
MixNet-S	23.92	7.17	4,134,606	260.76M	Converted from GL model (log)
MixNet-M	22.45	6.47	5,014,382	366.68M	Converted from GL model (log)
MixNet-L	21.54	5.71	7,329,252	591.34M	Converted from GL model (log)
ResNet(A)-10	31.29	11.90	5,438,024	1,135.86M	Converted from GL model (log)
ResNet(A)-BC-14	28.06	9.90	10,084,168	1,721.57M	Converted from GL model (log)
ResNet(A)-18	25.54	8.31	11,708,744	2,062.24M	Converted from GL model (log)
ResNet(A)-50b	21.03	5.56	25,576,264	4,352.93M	Converted from GL model (log)
ResNet(A)-101b	19.13	4.53	44,568,392	8,072.93M	Converted from GL model (log)
ResNet(A)-152b	18.80	4.41	60,212,040	11,796.83M	Converted from GL model (log)
ResNet(D)-50b	21.04	5.65	25,680,808	20,497.60M	From dmlc/gluon-cv (log)
ResNet(D)-101b	19.59	4.73	44,672,936	35,392.65M	From dmlc/gluon-cv (log)
ResNet(D)-152b	19.42	4.82	60,316,584	47,662.18M	From dmlc/gluon-cv (log)

