引言此系列重点在于复现计算机视觉（分类、目标检测、语义分割）中深度学习各个经典的网络模型，以便初学者使用（浅入深出）！

代码都运行无误！！

首先复现深度学习的经典分类网络模块，其中专门做目标检测的Backbone(10.，11.)但是它的主要目的是用来提取特征所以也放在这里，有：

1.LeNet5(√)

2.VGG(√)

3.AlexNet(√)

4.ResNet(√)

5.ResNeXt

6.GoogLeNet

7.MobileNet

8.ShuffleNet

9.EfficientNet

10.VovNet

11.DarkNet

...

注意：

a) 完整代码上传至我的github

/HanXiaoyiGitHub/Simple-CV-Pytorch-master/HanXiaoyiGitHub/Simple-CV-Pytorch-masterb) 编译环境设置为 （其实不用这个编译环境，你会调bug也行！）

python == 3.9.12torch == 1.11.0+cu113torchvision== 0.11.0+cu113torchaudio== 0.12.0+cu113pycocotools == 2.0.4numpyCythonmatplotlibopencv-pythonskimagetensorobardtqdmthop

c) 分类数据集使用ImageNet或CIFAR10，其目录 （coco和voc用于目标检测和语义分割现在暂时用不到）：

dataset path: /data/data||----coco----|----coco||----cifar||----ImageNet----|----ILSVRC||----VOCdevkitcoco path: /data/coco/cocococo|||----annotations|----train|----test|----valvoc path: /data/VOCdevkit|||----Annotations||----ImageSets|----VOC----|----JPEGImages||----SegmentationClass||----SegmentationObject||||----Annotations||----ImageSets|----VOC----|----JPEGImages||----SegmentationClass||----SegmentationObjectILSVRC path : /data/ImageNet/ILSVRC||----train||----valcifar path: /data/cifar||----cifar-10-batches-py||----cifar-10-python.tar.gz

d) 使用了amp混精度使gpu加速，若不知如何使用可参考如下链接：

如何使用Pytorch让网络模型加速训练？（autocast与GradScaler）/XiaoyYidiaodiao/article/details/124854343?spm=1001..3001.5502

所以需要在网络模型的forward函数前加入 @autocast()，并且又因为使用了1.4以上版本的torch，必须修改ReLu(inplace=False)，Dropout(inplace=False)，等等有inplace都设置为False。

e) 由于LeNet5、VGG16、AlexNet使用了全连接层不能修改图像的size，所以这些网络架构在图像预处理时图像的size就必须固定

f) 项目文件结构

使用的OS (Ubuntu 20.04)，当然windows下也能运行，我运行过。有的文件夹用不上，先别管，我之后会讲。

project path: /data/PycharmProject/Simple-CV-master path: /data/PycharmProject/Simple-CV-Pytorch-master||----checkpoints ( resnet50-19c8e357.pth \COCO_ResNet50.pth[RetinaNet]\ VOC_ResNet50.pth[RetinaNet] )|| |----cifar.py （ null, I just use torchvision.datasets.ImageFolder ）| |----CIAR_labels.txt| |----coco.py| |----coco_eval.py| |----coco_labels.txt|----data----|----__init__.py| |----config.py ( path )| |----imagenet.py ( null, I just use torchvision.datasets.ImageFolder )| |----ImageNet_labels.txt| |----voc0712.py| |----voc_eval.py| |----voc_labels.txt| |----crash_helmet.jpg|----images----|----classification----|----sunflower.jpg| | |----photocopier.jpg| | |----automobile.jpg| || |----detection----|----000001.jpg| |----000001.xml| |----000002.jpg| |----000002.xml| |----000003.jpg| |----000003.xml||----log(XXX[ detection or classification ]_XXX[ train or test or eval ].info.log)|| |----__init__.py| || | |----__init.py| |----anchor----|----RetinaNetAnchors.py| || ||----lenet5.py| ||----alexnet.py| |----basenet----|----vgg.py| ||----resnet.py| || | |----DarkNetBackbone.py| |----backbones----|----__init__.py ( Don't finish writing )| | |----ResNetBackbone.py| | |----VovNetBackbone.py| || || ||----models----|----heads----|----__init.py| | |----RetinaNetHeads.py| || | |----RetinaNetLoss.py| |----losses----|----__init.py| || | |----FPN.py| |----necks----|----__init__.py| | |-----FPN.txt| || |----RetinaNet.py||----results ( eg: detection ( VOC or COCO AP ) )||----tensorboard ( Loss visualization )||----tools |----eval.py| |----classification----|----train.py| | |----test.py| || || || | |----eval_coco.py| | |----eval_voc.py| |----detection----|----test.py| |----train.py||| |----AverageMeter.py| |----BBoxTransform.py| |----ClipBoxes.py| |----Sampler.py| |----iou.py|----utils----|----__init__.py| |----accuracy.py| |----augmentations.py| |----collate.py| |----get_logger.py| |----nms.py| |----path.py||----FolderOrganization.txt||----main.py||----README.md||----requirements.txt

1.LeNet5(size: 32 * 32 * 3)[1]

图 1.

