repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/fractalnet_cifar.py | """
FractalNet for CIFAR, implemented in PyTorch.
Original paper: 'FractalNet: Ultra-Deep Neural Networks without Residuals,' https://arxiv.org/abs/1605.07648.
"""
__all__ = ['CIFARFractalNet', 'fractalnet_cifar10', 'fractalnet_cifar100']
import os
import numpy as np
import torch
import torch.nn as nn
import ... | 15,954 | 31.038153 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/mobilenetv3.py | """
MobileNetV3 for ImageNet-1K, implemented in PyTorch.
Original paper: 'Searching for MobileNetV3,' https://arxiv.org/abs/1905.02244.
"""
__all__ = ['MobileNetV3', 'mobilenetv3_small_w7d20', 'mobilenetv3_small_wd2', 'mobilenetv3_small_w3d4',
'mobilenetv3_small_w1', 'mobilenetv3_small_w5d4', 'mobil... | 18,999 | 33.234234 | 118 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/diaresnet.py | """
DIA-ResNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'DIANet: Dense-and-Implicit Attention Network,' https://arxiv.org/abs/1905.10671.
"""
__all__ = ['DIAResNet', 'diaresnet10', 'diaresnet12', 'diaresnet14', 'diaresnetbc14b', 'diaresnet16', 'diaresnet18',
'diaresnet26', 'diaresnet... | 24,132 | 32.058904 | 116 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/lffd.py | """
LFFD for face detection, implemented in PyTorch.
Original paper: 'LFFD: A Light and Fast Face Detector for Edge Devices,' https://arxiv.org/abs/1904.10633.
"""
__all__ = ['LFFD', 'lffd20x5s320v2_widerface', 'lffd25x8s560v1_widerface']
import os
import torch.nn as nn
from .common import conv3x3, conv1x1_bl... | 10,582 | 30.685629 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/sepreresnet.py | """
SE-PreResNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
"""
__all__ = ['SEPreResNet', 'sepreresnet10', 'sepreresnet12', 'sepreresnet14', 'sepreresnet16', 'sepreresnet18',
'sepreresnet26', 'sepreresnetbc26b', 'sepr... | 18,420 | 32.371377 | 119 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/resnext.py | """
ResNeXt for ImageNet-1K, implemented in PyTorch.
Original paper: 'Aggregated Residual Transformations for Deep Neural Networks,' http://arxiv.org/abs/1611.05431.
"""
__all__ = ['ResNeXt', 'resnext14_16x4d', 'resnext14_32x2d', 'resnext14_32x4d', 'resnext26_16x4d', 'resnext26_32x2d',
'resnext26_32... | 14,857 | 31.090713 | 119 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/jasper.py | """
Jasper/DR for ASR, implemented in PyTorch.
Original paper: 'Jasper: An End-to-End Convolutional Neural Acoustic Model,' https://arxiv.org/abs/1904.03288.
"""
__all__ = ['Jasper', 'jasper5x3', 'jasper10x4', 'jasper10x5', 'get_jasper', 'MaskConv1d', 'NemoAudioReader',
'NemoMelSpecExtractor', 'CtcD... | 35,202 | 29.347414 | 117 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/resneta.py | """
ResNet(A) with average downsampling for ImageNet-1K, implemented in PyTorch.
Original paper: 'Deep Residual Learning for Image Recognition,' https://arxiv.org/abs/1512.03385.
"""
__all__ = ['ResNetA', 'resneta10', 'resnetabc14b', 'resneta18', 'resneta50b', 'resneta101b', 'resneta152b']
import os
import to... | 14,395 | 32.094253 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/resnesta.py | """
ResNeSt(A) with average downsampling for ImageNet-1K, implemented in PyTorch.
Original paper: 'ResNeSt: Split-Attention Networks,' https://arxiv.org/abs/2004.08955.
"""
__all__ = ['ResNeStA', 'resnestabc14', 'resnesta18', 'resnestabc26', 'resnesta50', 'resnesta101', 'resnesta152',
'resnesta200',... | 17,572 | 30.892922 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/senet.py | """
SENet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
"""
__all__ = ['SENet', 'senet16', 'senet28', 'senet40', 'senet52', 'senet103', 'senet154', 'SEInitBlock']
import os
import math
import torch.nn as nn
import torch.nn.init as... | 13,095 | 28.696145 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/diapreresnet_cifar.py | """
DIA-PreResNet for CIFAR/SVHN, implemented in PyTorch.
Original papers: 'DIANet: Dense-and-Implicit Attention Network,' https://arxiv.org/abs/1905.10671.
"""
__all__ = ['CIFARDIAPreResNet', 'diapreresnet20_cifar10', 'diapreresnet20_cifar100', 'diapreresnet20_svhn',
'diapreresnet56_cifar10', 'diap... | 20,604 | 36.327899 | 120 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/simplepose_coco.py | """
SimplePose for COCO Keypoint, implemented in PyTorch.
Original paper: 'Simple Baselines for Human Pose Estimation and Tracking,' https://arxiv.org/abs/1804.06208.