CIFAR-10

Model	Error, %	Params	FLOPs/2	Remarks
NIN	7.43	966,986	222.97M	Converted from GL model (log)
ResNet-20	5.97	272,474	41.29M	Converted from GL model (log)
ResNet-56	4.52	855,770	127.06M	Converted from GL model (log)
ResNet-110	3.69	1,730,714	255.70M	Converted from GL model (log)
ResNet-164(BN)	3.68	1,704,154	255.31M	Converted from GL model (log)
ResNet-272(BN)	3.33	2,816,986	420.61M	Converted from GL model (log)
ResNet-542(BN)	3.43	5,599,066	833.87M	Converted from GL model (log)
ResNet-1001	3.28	10,328,602	1,536.40M	Converted from GL model (log)
ResNet-1202	3.53	19,424,026	2,857.17M	Converted from GL model (log)
PreResNet-20	6.51	272,282	41.27M	Converted from GL model (log)
PreResNet-56	4.49	855,578	127.03M	Converted from GL model (log)
PreResNet-110	3.86	1,730,522	255.68M	Converted from GL model (log)
PreResNet-164(BN)	3.64	1,703,258	255.08M	Converted from GL model (log)
PreResNet-272(BN)	3.25	2,816,090	420.38M	Converted from GL model (log)
PreResNet-542(BN)	3.14	5,598,170	833.64M	Converted from GL model (log)
PreResNet-1001	2.65	10,327,706	1,536.18M	Converted from GL model (log)
PreResNet-1202	3.39	19,423,834	2,857.14M	Converted from GL model (log)
ResNeXt-29 (32x4d)	3.15	4,775,754	780.55M	Converted from GL model (log)
ResNeXt-29 (16x64d)	2.41	68,155,210	10,709.34M	Converted from GL model (log)
ResNeXt-272 (1x64d)	2.55	44,540,746	6,565.15M	Converted from GL model (log)
ResNeXt-272 (2x32d)	2.74	32,928,586	4,867.11M	Converted from GL model (log)
SE-ResNet-20	6.01	274,847	41.34M	Converted from GL model (log)
SE-ResNet-56	4.13	862,889	127.19M	Converted from GL model (log)
SE-ResNet-110	3.63	1,744,952	255.98M	Converted from GL model (log)
SE-ResNet-164(BN)	3.39	1,906,258	256.55M	Converted from GL model (log)
SE-ResNet-272(BN)	3.39	3,153,826	422.68M	Converted from GL model (log)
SE-ResNet-542(BN)	3.47	6,272,746	838.01M	Converted from GL model (log)
SE-PreResNet-20	6.18	274,559	41.35M	Converted from GL model (log)
SE-PreResNet-56	4.51	862,601	127.20M	Converted from GL model (log)
SE-PreResNet-110	4.54	1,744,664	255.98M	Converted from GL model (log)
SE-PreResNet-164(BN)	3.73	1,904,882	256.32M	Converted from GL model (log)
SE-PreResNet-272(BN)	3.39	3,152,450	422.45M	Converted from GL model (log)
SE-PreResNet-542(BN)	3.08	6,271,370	837.78M	Converted from GL model (log)
PyramidNet-110 (a=48)	3.72	1,772,706	408.37M	Converted from GL model (log)
PyramidNet-110 (a=84)	2.98	3,904,446	778.15M	Converted from GL model (log)