如图 1.还原代码

加入nn.BatchNorm2d()，使其精度上升，当然为了完全复现，你们可以忽略掉nn.BatchNorm2d()，将其从代码中删除。

可根据数据集类别自行调整最后一层连接层的输出

from torch import nnfrom torch.cuda.amp import autocastclass lenet5(nn.Module):# cifar: 10, ImageNet: 1000def __init__(self, num_classes=1000, init_weights=False):super(lenet5, self).__init__()self.num_classes = num_classesself.layers = nn.Sequential(# input:32 * 32 * 3 -> 28 * 28 * 6nn.Conv2d(in_channels=3, out_channels=6, kernel_size=5, padding=0, stride=1, bias=False),nn.BatchNorm2d(6),nn.ReLU(),# 28 * 28 * 6 -> 14 * 14 * 6nn.MaxPool2d(kernel_size=2, stride=2, padding=0),# 14 * 14 * 6 -> 10 * 10 * 16nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, padding=0, stride=1, bias=False),nn.BatchNorm2d(16),nn.ReLU(),# 10 * 10 * 16 -> 5 * 5 * 16nn.MaxPool2d(kernel_size=2, stride=2, padding=0),nn.Flatten(),nn.Linear(16 * 5 * 5, 120),nn.Linear(120, 84))self.classifier = nn.Linear(84, self.num_classes)if init_weights:self._initialize_weights()@autocast()def forward(self, x):x = self.layers(x)x = self.classifier(x)return xdef _initialize_weights(self):for m in self.modules():if isinstance(m, nn.Conv2d):nn.init.xavier_uniform_(m.weight)if m.bias is not None:nn.init.constant_(m.bias, 0)elif isinstance(m, nn.Linear):nn.init.xavier_uniform_(m.weight)nn.init.constant_(m.bias, 0)

2.AlexNet(Size: 224 * 224* 3)[2]

图 2.

如图 2.，若不是特别清楚，可参考下图 3.

图 3.

将图 3. 转成图 4. ，这是因为之前的AlexNet是放在两张显卡（当年的计算力是不行的）上跑，现在的计算力能跟上了，可放在一张GPU上跑。

图 4.

可根据数据集类别自行调整最后一层连接层的输出

加入nn.BatchNorm2d()，使其精度上升，当然为了完全复现，你们可以忽略掉nn.BatchNorm2d()，将其从代码中删除。

import torch.nn as nnfrom torch.cuda.amp import autocastclass alexnet(nn.Module):def __init__(self, num_classes=1000, init_weights=False):super(alexnet, self).__init__()self.layers = nn.Sequential(# input: 224 * 224 * 3 -> 55 * 55 * (48*2)nn.Conv2d(in_channels=3, out_channels=96, kernel_size=11, stride=4, padding=2, bias=False),nn.BatchNorm2d(96),nn.ReLU(),# 55 * 55 * (48*2) -> 27 * 27 * (48*2)nn.MaxPool2d(kernel_size=3, stride=2),# 27 * 27 * (48*2) -> 27 * 27 * (128*2)nn.Conv2d(in_channels=96, out_channels=256, kernel_size=5, padding=2, bias=False),nn.BatchNorm2d(256),nn.ReLU(),# 27 * 27 * (128*2) -> 13 * 13 * (128*2)nn.MaxPool2d(kernel_size=3, stride=2),# 13 * 13 * (128*2) -> 13 * 13 * (192*2)nn.Conv2d(in_channels=256, out_channels=384, kernel_size=3, padding=1, bias=False),nn.BatchNorm2d(384),nn.ReLU(),# 13 * 13 * (192*2) -> 13 * 13 * (192*2)nn.Conv2d(in_channels=384, out_channels=384, kernel_size=3, padding=1, bias=False),nn.BatchNorm2d(384),nn.ReLU(),# 13 * 13 * (192*2) -> 13 * 13 * (128*2)nn.Conv2d(in_channels=384, out_channels=256, kernel_size=3, padding=1, bias=False),nn.BatchNorm2d(256),nn.ReLU(),# 13 * 13 * (128*2) -> 6 * 6 * (128*2)nn.MaxPool2d(kernel_size=3, stride=2))self.fc = nn.Sequential(nn.Flatten(),nn.Dropout(0.5),nn.Linear(6 * 6 * 128 * 2, 2048),nn.ReLU(),nn.Dropout(0.5),nn.Linear(2048, 2048),nn.ReLU())self.classifier = nn.Linear(2048, num_classes)if init_weights:self._initialize_weights()@autocast()def forward(self, x):x = self.layers(x)x = self.fc(x)x = self.classifier(x)return xdef _initialize_weights(self):for m in self.modules():if isinstance(m, nn.Conv2d):nn.init.xavier_uniform_(m.weight)if m.bias is not None:nn.init.constant_(m.bias, 0)elif isinstance(m, nn.Linear):nn.init.xavier_uniform_(m.weight)nn.init.constant_(m.bias, 0)