"""
__all__ = ['SimplePose', 'simplepose_resnet18_coco', 'simplepose_resnet50b_coco', 'simplepose_resnet101b_coco',
'simplepose_resne... | 12,777 | 36.145349 | 118 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/vovnet.py | """
VoVNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection,'
https://arxiv.org/abs/1904.09730.
"""
__all__ = ['VoVNet', 'vovnet27s', 'vovnet39', 'vovnet57']
import os
import torch.nn as nn
from .common import... | 10,220 | 29.601796 | 120 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/espnetv2.py | """
ESPNetv2 for ImageNet-1K, implemented in PyTorch.
Original paper: 'ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network,'
https://arxiv.org/abs/1811.11431.
"""
__all__ = ['ESPNetv2', 'espnetv2_wd2', 'espnetv2_w1', 'espnetv2_w5d4', 'espnetv2_w3d2', 'espnetv2_w2']
... | 17,203 | 30.336976 | 118 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/shufflenet.py | """
ShuffleNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices,'
https://arxiv.org/abs/1707.01083.
"""
__all__ = ['ShuffleNet', 'shufflenet_g1_w1', 'shufflenet_g2_w1', 'shufflenet_g3_w1', 'shufflenet_g4_w1',
... | 15,779 | 31.875 | 120 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/bamresnet.py | """
BAM-ResNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'BAM: Bottleneck Attention Module,' https://arxiv.org/abs/1807.06514.
"""
__all__ = ['BamResNet', 'bam_resnet18', 'bam_resnet34', 'bam_resnet50', 'bam_resnet101', 'bam_resnet152']
import os
import torch.nn as nn
import torch.nn.init as in... | 13,297 | 28.420354 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/resattnet.py | """
ResAttNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Residual Attention Network for Image Classification,' https://arxiv.org/abs/1704.06904.
"""
__all__ = ['ResAttNet', 'resattnet56', 'resattnet92', 'resattnet128', 'resattnet164', 'resattnet200', 'resattnet236',
'resattnet452']
i... | 20,035 | 28.464706 | 117 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/centernet.py | """
CenterNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Objects as Points,' https://arxiv.org/abs/1904.07850.
"""
__all__ = ['CenterNet', 'centernet_resnet18_voc', 'centernet_resnet18_coco', 'centernet_resnet50b_voc',
'centernet_resnet50b_coco', 'centernet_resnet101b_voc', 'centernet... | 16,535 | 32.204819 | 118 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/xdensenet_cifar.py | """
X-DenseNet for CIFAR/SVHN, implemented in PyTorch.
Original paper: 'Deep Expander Networks: Efficient Deep Networks from Graph Theory,'
https://arxiv.org/abs/1711.08757.
"""
__all__ = ['CIFARXDenseNet', 'xdensenet40_2_k24_bc_cifar10', 'xdensenet40_2_k24_bc_cifar100',
'xdensenet40_2_k24_bc_sv... | 12,852 | 33.831978 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/revnet.py | """
RevNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'The Reversible Residual Network: Backpropagation Without Storing Activations,'
https://arxiv.org/abs/1707.04585.
"""
__all__ = ['RevNet', 'revnet38', 'revnet110', 'revnet164']
import os
from contextlib import contextmanager
import torch
... | 15,590 | 28.142056 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/ntsnet_cub.py | """
NTS-Net for CUB-200-2011, implemented in PyTorch.
Original paper: 'Learning to Navigate for Fine-grained Classification,' https://arxiv.org/abs/1809.00287.
"""
__all__ = ['NTSNet', 'ntsnet_cub']
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn... | 14,019 | 32.54067 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/proxylessnas_cub.py | """
ProxylessNAS for CUB-200-2011, implemented in Gluon.
Original paper: 'ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware,'
https://arxiv.org/abs/1812.00332.
"""
__all__ = ['proxylessnas_cpu_cub', 'proxylessnas_gpu_cub', 'proxylessnas_mobile_cub', 'proxylessnas_mobile14_cub']
f... | 4,155 | 32.788618 | 120 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/ibnresnet.py | """
IBN-ResNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net,'
https://arxiv.org/abs/1807.09441.
"""
__all__ = ['IBNResNet', 'ibn_resnet50', 'ibn_resnet101', 'ibn_resnet152']
import os
import torch.nn as nn
import torch.... | 12,570 | 29.002387 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/common.py | """
Common routines for models in PyTorch.
"""
__all__ = ['round_channels', 'Identity', 'BreakBlock', 'Swish', 'HSigmoid', 'HSwish', 'get_activation_layer',
'SelectableDense', 'DenseBlock', 'ConvBlock1d', 'conv1x1', 'conv3x3', 'depthwise_conv3x3', 'ConvBlock',
'conv1x1_block', 'conv3x3_block'... | 74,363 | 30.902188 | 130 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/lwopenpose_cmupan.py | """
Lightweight OpenPose 2D/3D for CMU Panoptic, implemented in PyTorch.
Original paper: 'Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose,'
https://arxiv.org/abs/1811.12004.
"""
__all__ = ['LwOpenPose', 'lwopenpose2d_mobilenet_cmupan_coco', 'lwopenpose3d_mobilenet_cmupan_coco',
... | 21,152 | 31.643519 | 119 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/rir_cifar.py | """
RiR for CIFAR/SVHN, implemented in PyTorch.