PyramidNet-110 (a=270)	2.51	28,485,477	4,730.60M	Converted from GL model (log)
PyramidNet-164 (a=270, BN)	2.42	27,216,021	4,608.81M	Converted from GL model (log)
PyramidNet-200 (a=240, BN)	2.44	26,752,702	4,563.40M	Converted from GL model (log)
PyramidNet-236 (a=220, BN)	2.47	26,969,046	4,631.32M	Converted from GL model (log)
PyramidNet-272 (a=200, BN)	2.39	26,210,842	4,541.36M	Converted from GL model (log)
DenseNet-40 (k=12)	5.61	599,050	210.80M	Converted from GL model (log)
DenseNet-BC-40 (k=12)	6.43	176,122	74.89M	Converted from GL model (log)
DenseNet-BC-40 (k=24)	4.52	690,346	293.09M	Converted from GL model (log)
DenseNet-BC-40 (k=36)	4.04	1,542,682	654.60M	Converted from GL model (log)
DenseNet-100 (k=12)	3.66	4,068,490	1,353.55M	Converted from GL model (log)
DenseNet-100 (k=24)	3.13	16,114,138	5,354.19M	Converted from GL model (log)
DenseNet-BC-100 (k=12)	4.16	769,162	298.45M	Converted from GL model (log)
DenseNet-BC-190 (k=40)	2.52	25,624,430	9,400.45M	Converted from GL model (log)
DenseNet-BC-250 (k=24)	2.67	15,324,406	5,519.54M	Converted from GL model (log)
X-DenseNet-BC-40-2 (k=24)	5.31	690,346	293.09M	Converted from GL model (log)
X-DenseNet-BC-40-2 (k=36)	4.37	1,542,682	654.60M	Converted from GL model (log)
WRN-16-10	2.93	17,116,634	2,414.04M	Converted from GL model (log)
WRN-28-10	2.39	36,479,194	5,246.98M	Converted from GL model (log)
WRN-40-8	2.37	35,748,314	5,176.90M	Converted from GL model (log)
WRN-20-10-1bit	3.26	26,737,140	4,019.14M	Converted from GL model (log)
WRN-20-10-32bit	3.14	26,737,140	4,019.14M	Converted from GL model (log)
RoR-3-56	5.43	762,746	113.43M	Converted from GL model (log)
RoR-3-110	4.35	1,637,690	242.07M	Converted from GL model (log)
RoR-3-164	3.93	2,512,634	370.72M	Converted from GL model (log)
RiR	3.28	9,492,980	1,281.08M	Converted from GL model (log)
Shake-Shake-ResNet-20-2x16d	5.15	541,082	81.78M	Converted from GL model (log)
Shake-Shake-ResNet-26-2x32d	3.17	2,923,162	428.89M	Converted from GL model (log)
DIA-ResNet-20	6.22	286,866	41.54M	Converted from GL model (log)
DIA-ResNet-56	5.05	870,162	129.31M	Converted from GL model (log)
DIA-ResNet-110	4.10	1,745,106	264.71M	Converted from GL model (log)
DIA-ResNet-164(BN)	3.50	1,923,002	343.60M	Converted from GL model (log)
DIA-PreResNet-20	6.42	286,674	41.52M	Converted from GL model (log)
DIA-PreResNet-56	4.83	869,970	129.28M	Converted from GL model (log)
DIA-PreResNet-110	4.25	1,744,914	264.69M	Converted from GL model (log)
DIA-PreResNet-164(BN)	3.56	1,922,106	343.37M	Converted from GL model (log)