3.VGG(Size: 224 * 224*3)[3]

图 5.

如图 5.复现绿框中框出的网络架构，还原代码

我是看不下去精度那么差，所以我就把nn.BatchNorm2d(i)添加进去，并且做了迁移学习。

可根据数据集类别自行调整最后一层连接层的输出

import torchfrom torch import nnfrom utils.path import CheckPointsfrom torch.cuda.amp import autocast__all__ = ['vgg11','vgg13','vgg16','vgg19',]# if your network is limited, you can download them, and put them into CheckPoints(my Project:Simple-CV-Pytorch-master/checkpoints/).model_urls = {# 'vgg11': '/models/vgg11-bbd30ac9.pth','vgg11': '{}/vgg11-bbd30ac9.pth'.format(CheckPoints),# 'vgg13': '/models/vgg13-c768596a.pth','vgg13': '{}/vgg13-c768596a.pth'.format(CheckPoints),# 'vgg16': '/models/vgg16-397923af.pth','vgg16': '{}/vgg16-397923af.pth'.format(CheckPoints),# 'vgg19': '/models/vgg19-dcbb9e9d.pth','vgg19': '{}/vgg19-dcbb9e9d.pth'.format(CheckPoints)}def vgg_(arch, num_classes, pretrained, init_weights=False, **kwargs):cfg = cfgs["vgg" + arch]features = make_features(cfg)model = vgg(num_classes=num_classes, features=features, init_weights=init_weights, **kwargs)# if you're training for the first time, no pretrained is required!if pretrained:pretrained_models = torch.load(model_urls["vgg" + arch])# transfer learning# if you want to train your own datasetif arch == '11':del pretrained_models['features.8.weight']del pretrained_models['features.11.weight']del pretrained_models['features.16.weight']elif arch == '13':del pretrained_models['features.7.weight']del pretrained_models['features.10.weight']del pretrained_models['features.15.weight']del pretrained_models['features.17.weight']del pretrained_models['features.22.weight']elif arch == '16':del pretrained_models['features.7.weight']del pretrained_models['features.10.weight']del pretrained_models['features.14.weight']del pretrained_models['features.17.weight']del pretrained_models['features.21.weight']del pretrained_models['features.24.weight']del pretrained_models['features.28.weight']elif arch == '19':del pretrained_models['features.7.weight']del pretrained_models['features.10.weight']del pretrained_models['features.14.weight']del pretrained_models['features.21.weight']del pretrained_models['features.23.weight']del pretrained_models['features.28.weight']del pretrained_models['features.34.weight']else:raise ValueError("Pretrained: unsupported VGG depth")model.load_state_dict(pretrained_models, strict=False)return modeldef vgg11(num_classes, pretrained=False, init_weights=False, **kwargs):return vgg_('11', num_classes, pretrained, init_weights, **kwargs)def vgg13(num_classes, pretrained=False, init_weights=False, **kwargs):return vgg_('13', num_classes, pretrained, init_weights, **kwargs)def vgg16(num_classes, pretrained=False, init_weights=False, **kwargs):return vgg_('16', num_classes, pretrained, init_weights, **kwargs)def vgg19(num_classes, pretrained=False, init_weights=False, **kwargs):return vgg_('19', num_classes, pretrained, init_weights, **kwargs)class vgg(nn.Module):# cifar: 10, ImageNet: 1000def __init__(self, features, num_classes=1000, init_weights=False):super(vgg, self).__init__()self.num_classes = num_classesself.features = featuresself.fc = nn.Sequential(nn.Flatten(),nn.Linear(7 * 7 * 512, 4096),nn.ReLU(),nn.Dropout(0.5),nn.Linear(4096, 4096),nn.ReLU(),nn.Dropout(0.5),)self.classifier = nn.Linear(4096, self.num_classes)if init_weights:self._initialize_weights()@autocast()def forward(self, x):x = self.features(x)x = x.view(x.size(0), -1)x = self.fc(x)x = self.classifier(x)return xdef _initialize_weights(self):for m in self.modules():if isinstance(m, nn.Conv2d):nn.init.xavier_uniform_(m.weight)if m.bias is not None:nn.init.constant_(m.bias, 0)elif isinstance(m, nn.Linear):nn.init.xavier_uniform_(m.weight)nn.init.constant_(m.bias, 0)elif isinstance(m, nn.BatchNorm2d):nn.init.xavier_uniform_(m.weight)nn.init.constant_(m.bias, 0)def make_features(cfgs: list):layers = []in_channels = 3for i in cfgs:if i == "M":layers += [nn.MaxPool2d(kernel_size=2, stride=2)]else:conv2d = nn.Conv2d(in_channels, i, kernel_size=3, stride=1, padding=1, bias=False)layers += [conv2d, nn.BatchNorm2d(i), nn.ReLU()]in_channels = ireturn nn.Sequential(*layers)cfgs = {'vgg11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],'vgg13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],'vgg16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],'vgg19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],}