Original paper: 'Resnet in Resnet: Generalizing Residual Architectures,' https://arxiv.org/abs/1603.08029.
"""
__all__ = ['CIFARRiR', 'rir_cifar10', 'rir_cifar100', 'rir_svhn', 'RiRFinalBlock']
import os
import torch
import torch.nn as nn
import torch.nn.init as... | 10,658 | 29.454286 | 119 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/unet.py | """
U-Net for image segmentation, implemented in PyTorch.
Original paper: 'U-Net: Convolutional Networks for Biomedical Image Segmentation,'
https://arxiv.org/abs/1505.04597.
"""
__all__ = ['UNet', 'unet_cityscapes']
import os
import torch
import torch.nn as nn
from .common import conv1x1, conv3x3_block, ... | 9,378 | 27.335347 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/diapreresnet.py | """
DIA-PreResNet for ImageNet-1K, implemented in PyTorch.
Original papers: 'DIANet: Dense-and-Implicit Attention Network,' https://arxiv.org/abs/1905.10671.
"""
__all__ = ['DIAPreResNet', 'diapreresnet10', 'diapreresnet12', 'diapreresnet14', 'diapreresnetbc14b', 'diapreresnet16',
'diapreresnet18', ... | 21,166 | 33.814145 | 119 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/jasperdr.py | """
Jasper DR (Dense Residual) for ASR, implemented in PyTorch.
Original paper: 'Jasper: An End-to-End Convolutional Neural Acoustic Model,' https://arxiv.org/abs/1904.03288.
"""
__all__ = ['jasperdr10x5_en', 'jasperdr10x5_en_nr']
from .jasper import get_jasper
def jasperdr10x5_en(num_classes=29, **kwargs):... | 2,982 | 30.4 | 120 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/segnet.py | """
SegNet for image segmentation, implemented in PyTorch.
Original paper: 'SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,'
https://arxiv.org/abs/1511.00561.
"""
__all__ = ['SegNet', 'segnet_cityscapes']
import os
import torch
import torch.nn as nn
from .common import co... | 7,072 | 31.74537 | 120 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/deeplabv3.py | """
DeepLabv3 for image segmentation, implemented in PyTorch.
Original paper: 'Rethinking Atrous Convolution for Semantic Image Segmentation,' https://arxiv.org/abs/1706.05587.
"""
__all__ = ['DeepLabv3', 'deeplabv3_resnetd50b_voc', 'deeplabv3_resnetd101b_voc', 'deeplabv3_resnetd152b_voc',
'deeplabv... | 21,944 | 37.840708 | 120 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/fpenet.py | """
FPENet for image segmentation, implemented in PyTorch.
Original paper: 'Feature Pyramid Encoding Network for Real-time Semantic Segmentation,'
https://arxiv.org/abs/1909.08599.
"""
__all__ = ['FPENet', 'fpenet_cityscapes']
import os
import torch
import torch.nn as nn
from .common import conv1x1, conv1... | 12,630 | 28.511682 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/irevnet.py | """
i-RevNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'i-RevNet: Deep Invertible Networks,' https://arxiv.org/abs/1802.07088.
"""
__all__ = ['IRevNet', 'irevnet301', 'IRevDownscale', 'IRevSplitBlock', 'IRevMergeBlock']
import os
import torch
import torch.nn as nn
import torch.nn.init as init
f... | 15,151 | 29.796748 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/model_store.py | """
Model store which provides pretrained models.
"""
__all__ = ['get_model_file', 'load_model', 'download_model', 'calc_num_params']
import os
import zipfile
import logging
import hashlib
_model_sha1 = {name: (error, checksum, repo_release_tag, caption, paper, ds, img_size, scale, batch, rem) for
... | 94,745 | 110.465882 | 205 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/tresnet.py | """
TResNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'TResNet: High Performance GPU-Dedicated Architecture,' https://arxiv.org/abs/2003.13630.
NB: Not tested!
"""
__all__ = ['TResNet', 'tresnet_m', 'tresnet_l', 'tresnet_xl']
import os
import torch
import torch.nn as nn
import torch.nn.fun... | 15,627 | 28.542533 | 117 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/fastseresnet.py | """
Fast-SE-ResNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
"""
__all__ = ['FastSEResNet', 'fastseresnet101b']
import os
import torch.nn as nn
import torch.nn.init as init
from .common import conv1x1_block, SEBlock
from .resn... | 9,345 | 30.049834 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/ibnbresnet.py | """
IBN(b)-ResNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net,'
https://arxiv.org/abs/1807.09441.
"""
__all__ = ['IBNbResNet', 'ibnb_resnet50', 'ibnb_resnet101', 'ibnb_resnet152']
import os
import torch.nn as nn
import... | 11,999 | 29 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/polynet.py | """
PolyNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'PolyNet: A Pursuit of Structural Diversity in Very Deep Networks,'
https://arxiv.org/abs/1611.05725.