CIFAR-100

Model	Error, %	Params	FLOPs/2	Remarks
NIN	28.39	984,356	224.08M	Converted from GL model (log)
ResNet-20	29.64	278,324	41.30M	Converted from GL model (log)
ResNet-56	24.88	861,620	127.06M	Converted from GL model (log)
ResNet-110	22.80	1,736,564	255.71M	Converted from GL model (log)
ResNet-164(BN)	20.44	1,727,284	255.33M	Converted from GL model (log)
ResNet-272(BN)	20.07	2,840,116	420.63M	Converted from GL model (log)
ResNet-542(BN)	19.32	5,622,196	833.89M	Converted from GL model (log)
ResNet-1001	19.79	10,351,732	1,536.43M	Converted from GL model (log)
ResNet-1202	21.56	19,429,876	2,857.17M	Converted from GL model (log)
PreResNet-20	30.22	278,132	41.28M	Converted from GL model (log)
PreResNet-56	25.05	861,428	127.04M	Converted from GL model (log)
PreResNet-110	22.67	1,736,372	255.68M	Converted from GL model (log)
PreResNet-164(BN)	20.18	1,726,388	255.10M	Converted from GL model (log)
PreResNet-272(BN)	19.63	2,839,220	420.40M	Converted from GL model (log)
PreResNet-542(BN)	18.71	5,621,300	833.66M	Converted from GL model (log)
PreResNet-1001	18.41	10,350,836	1,536.20M	Converted from GL model (log)
ResNeXt-29 (32x4d)	19.50	4,868,004	780.64M	Converted from GL model (log)
ResNeXt-29 (16x64d)	16.93	68,247,460	10,709.43M	Converted from GL model (log)
ResNeXt-272 (1x64d)	19.11	44,632,996	6,565.25M	Converted from GL model (log)
ResNeXt-272 (2x32d)	18.34	33,020,836	4,867.20M	Converted from GL model (log)
SE-ResNet-20	28.54	280,697	41.35M	Converted from GL model (log)
SE-ResNet-56	22.94	868,739	127.07M	Converted from GL model (log)
SE-ResNet-110	20.86	1,750,802	255.98M	Converted from GL model (log)
SE-ResNet-164(BN)	19.95	1,929,388	256.57M	Converted from GL model (log)
SE-ResNet-272(BN)	19.07	3,176,956	422.70M	Converted from GL model (log)
SE-ResNet-542(BN)	18.87	6,295,876	838.03M	Converted from GL model (log)
SE-PreResNet-20	28.31	280,409	41.35M	Converted from GL model (log)
SE-PreResNet-56	23.05	868,451	127.21M	Converted from GL model (log)
SE-PreResNet-110	22.61	1,750,514	255.99M	Converted from GL model (log)
SE-PreResNet-164(BN)	20.05	1,928,012	256.34M	Converted from GL model (log)
SE-PreResNet-272(BN)	19.13	3,175,580	422.47M	Converted from GL model (log)
SE-PreResNet-542(BN)	19.45	6,294,500	837.80M	Converted from GL model (log)
PyramidNet-110 (a=48)	20.95	1,778,556	408.38M	Converted from GL model (log)
PyramidNet-110 (a=84)	18.87	3,913,536	778.16M	Converted from GL model (log)
PyramidNet-110 (a=270)	17.10	28,511,307	4,730.62M	Converted from GL model (log)
PyramidNet-164 (a=270, BN)	16.70	27,319,071	4,608.91M	Converted from GL model (log)
PyramidNet-200 (a=240, BN)	16.09	26,844,952	4,563.49M	Converted from GL model (log)
PyramidNet-236 (a=220, BN)	16.34	27,054,096	4,631.41M	Converted from GL model (log)
PyramidNet-272 (a=200, BN)	16.19	26,288,692	4,541.43M	Converted from GL model (log)
DenseNet-40 (k=12)	24.90	622,360	210.82M	Converted from GL model (log)
DenseNet-BC-40 (k=12)	28.41	188,092	74.90M	Converted from GL model (log)
DenseNet-BC-40 (k=24)	22.67	714,196	293.11M	Converted from GL model (log)
DenseNet-BC-40 (k=36)	20.50	1,578,412	654.64M	Converted from GL model (log)
DenseNet-100 (k=12)	19.64	4,129,600	1,353.62M	Converted from GL model (log)
DenseNet-100 (k=24)	18.08	16,236,268	5,354.32M	Converted from GL model (log)
DenseNet-BC-100 (k=12)	21.19	800,032	298.48M	Converted from GL model (log)
DenseNet-BC-250 (k=24)	17.39	15,480,556	5,519.69M	Converted from GL model (log)
X-DenseNet-BC-40-2 (k=24)	23.96	714,196	293.11M	Converted from GL model (log)
X-DenseNet-BC-40-2 (k=36)	21.65	1,578,412	654.64M	Converted from GL model (log)
WRN-16-10	18.95	17,174,324	2,414.09M	Converted from GL model (log)
WRN-28-10	17.88	36,536,884	5,247.04M	Converted from GL model (log)
WRN-40-8	18.03	35,794,484	5,176.95M	Converted from GL model (log)
WRN-20-10-1bit	19.04	26,794,920	4,022.81M	Converted from GL model (log)
WRN-20-10-32bit	18.12	26,794,920	4,022.81M	Converted from GL model (log)
RoR-3-56	25.49	768,596	113.43M	Converted from GL model (log)
RoR-3-110	23.64	1,643,540	242.08M	Converted from GL model (log)
RoR-3-164	22.34	2,518,484	370.72M	Converted from GL model (log)
RiR	19.23	9,527,720	1,283.29M	Converted from GL model (log)
Shake-Shake-ResNet-20-2x16d	29.22	546,932	81.79M	Converted from GL model (log)
Shake-Shake-ResNet-26-2x32d	18.80	2,934,772	428.90M	Converted from GL model (log)
DIA-ResNet-20	27.71	292,716	41.55M	Converted from GL model (log)
DIA-ResNet-56	24.35	876,012	129.32M	Converted from GL model (log)
DIA-ResNet-110	22.11	1,750,956	264.72M	Converted from GL model (log)
DIA-ResNet-164(BN)	19.53	1,946,132	343.62M	Converted from GL model (log)
DIA-PreResNet-20	28.37	292,524	41.53M	Converted from GL model (log)
DIA-PreResNet-56	25.05	875,820	129.29M	Converted from GL model (log)
DIA-PreResNet-110	22.69	1,750,764	264.69M	Converted from GL model (log)
DIA-PreResNet-164(BN)	19.99	1,945,236	343.39M	Converted from GL model (log)