4.ResNet[4]

图 6.

如图 6. 复现网络架构（ResNet18，ResNet34，ResNet50，ResNet101，ResNet152），还原代码

首先来看每个block如何复现？如图18-layer, 34-layer由下图 7. 绿框表示，50-layer, 101-layer, 152-layer由下图 7. 蓝框表示；

block: 18-layer, 34-layer

# 18-layer, 34-layerclass BasicBlock(nn.Module):expansion = 1def __init__(self, in_channels, out_channels, stride=1, downsample=None):super(BasicBlock, self).__init__()self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=stride,padding=1, bias=False)self.bn1 = nn.BatchNorm2d(out_channels)self.relu = nn.ReLU()self.conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1,bias=False)self.bn2 = nn.BatchNorm2d(out_channels)self.downsample = downsampledef forward(self, x):identity = xif self.downsample is not None:identity = self.downsample(x)out = self.conv1(x)out = self.bn1(out)out = self.relu(out)out = self.conv2(out)out = self.bn2(out)out += identityout = self.relu(out)return out

block: 50-layer, 101-layer, 152-layer

# 50-layer, 101-layer, 152-layerclass Bottleneck(nn.Module):"""self.conv1(kernel_size=1,stride=2)self.conv2(kernel_size=3,stride=1)toself.conv1(kernel_size=1,stride=1)self.conv2(kernel_size=3,stride=2)"""expansion = 4def __init__(self, in_channels, out_channels, stride=1, downsample=None):super(Bottleneck, self).__init__()self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1,stride=1, bias=False)self.bn1 = nn.BatchNorm2d(out_channels)self.conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=3,stride=stride, bias=False)self.bn2 = nn.BatchNorm2d(out_channels)self.conv3 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels * self.expansion, kernel__size=1,stride=1, bias=False)self.bn3 = nn.BatchNorm2d(out_channels * self.expansion)self.relu = nn.ReLU()self.downsample = downsampledef forward(self, x):identity = xif self.downsample is not None:identity = self.downsample(x)out = self.conv1(x)out = self.bn1(out)out = self.relu(out)out = self.conv2(out)out = self.bn2(out)out = self.relu(out)out = self.conv3(out)out = self.bn3(out)out += identityout = self.relu(out)return out

整个ResNet模型的还原，先还原第一层卷积和最大池化层

class ResNet(nn.Module):def __init__(self, block, blocks_num, num_classes=1000, include_top=True):super(ResNet, self).__init__()self.include_top = include_topself.in_channels = 64self.conv1 = nn.Conv2d(in_channels=3, out_channels=self.in_channels, kernel_size=7, stride=2,padding=3, bias=False)self.bn1 = nn.BatchNorm2d(self.in_channels)self.relu = nn.ReLU()self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

之后的layer层，如图 8. 代码与图中的表示模块的对等关系

conv2_x -> self.layer1, conv3_x -> self.layer2, conv4_x -> self.layer3, conv5_x -> self.layer4

...self.layer1 = self._make_layer(block, 64, blocks_num[0])self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2)self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2)self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2)...