"""
__all__ = ['PolyNet', 'polynet']
import os
import torch.nn as nn
import torch.nn.init as init
from .common import ConvBlock, conv1x... | 28,281 | 28.928042 | 119 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/resnet_cifar.py | """
ResNet for CIFAR/SVHN, implemented in PyTorch.
Original paper: 'Deep Residual Learning for Image Recognition,' https://arxiv.org/abs/1512.03385.
"""
__all__ = ['CIFARResNet', 'resnet20_cifar10', 'resnet20_cifar100', 'resnet20_svhn',
'resnet56_cifar10', 'resnet56_cifar100', 'resnet56_svhn',
... | 23,882 | 35.131619 | 120 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/nasnet.py | """
NASNet-A for ImageNet-1K, implemented in PyTorch.
Original paper: 'Learning Transferable Architectures for Scalable Image Recognition,'
https://arxiv.org/abs/1707.07012.
"""
__all__ = ['NASNet', 'nasnet_4a1056', 'nasnet_6a4032', 'nasnet_dual_path_sequential']
import os
import torch
import torch.nn as ... | 38,588 | 28.502294 | 119 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/resnext_cifar.py | """
ResNeXt for CIFAR/SVHN, implemented in PyTorch.
Original paper: 'Aggregated Residual Transformations for Deep Neural Networks,' http://arxiv.org/abs/1611.05431.
"""
__all__ = ['CIFARResNeXt', 'resnext20_16x4d_cifar10', 'resnext20_16x4d_cifar100', 'resnext20_16x4d_svhn',
'resnext20_32x2d_cifar10'... | 23,083 | 37.092409 | 116 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/densenet_cifar.py | """
DenseNet for CIFAR/SVHN, implemented in PyTorch.
Original paper: 'Densely Connected Convolutional Networks,' https://arxiv.org/abs/1608.06993.
"""
__all__ = ['CIFARDenseNet', 'densenet40_k12_cifar10', 'densenet40_k12_cifar100', 'densenet40_k12_svhn',
'densenet40_k12_bc_cifar10', 'densenet40_k12_... | 29,468 | 36.780769 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/bninception.py | """
BN-Inception for ImageNet-1K, implemented in PyTorch.
Original paper: 'Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift,'
https://arxiv.org/abs/1502.03167.
"""
__all__ = ['BNInception', 'bninception']
import os
import torch.nn as nn
import torch.nn.init as i... | 16,280 | 29.488764 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/msdnet.py | """
MSDNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Multi-Scale Dense Networks for Resource Efficient Image Classification,'
https://arxiv.org/abs/1703.09844.
"""
__all__ = ['MSDNet', 'msdnet22', 'MultiOutputSequential', 'MSDFeatureBlock']
import os
import math
import torch
import torch.n... | 19,529 | 30.65316 | 120 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/zfnet.py | """
ZFNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Visualizing and Understanding Convolutional Networks,' https://arxiv.org/abs/1311.2901.
"""
__all__ = ['zfnet', 'zfnetb']
import os
from .alexnet import AlexNet
def get_zfnet(version="a",
model_name=None,
pretrai... | 3,659 | 26.727273 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/peleenet.py | """
PeleeNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Pelee: A Real-Time Object Detection System on Mobile Devices,' https://arxiv.org/abs/1804.06882.
"""
__all__ = ['PeleeNet', 'peleenet']
import os
import torch
import torch.nn as nn
import torch.nn.init as init
from .common import conv1x1_b... | 10,823 | 27.710875 | 117 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/msdnet_cifar10.py | """
MSDNet for CIFAR-10, implemented in PyTorch.
Original paper: 'Multi-Scale Dense Networks for Resource Efficient Image Classification,'
https://arxiv.org/abs/1703.09844.
"""
__all__ = ['CIFAR10MSDNet', 'msdnet22_cifar10']
import os
import math
import torch.nn as nn
import torch.nn.init as init
from .co... | 10,172 | 30.691589 | 120 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/erfnet.py | """
ERFNet for image segmentation, implemented in PyTorch.
Original paper: 'ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation,'
http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17tits.pdf.
"""
__all__ = ['ERFNet', 'erfnet_cityscapes', 'FCU']
import os
import to... | 9,330 | 31.175862 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/sharesnet.py | """
ShaResNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'ShaResNet: reducing residual network parameter number by sharing weights,'
https://arxiv.org/abs/1702.08782.
"""
__all__ = ['ShaResNet', 'sharesnet18', 'sharesnet34', 'sharesnet50', 'sharesnet50b', 'sharesnet101', 'sharesnet101b',
... | 19,841 | 31.263415 | 117 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/ibppose_coco.py | """
IBPPose for COCO Keypoint, implemented in PyTorch.
Original paper: 'Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation,'
https://arxiv.org/abs/1911.10529.
"""
__all__ = ['IbpPose', 'ibppose_coco']
import os
import torch
from torch import nn
from .common import ... | 17,476 | 28.521959 | 117 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/xception.py | """
Xception for ImageNet-1K, implemented in PyTorch.
Original paper: 'Xception: Deep Learning with Depthwise Separable Convolutions,' https://arxiv.org/abs/1610.02357.