SVHN

Model	Error, %	Params	FLOPs/2	Remarks
NIN	3.76	966,986	222.97M	Converted from GL model (log)
ResNet-20	3.43	272,474	41.29M	Converted from GL model (log)
ResNet-56	2.75	855,770	127.06M	Converted from GL model (log)
ResNet-110	2.45	1,730,714	255.70M	Converted from GL model (log)
ResNet-164(BN)	2.42	1,704,154	255.31M	Converted from GL model (log)
ResNet-272(BN)	2.43	2,816,986	420.61M	Converted from GL model (log)
ResNet-542(BN)	2.34	5,599,066	833.87M	Converted from GL model (log)
ResNet-1001	2.41	10,328,602	1,536.40M	Converted from GL model (log)
PreResNet-20	3.22	272,282	41.27M	Converted from GL model (log)
PreResNet-56	2.80	855,578	127.03M	Converted from GL model (log)
PreResNet-110	2.79	1,730,522	255.68M	Converted from GL model (log)
PreResNet-164(BN)	2.58	1,703,258	255.08M	Converted from GL model (log)
PreResNet-272(BN)	2.34	2,816,090	420.38M	Converted from GL model (log)
PreResNet-542(BN)	2.36	5,598,170	833.64M	Converted from GL model (log)
ResNeXt-29 (32x4d)	2.80	4,775,754	780.55M	Converted from GL model (log)
ResNeXt-29 (16x64d)	2.68	68,155,210	10,709.34M	Converted from GL model (log)
ResNeXt-272 (1x64d)	2.35	44,540,746	6,565.15M	Converted from GL model (log)
ResNeXt-272 (2x32d)	2.44	32,928,586	4,867.11M	Converted from GL model (log)
SE-ResNet-20	3.23	274,847	41.34M	Converted from GL model (log)
SE-ResNet-56	2.64	862,889	127.19M	Converted from GL model (log)
SE-ResNet-110	2.35	1,744,952	255.98M	Converted from GL model (log)
SE-ResNet-164(BN)	2.45	1,906,258	256.55M	Converted from GL model (log)
SE-ResNet-272(BN)	2.38	3,153,826	422.68M	Converted from GL model (log)
SE-ResNet-542(BN)	2.26	6,272,746	838.01M	Converted from GL model (log)
SE-PreResNet-20	3.24	274,559	41.35M	Converted from GL model (log)
SE-PreResNet-56	2.71	862,601	127.20M	Converted from GL model (log)
SE-PreResNet-110	2.59	1,744,664	255.98M	Converted from GL model (log)
SE-PreResNet-164(BN)	2.56	1,904,882	256.32M	Converted from GL model (log)
SE-PreResNet-272(BN)	2.49	3,152,450	422.45M	Converted from GL model (log)
SE-PreResNet-542(BN)	2.47	6,271,370	837.78M	Converted from GL model (log)
PyramidNet-110 (a=48)	2.47	1,772,706	408.37M	Converted from GL model (log)
PyramidNet-110 (a=84)	2.43	3,904,446	778.15M	Converted from GL model (log)
PyramidNet-110 (a=270)	2.38	28,485,477	4,730.60M	Converted from GL model (log)
PyramidNet-164 (a=270, BN)	2.33	27,216,021	4,608.81M	Converted from GL model (log)
PyramidNet-200 (a=240, BN)	2.32	26,752,702	4,563.40M	Converted from GL model (log)
PyramidNet-236 (a=220, BN)	2.35	26,969,046	4,631.32M	Converted from GL model (log)
PyramidNet-272 (a=200, BN)	2.40	26,210,842	4,541.36M	Converted from GL model (log)
DenseNet-40 (k=12)	3.05	599,050	210.80M	Converted from GL model (log)
DenseNet-BC-40 (k=12)	3.20	176,122	74.89M	Converted from GL model (log)
DenseNet-BC-40 (k=24)	2.90	690,346	293.09M	Converted from GL model (log)
DenseNet-BC-40 (k=36)	2.60	1,542,682	654.60M	Converted from GL model (log)
DenseNet-100 (k=12)	2.60	4,068,490	1,353.55M	Converted from GL model (log)
X-DenseNet-BC-40-2 (k=24)	2.87	690,346	293.09M	Converted from GL model (log)
X-DenseNet-BC-40-2 (k=36)	2.74	1,542,682	654.60M	Converted from GL model (log)
WRN-16-10	2.78	17,116,634	2,414.04M	Converted from GL model (log)
WRN-28-10	2.71	36,479,194	5,246.98M	Converted from GL model (log)
WRN-40-8	2.54	35,748,314	5,176.90M	Converted from GL model (log)
WRN-20-10-1bit	2.73	26,737,140	4,019.14M	Converted from GL model (log)
WRN-20-10-32bit	2.59	26,737,140	4,019.14M	Converted from GL model (log)
RoR-3-56	2.69	762,746	113.43M	Converted from GL model (log)
RoR-3-110	2.57	1,637,690	242.07M	Converted from GL model (log)
RoR-3-164	2.73	2,512,634	370.72M	Converted from GL model (log)
RiR	2.68	9,492,980	1,281.08M	Converted from GL model (log)
Shake-Shake-ResNet-20-2x16d	3.17	541,082	81.78M	Converted from GL model (log)
Shake-Shake-ResNet-26-2x32d	2.62	2,923,162	428.89M	Converted from GL model (log)
DIA-ResNet-20	3.23	286,866	41.54M	Converted from GL model (log)
DIA-ResNet-56	2.68	870,162	129.31M	Converted from GL model (log)
DIA-ResNet-110	2.47	1,745,106	264.71M	Converted from GL model (log)
DIA-ResNet-164(BN)	2.44	1,923,002	343.60M	Converted from GL model (log)
DIA-PreResNet-20	3.03	286,674	41.52M	Converted from GL model (log)
DIA-PreResNet-56	2.80	869,970	129.28M	Converted from GL model (log)
DIA-PreResNet-110	2.42	1,744,914	264.69M	Converted from GL model (log)
DIA-PreResNet-164(BN)	2.56	1,922,106	343.37M	Converted from GL model (log)