"""
__all__ = ['Xception', 'xception']
import os
import torch.nn as nn
import torch.nn.init as init
from .common import conv1x1_block, conv3x... | 11,572 | 27.717122 | 118 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/darknet53.py | """
DarkNet-53 for ImageNet-1K, implemented in PyTorch.
Original source: 'YOLOv3: An Incremental Improvement,' https://arxiv.org/abs/1804.02767.
"""
__all__ = ['DarkNet53', 'darknet53']
import os
import torch.nn as nn
import torch.nn.init as init
from .common import conv1x1_block, conv3x3_block
class DarkUn... | 6,707 | 29.080717 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/mobilenet.py | """
MobileNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,'
https://arxiv.org/abs/1704.04861.
"""
__all__ = ['MobileNet', 'mobilenet_w1', 'mobilenet_w3d4', 'mobilenet_wd2', 'mobilenet_wd4', 'get_mobilenet']
im... | 8,480 | 32.521739 | 119 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/dpn.py | """
DPN for ImageNet-1K, implemented in PyTorch.
Original paper: 'Dual Path Networks,' https://arxiv.org/abs/1707.01629.
"""
__all__ = ['DPN', 'dpn68', 'dpn68b', 'dpn98', 'dpn107', 'dpn131']
import os
import torch
import torch.nn as nn
import torch.nn.init as init
from .common import conv1x1, DualPathSequenti... | 18,976 | 27.709531 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/sknet.py | """
SKNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Selective Kernel Networks,' https://arxiv.org/abs/1903.06586.
"""
__all__ = ['SKNet', 'sknet50', 'sknet101', 'sknet152']
import os
import torch.nn as nn
import torch.nn.init as init
from .common import conv1x1, conv1x1_block, conv3x3_block, C... | 10,908 | 28.563686 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/spnasnet.py | """
Single-Path NASNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours,'
https://arxiv.org/abs/1904.02877.
"""
__all__ = ['SPNASNet', 'spnasnet']
import os
import torch.nn as nn
import torch.nn.init as init
from .common ... | 10,388 | 30.10479 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/fastscnn.py | """
Fast-SCNN for image segmentation, implemented in PyTorch.
Original paper: 'Fast-SCNN: Fast Semantic Segmentation Network,' https://arxiv.org/abs/1902.04502.
"""
__all__ = ['FastSCNN', 'fastscnn_cityscapes']
import os
import torch.nn as nn
from .common import conv1x1, conv1x1_block, conv3x3_block, dwconv3x... | 15,264 | 28.814453 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/esnet.py | """
ESNet for image segmentation, implemented in PyTorch.
Original paper: 'ESNet: An Efficient Symmetric Network for Real-time Semantic Segmentation,'
https://arxiv.org/abs/1906.09826.
"""
__all__ = ['ESNet', 'esnet_cityscapes']
import os
import torch
import torch.nn as nn
from .common import AsymConvBloc... | 10,912 | 31.002933 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/enet.py | """
ENet for image segmentation, implemented in PyTorch.
Original paper: 'ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation,'
https://arxiv.org/abs/1606.02147.
"""
__all__ = ['ENet', 'enet_cityscapes', 'ENetMixDownBlock']
import os
import torch
import torch.nn as nn
import torch... | 18,480 | 31.14087 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/darknet.py | """
DarkNet for ImageNet-1K, implemented in PyTorch.
Original source: 'Darknet: Open source neural networks in c,' https://github.com/pjreddie/darknet.
"""
__all__ = ['DarkNet', 'darknet_ref', 'darknet_tiny', 'darknet19']
import os
import torch
import torch.nn as nn
import torch.nn.init as init
from .common i... | 8,529 | 30.360294 | 116 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/ror_cifar.py | """
RoR-3 for CIFAR/SVHN, implemented in PyTorch.
Original paper: 'Residual Networks of Residual Networks: Multilevel Residual Networks,'
https://arxiv.org/abs/1608.02908.
"""
__all__ = ['CIFARRoR', 'ror3_56_cifar10', 'ror3_56_cifar100', 'ror3_56_svhn', 'ror3_110_cifar10', 'ror3_110_cifar100',
'... | 16,718 | 31.401163 | 118 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/contextnet.py | """
ContextNet for image segmentation, implemented in PyTorch.
Original paper: 'ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time,'
https://arxiv.org/abs/1805.04554.
"""
__all__ = ['ContextNet', 'ctxnet_cityscapes']
import os
import torch
import torch.nn as nn
from .common im... | 12,923 | 28.239819 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/dicenet.py | """
DiCENet for ImageNet-1K, implemented in PyTorch.
Original paper: 'DiCENet: Dimension-wise Convolutions for Efficient Networks,' https://arxiv.org/abs/1906.03516.
"""
__all__ = ['DiceNet', 'dicenet_wd5', 'dicenet_wd2', 'dicenet_w3d4', 'dicenet_w1', 'dicenet_w5d4', 'dicenet_w3d2',
'dicenet_w7d8', ... | 23,378 | 29.441406 | 119 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/nvpattexp.py | """
Neural Voice Puppetry Audio-to-Expression net for speech-driven facial animation, implemented in PyTorch.
Original paper: 'Neural Voice Puppetry: Audio-driven Facial Reenactment,' https://arxiv.org/abs/1912.05566.
"""
__all__ = ['NvpAttExp', 'nvpattexp116bazel76']
import os
import torch
import torch.nn as... | 8,810 | 31.754647 | 116 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/octresnet.py | """
Oct-ResNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave
Convolution,' https://arxiv.org/abs/1904.05049.
"""
__all__ = ['OctResNet', 'octresnet10_ad2', 'octresnet50b_ad2', 'OctResUnit']
import os
from ... | 27,931 | 32.612515 | 119 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/prnet.py | """
PRNet for AFLW2000-3D, implemented in PyTorch.
Original paper: 'Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network,'
https://arxiv.org/abs/1803.07835.
"""
__all__ = ['PRNet', 'prnet']
import os
import torch.nn as nn
from .common import ConvBlock, DeconvBlock, conv1x1... | 13,924 | 30.362613 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/pfpcnet.py | """
PFPCNet for 3D face reconstruction, implemented in PyTorch.
Original paper: 'Production-Level Facial Performance Capture Using Deep Convolutional Neural Networks,'
https://arxiv.org/abs/1609.06536.
"""
__all__ = ['PFPCNet', 'pfpcnet']
import os
import torch.nn as nn
import torch.nn.init as init
from .... | 5,314 | 28.859551 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/espcnet.py | """
ESPNet-C for image segmentation, implemented in PyTorch.
Original paper: 'ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation,'
https://arxiv.org/abs/1803.06815.
"""
__all__ = ['ESPCNet', 'espcnet_cityscapes', 'ESPBlock']
import os
import torch
import torch.nn as nn
fro... | 12,104 | 29.2625 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/alexnet.py | """
AlexNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'One weird trick for parallelizing convolutional neural networks,'
https://arxiv.org/abs/1404.5997.
"""
__all__ = ['AlexNet', 'alexnet', 'alexnetb']
import os
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as ... | 9,244 | 27.890625 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/mobilenet_cub.py | """
MobileNet & FD-MobileNet for CUB-200-2011, implemented in torch.
Original papers:
- 'MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,'
https://arxiv.org/abs/1704.04861.
- 'FD-MobileNet: Improved MobileNet with A Fast Downsampling Strategy,' https://arxiv.org... | 7,269 | 34.990099 | 120 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/wrn.py | """
WRN for ImageNet-1K, implemented in PyTorch.
Original paper: 'Wide Residual Networks,' https://arxiv.org/abs/1605.07146.
"""
__all__ = ['WRN', 'wrn50_2']
import os
import torch.nn as nn
import torch.nn.init as init
class WRNConv(nn.Module):
"""
WRN specific convolution block.
Parameters:
... | 11,401 | 26.474699 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/inceptionv3.py | """
InceptionV3 for ImageNet-1K, implemented in PyTorch.
Original paper: 'Rethinking the Inception Architecture for Computer Vision,'
https://arxiv.org/abs/1512.00567.
"""
__all__ = ['InceptionV3', 'inceptionv3', 'MaxPoolBranch', 'AvgPoolBranch', 'Conv1x1Branch', 'ConvSeqBranch']
import os
import torch
im... | 21,472 | 29.807747 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/fdmobilenet.py | """
FD-MobileNet for ImageNet-1K, implemented in PyTorch.
Original paper: 'FD-MobileNet: Improved MobileNet with A Fast Downsampling Strategy,'
https://arxiv.org/abs/1802.03750.
"""
__all__ = ['fdmobilenet_w1', 'fdmobilenet_w3d4', 'fdmobilenet_wd2', 'fdmobilenet_wd4', 'get_fdmobilenet']
import os
from .mo... | 4,771 | 29.394904 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/others/_inceptionresnetv1_.py | __all__ = ['inceptionresnetv1']
import torch
from torch import nn
from common import conv1x1, ConvBlock, conv1x1_block, conv3x3_block, Concurrent
class MaxPoolBranch(nn.Module):
"""
InceptionResNetV2 specific max pooling branch block.
"""
def __init__(self):
super(MaxPoolBranch, self).__init_... | 15,341 | 28.334608 | 108 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/others/oth_vit.py | from functools import partial
import torch
import torch.nn as nn
class Attention(nn.Module):
def __init__(self,
dim,
num_heads=8,
qkv_bias=False,
qk_scale=None,
attn_drop=0.,
proj_drop=0.):
super().__init... | 9,413 | 31.129693 | 118 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/others/_espnet.py | """
ESPNet for image segmentation, implemented in PyTorch.
Original paper: 'ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation,'
https://arxiv.org/abs/1803.06815.