CUB-200-2011

Model	Error, %	Params	FLOPs/2	Remarks
ResNet-10	27.77	5,008,392	893.63M	Converted from GL model (log)
ResNet-12	27.27	5,082,376	1,125.84M	Converted from GL model (log)
ResNet-14	24.77	5,377,800	1,357.53M	Converted from GL model (log)
ResNet-16	23.65	6,558,472	1,588.93M	Converted from GL model (log)
ResNet-18	23.33	11,279,112	1,820.00M	Converted from GL model (log)
ResNet-26	23.16	17,549,832	2,746.38M	Converted from GL model (log)
SE-ResNet-10	27.72	5,052,932	893.86M	Converted from GL model (log)
SE-ResNet-12	26.51	5,127,496	1,126.17M	Converted from GL model (log)
SE-ResNet-14	24.16	5,425,104	1,357.92M	Converted from GL model (log)
SE-ResNet-16	23.32	6,614,240	1,589.35M	Converted from GL model (log)
SE-ResNet-18	23.52	11,368,192	1,820.47M	Converted from GL model (log)
SE-ResNet-26	22.99	17,683,452	2,747.08M	Converted from GL model (log)
MobileNet x1.0	23.77	3,411,976	578.98M	Converted from GL model (log)
ProxylessNAS Mobile	22.66	3,055,712	331.44M	Converted from GL model (log)
NTS-Net	12.77	28,623,333	33,361.79M	From yangze0930/NTS-Net (log)

Pascal VOC20102

Model	Extractor	Pix.Acc.,%	mIoU,%	Params	FLOPs/2	Remarks
PSPNet	ResNet(D)-101b	98.09	81.44	65,708,501	230,771.01M	From dmlc/gluon-cv (log)
DeepLabv3	ResNet(D)-101b	97.95	80.24	58,754,773	47,625.34M	From dmlc/gluon-cv (log)
DeepLabv3	ResNet(D)-152b	98.11	81.20	74,398,421	59,894.87M	From dmlc/gluon-cv (log)
FCN-8s(d)	ResNet(D)-101b	97.80	80.40	52,072,917	196,562.96M	From dmlc/gluon-cv (log)

ADE20K

Model	Extractor	Pix.Acc.,%	mIoU,%	Params	FLOPs/2	Remarks
PSPNet	ResNet(D)-50b	79.37	36.87	46,782,550	162,595.14M	From dmlc/gluon-cv (log)
PSPNet	ResNet(D)-101b	79.93	37.97	65,774,678	231,008.79M	From dmlc/gluon-cv (log)
DeepLabv3	ResNet(D)-50b	79.72	37.13	39,795,798	32,756.18M	From dmlc/gluon-cv (log)
DeepLabv3	ResNet(D)-101b	80.21	37.84	58,787,926	47,651.23M	From dmlc/gluon-cv (log)
FCN-8s(d)	ResNet(D)-50b	76.92	33.39	33,146,966	128,387.08M	From dmlc/gluon-cv (log)
FCN-8s(d)	ResNet(D)-101b	79.01	35.88	52,139,094	196,800.73M	From dmlc/gluon-cv (log)

Cityscapes

Model	Extractor	Pix.Acc.,%	mIoU,%	Params	FLOPs/2	Remarks
PSPNet	ResNet(D)-101b	96.17	71.72	65,707,475	230,767.33M	From dmlc/gluon-cv (log)
ICNet	ResNet(D)-50b	95.50	64.02	47,489,184	14,253.43M	From dmlc/gluon-cv (log)
SINet	-	94.08	61.72	119,418	1,419.90M	From clovaai/c3_sinet (log)
Fast-SCNN	-	95.14	65.76	1,138,051	3493.33M	From dmlc/gluon-cv (log)
DANet	ResNet(D)-50b	95.91	67.99	47,586,427	180,397.43M	From dmlc/gluon-cv (log)
DANet	ResNet(D)-101b	96.03	68.10	66,578,555	248,811.08M	From dmlc/gluon-cv (log)

COCO Semantic Segmentation

Model	Extractor	Pix.Acc.,%	mIoU,%	Params	FLOPs/2	Remarks
PSPNet	ResNet(D)-101b	92.05	67.41	65,708,501	230,771.01M	From dmlc/gluon-cv (log)
DeepLabv3	ResNet(D)-101b	92.19	67.73	58,754,773	47,625.34M	From dmlc/gluon-cv (log)
DeepLabv3	ResNet(D)-152b	92.24	68.99	74,398,421	275,087.91M	From dmlc/gluon-cv (log)
FCN-8s(d)	ResNet(D)-101b	91.44	60.11	52,072,917	196,562.96M	From dmlc/gluon-cv (log)

CelebAMask-HQ

Model	Extractor	Params	FLOPs/2	Remarks
BiSeNet	ResNet-18	13,300,416	-	From zllrunning/face...Torch (log)