"""
__all__ = ['ESPNet', 'espnet_cityscapes']
import os
import torch
import torch.nn as nn
from common import ... | 8,299 | 29.181818 | 115 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/others/oth_espnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class CBR(nn.Module):
'''
This class defines the convolution layer with batch normalization and PReLU activation
'''
def __init__(self, nIn, nOut, kSize, stride=1):
'''
:param nIn: number of input channels
:para... | 15,567 | 33.90583 | 151 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/others/oth_quartznet.py | __all__ = ['oth_quartznet5x5_en_ls', 'oth_quartznet15x5_en', 'oth_quartznet15x5_en_nr', 'oth_quartznet15x5_fr',
'oth_quartznet15x5_de', 'oth_quartznet15x5_it', 'oth_quartznet15x5_es', 'oth_quartznet15x5_ca',
'oth_quartznet15x5_pl', 'oth_quartznet15x5_ru', 'oth_jasperdr10x5_en', 'oth_jasperdr10x5_e... | 11,139 | 34.253165 | 111 | py |
imgclsmob | imgclsmob-master/pytorch/pytorchcv/models/others/oth_inception_resnet_v1.py | __all__ = ['oth_inceptionresnetv1']
import torch
from torch import nn
class BasicConv2d(nn.Module):
def __init__(self,
in_planes,
out_planes,
kernel_size,
stride,
padding=0):
super().__init__()
self.conv = nn.Co... | 9,240 | 28.336508 | 97 | py |
imgclsmob | imgclsmob-master/keras_/setup.py | from setuptools import setup, find_packages
from os import path
from io import open
here = path.abspath(path.dirname(__file__))
with open(path.join(here, 'README.md'), encoding='utf-8') as f:
long_description = f.read()
setup(
name='kerascv',
version='0.0.40',
description='Image classification models ... | 1,280 | 36.676471 | 119 | py |
imgclsmob | imgclsmob-master/keras_/utils.py | import math
import logging
import os
from keras import backend as K
from keras.utils.np_utils import to_categorical
import mxnet as mx
from keras_.kerascv.model_provider import get_model
def prepare_ke_context(num_gpus,
batch_size):
batch_size *= max(1, num_gpus)
return batch_size
d... | 4,497 | 28.592105 | 91 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/shufflenetv2.py | """
ShuffleNet V2 for ImageNet-1K, implemented in Keras.
Original paper: 'ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design,'
https://arxiv.org/abs/1807.11164.
"""
__all__ = ['shufflenetv2', 'shufflenetv2_wd2', 'shufflenetv2_w1', 'shufflenetv2_w3d2', 'shufflenetv2_w2']
import os
fr... | 11,732 | 29.396373 | 115 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/igcv3.py | """
IGCV3 for ImageNet-1K, implemented in Keras.
Original paper: 'IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks,'
https://arxiv.org/abs/1806.00178.
"""
__all__ = ['igcv3', 'igcv3_w1', 'igcv3_w3d4', 'igcv3_wd2', 'igcv3_wd4']
import os
from keras import layers as nn
from ... | 9,422 | 29.495146 | 117 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/preresnet.py | """
PreResNet for ImageNet-1K, implemented in Keras.
Original paper: 'Identity Mappings in Deep Residual Networks,' https://arxiv.org/abs/1603.05027.
"""
__all__ = ['preresnet', 'preresnet10', 'preresnet12', 'preresnet14', 'preresnetbc14b', 'preresnet16', 'preresnet18_wd4',
'preresnet18_wd2', 'prere... | 26,177 | 31.398515 | 120 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/shufflenetv2b.py | """
ShuffleNet V2 for ImageNet-1K, implemented in Keras. The alternative variant.
Original paper: 'ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design,'
https://arxiv.org/abs/1807.11164.
"""
__all__ = ['shufflenetv2b', 'shufflenetv2b_wd2', 'shufflenetv2b_w1', 'shufflenetv2b_w3d2', 'sh... | 11,952 | 27.941889 | 115 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/menet.py | """
MENet for ImageNet-1K, implemented in Keras.
Original paper: 'Merging and Evolution: Improving Convolutional Neural Networks for Mobile Applications,'
https://arxiv.org/abs/1803.09127.
"""
__all__ = ['menet', 'menet108_8x1_g3', 'menet128_8x1_g4', 'menet160_8x1_g8', 'menet228_12x1_g3', 'menet256_12x1_g4... | 15,495 | 30.054108 | 116 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/efficientnet.py | """
EfficientNet for ImageNet-1K, implemented in Keras.
Original paper: 'EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,'
https://arxiv.org/abs/1905.11946.
"""
__all__ = ['efficientnet_model', 'efficientnet_b0', 'efficientnet_b1', 'efficientnet_b2', 'efficientnet_b3',
'... | 29,565 | 34.366029 | 120 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/squeezenext.py | """
SqueezeNext for ImageNet-1K, implemented in Keras.
Original paper: 'SqueezeNext: Hardware-Aware Neural Network Design,' https://arxiv.org/abs/1803.10615.
"""
__all__ = ['squeezenext', 'sqnxt23_w1', 'sqnxt23_w3d2', 'sqnxt23_w2', 'sqnxt23v5_w1', 'sqnxt23v5_w3d2', 'sqnxt23v5_w2']
import os
from keras import ... | 11,973 | 29.390863 | 119 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/resnet.py | """
ResNet for ImageNet-1K, implemented in Keras.
Original paper: 'Deep Residual Learning for Image Recognition,' https://arxiv.org/abs/1512.03385.
"""
__all__ = ['resnet', 'resnet10', 'resnet12', 'resnet14', 'resnetbc14b', 'resnet16', 'resnet18_wd4', 'resnet18_wd2',
'resnet18_w3d4', 'resnet18', 're... | 24,153 | 30.698163 | 118 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/mobilenetv2.py | """
MobileNetV2 for ImageNet-1K, implemented in Keras.
Original paper: 'MobileNetV2: Inverted Residuals and Linear Bottlenecks,' https://arxiv.org/abs/1801.04381.
"""
__all__ = ['mobilenetv2', 'mobilenetv2_w1', 'mobilenetv2_w3d4', 'mobilenetv2_wd2', 'mobilenetv2_wd4']
import os
from keras import layers as nn
... | 9,328 | 30.305369 | 118 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/squeezenet.py | """
SqueezeNet for ImageNet-1K, implemented in Keras.
Original paper: 'SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size,'
https://arxiv.org/abs/1602.07360.
"""
__all__ = ['squeezenet', 'squeezenet_v1_0', 'squeezenet_v1_1', 'squeezeresnet_v1_0', 'squeezeresnet_v1_1']
impor... | 12,000 | 29.693095 | 118 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/vgg.py | """
VGG for ImageNet-1K, implemented in Keras.
Original paper: 'Very Deep Convolutional Networks for Large-Scale Image Recognition,'
https://arxiv.org/abs/1409.1556.
"""
__all__ = ['vgg', 'vgg11', 'vgg13', 'vgg16', 'vgg19', 'bn_vgg11', 'bn_vgg13', 'bn_vgg16', 'bn_vgg19', 'bn_vgg11b',
'bn_vgg13b'... | 13,419 | 29.639269 | 117 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/mnasnet.py | """
MnasNet for ImageNet-1K, implemented in Keras.
Original paper: 'MnasNet: Platform-Aware Neural Architecture Search for Mobile,' https://arxiv.org/abs/1807.11626.
"""
__all__ = ['mnasnet_model', 'mnasnet_b1', 'mnasnet_a1', 'mnasnet_small']
import os
from keras import layers as nn
from keras.models import M... | 14,240 | 31.439636 | 118 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/seresnet.py | """
SE-ResNet for ImageNet-1K, implemented in Keras.
Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
"""
__all__ = ['seresnet', 'seresnet10', 'seresnet12', 'seresnet14', 'seresnet16', 'seresnet18', 'seresnet26',
'seresnetbc26b', 'seresnet34', 'seresnetbc38b', 'se... | 17,838 | 31.02693 | 118 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/densenet.py | """
DenseNet for ImageNet-1K, implemented in Keras.
Original paper: 'Densely Connected Convolutional Networks,' https://arxiv.org/abs/1608.06993.
"""
__all__ = ['densenet', 'densenet121', 'densenet161', 'densenet169', 'densenet201']
import os
from keras import layers as nn
from keras.models import Model
from ... | 9,837 | 28.722054 | 116 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/seresnext.py | """
SE-ResNeXt for ImageNet-1K, implemented in Keras.
Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
"""
__all__ = ['seresnext', 'seresnext50_32x4d', 'seresnext101_32x4d', 'seresnext101_64x4d']
import os
from keras import layers as nn
from keras.models import Model
from .... | 8,382 | 29.046595 | 115 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/mobilenetv3.py | """
MobileNetV3 for ImageNet-1K, implemented in Keras.
Original paper: 'Searching for MobileNetV3,' https://arxiv.org/abs/1905.02244.
"""
__all__ = ['mobilenetv3', 'mobilenetv3_small_w7d20', 'mobilenetv3_small_wd2', 'mobilenetv3_small_w3d4',
'mobilenetv3_small_w1', 'mobilenetv3_small_w5d4', 'mobilen... | 18,859 | 32.204225 | 115 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/sepreresnet.py | """
SE-PreResNet for ImageNet-1K, implemented in Keras.
Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
"""
__all__ = ['sepreresnet', 'sepreresnet10', 'sepreresnet12', 'sepreresnet14', 'sepreresnet16', 'sepreresnet18',
'sepreresnet26', 'sepreresnetbc26b', 'seprer... | 18,104 | 31.739602 | 119 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/resnext.py | """
ResNeXt for ImageNet-1K, implemented in Keras.
Original paper: 'Aggregated Residual Transformations for Deep Neural Networks,' http://arxiv.org/abs/1611.05431.
"""
__all__ = ['resnext', 'resnext14_16x4d', 'resnext14_32x2d', 'resnext14_32x4d', 'resnext26_16x4d', 'resnext26_32x2d',
'resnext26_32x4... | 14,656 | 30.45279 | 119 | py |
imgclsmob | imgclsmob-master/keras_/kerascv/models/senet.py | """
SENet for ImageNet-1K, implemented in Keras.
Original paper: 'Squeeze-and-Excitation Networks,' https://arxiv.org/abs/1709.01507.
"""
__all__ = ['senet', 'senet16', 'senet28', 'senet40', 'senet52', 'senet103', 'senet154']
import os
import math
from keras import layers as nn
from keras.models import Model
... | 13,026 | 27.381264 | 115 | py |
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