COCO Keypoints Detection

Model	Extractor	OKS AP, %	Params	FLOPs/2	Remarks
AlphaPose	Fast-SE-ResNet-101b	74.15/91.59/80.68	59,569,873	9,553.89M	From dmlc/gluon-cv (log)
SimplePose	ResNet-18	66.31/89.20/73.41	15,376,721	1,799.25M	From dmlc/gluon-cv (log)
SimplePose	ResNet-50b	71.02/91.23/78.57	33,999,697	4,041.06M	From dmlc/gluon-cv (log)
SimplePose	ResNet-101b	72.44/92.18/79.76	52,991,825	7,685.04M	From dmlc/gluon-cv (log)
SimplePose	ResNet-152b	72.53/92.14/79.61	68,635,473	11,332.86M	From dmlc/gluon-cv (log)
SimplePose	ResNet(A)-50b	71.70/91.31/78.66	34,018,929	4,278.56M	From dmlc/gluon-cv (log)
SimplePose	ResNet(A)-101b	72.97/92.24/80.81	53,011,057	7,922.54M	From dmlc/gluon-cv (log)
SimplePose	ResNet(A)-152b	73.44/92.27/80.72	68,654,705	11,570.36M	From dmlc/gluon-cv (log)
SimplePose(Mobile)	ResNet-18	66.25/89.17/74.32	12,858,208	1,960.96M	From dmlc/gluon-cv (log)
SimplePose(Mobile)	ResNet-50b	71.10/91.28/78.67	25,582,944	4,221.30M	From dmlc/gluon-cv (log)
SimplePose(Mobile)	1.0 MobileNet-224	64.10/88.06/71.23	5,019,744	751.36M	From dmlc/gluon-cv (log)
SimplePose(Mobile)	1.0 MobileNetV2b-224	63.74/88.12/71.06	4,102,176	495.95M	From dmlc/gluon-cv (log)
SimplePose(Mobile)	MobileNetV3 Small 224/1.0	54.34/83.67/59.35	2,625,088	236.51M	From dmlc/gluon-cv (log)
SimplePose(Mobile)	MobileNetV3 Large 224/1.0	63.67/88.91/70.82	4,768,336	403.97M	From dmlc/gluon-cv (log)
Lightweight OpenPose 2D	MobileNet	39.99/65.95/40.70	4,091,698	8,948.96M	From Daniil-Osokin/lighw...ch (log)
Lightweight OpenPose 3D	MobileNet	39.99/65.95/40.70	5,085,983	11,049.43M	From Daniil-Osokin/li...3d...ch (log)
IBPPose	-	64.87/83.62/70.13	95,827,784	57,195.91M	From jialee93/Improved...Parts (log)

Mozilla Common Voice (Corpus 6.1, dev subset)

Some remarks:

• NR means Noise Reduction.
• LS means trained on LibriSpeech dataset.

Model	Lang	WER, %	Params	FLOPs/2	Remarks
Jasper DR 10x5	En	21.92	332,632,349	85,142.96M	From NVIDIA/NeMo (log)
Jasper DR 10x5 NR	En	17.92	332,632,349	85,142.96M	From NVIDIA/NeMo (log)
QuartzNet 5x5 LS	En	44.69	6,713,181	1,717.12M	From NVIDIA/NeMo (log)
QuartzNet 15x5	En	16.79	18,924,381	4,840.29M	From NVIDIA/NeMo (log)
QuartzNet 15x5 NR	En	17.76	18,924,381	4,840.29M	From NVIDIA/NeMo (log)
QuartzNet 15x5	De	11.67	18,927,456	4,841.08M	From NVIDIA/NeMo (log)
QuartzNet 15x5	Fr	13.88	18,938,731	4,843.96M	From NVIDIA/NeMo (log)
QuartzNet 15x5	It	15.02	18,934,631	4,842.91M	From NVIDIA/NeMo (log)
QuartzNet 15x5	Es	12.95	18,931,556	4,842.13M	From NVIDIA/NeMo (log)
QuartzNet 15x5	Ca	8.42	18,934,631	4,842.91M	From NVIDIA/NeMo (log)
QuartzNet 15x5	Pl	13.59	18,929,506	4,841.60M	From NVIDIA/NeMo (log)
QuartzNet 15x5	Ru	16.48	18,930,531	4,841.87M	From NVIDIA/NeMo (log)
QuartzNet 15x5	Ru/34	9.69	18,929,506	4,841.60M	From sberdevices/golos (log)