50-layer, 101-layer, 152-layer 复现虚线部分；18-layer，34-layer也是这样，就不展示了。

图 8.

def _make_layer(self, block, channels, block_num, stride=1):downsample = Noneif stride != 1 or self.in_channels != channels * block.expansion:downsample = nn.Sequential(nn.Conv2d(in_channels=self.in_channels, out_channels=channels * block.expansion,kernel_size=1, stride=stride, bias=False),nn.BatchNorm2d(channels * block.expansion))...

之后调用ResNet模型，选取合适的layer(18, 34, 50, 101, 152)

def resnet18(num_classes=1000, pretrained=False, include_top=True):return resnet_('18', BasicBlock, [2, 2, 2, 2], num_classes, pretrained, include_top)def resnet34(num_classes=1000, pretrained=False, include_top=True):return resnet_('34', BasicBlock, [3, 4, 6, 3], num_classes, pretrained, include_top)def resnet50(num_classes=1000, pretrained=False, include_top=True):return resnet_('50', Bottleneck, [3, 4, 6, 3], num_classes, pretrained, include_top)def resnet101(num_classes=1000, pretrained=False, include_top=True):return resnet_('101', Bottleneck, [3, 4, 23, 3], num_classes, pretrained, include_top)def resnet152(num_classes=1000, pretrained=False, include_top=True):return resnet_('152', Bottleneck, [3, 8, 36, 3], num_classes, pretrained, include_top)

完整代码

import torchimport torch.nn as nnfrom utils.path import CheckPointsfrom torch.cuda.amp import autocast__all__ = ['resnet18','resnet34','resnet50','resnet101','resnet152']# if your network is limited, you can download them, and put them into CheckPoints(my Project:Simple-CV-Pytorch-master/checkpoints/).model_urls = {# 'resnet18': '/models/resnet18-5c106cde.pth','resnet18': '{}/resnet18-5c106cde.pth'.format(CheckPoints),# 'resnet34': '/models/resnet34-333f7ec4.pth','resnet34': '{}/resnet34-333f7ec4.pth'.format(CheckPoints),# 'resnet50': '/models/resnet50-19c8e357.pth','resnet50': '{}/resnet50-19c8e357.pth'.format(CheckPoints),# 'resnet101': '/models/resnet101-5d3b4d8f.pth','resnet101': '{}/resnet101-5d3b4d8f.pth'.format(CheckPoints),# 'resnet152': '/models/resnet152-b121ed2d.pth','resnet152': '{}/resnet152-b121ed2d.pth'.format(CheckPoints)}def resnet_(arch, block, block_num, num_classes, pretrained, include_top, **kwargs):model = resnet(block=block, blocks_num=block_num, num_classes=num_classes, include_top=include_top, **kwargs)# if you're training for the first time, no pretrained is required!if pretrained:# if you want to use cpu, you should modify map_loaction=torch.device("cpu")pretrained_models = torch.load(model_urls["resnet" + arch], map_location=torch.device("cuda:0"))# transfer learning# if you want to train your own dataset# del pretrained_models['module.classifier.bias']model.load_state_dict(pretrained_models, strict=False)return model# 18-layer, 34-layerclass BasicBlock(nn.Module):expansion = 1def __init__(self, in_channels, out_channels, stride=1, downsample=None):super(BasicBlock, self).__init__()self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=stride,padding=1, bias=False)self.bn1 = nn.BatchNorm2d(out_channels)self.relu = nn.ReLU()self.conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=3, stride=1, padding=1,bias=False)self.bn2 = nn.BatchNorm2d(out_channels)self.downsample = downsample@autocast()def forward(self, x):identity = xif self.downsample is not None:identity = self.downsample(x)out = self.conv1(x)out = self.bn1(out)out = self.relu(out)out = self.conv2(out)out = self.bn2(out)out += identityout = self.relu(out)return out# 50-layer, 101-layer, 152-layerclass Bottleneck(nn.Module):"""self.conv1(kernel_size=1,stride=2)self.conv2(kernel_size=3,stride=1)toself.conv1(kernel_size=1,stride=1)self.conv2(kernel_size=3,stride=2)acc: up 0.5%"""expansion = 4def __init__(self, in_channels, out_channels, stride=1, downsample=None):super(Bottleneck, self).__init__()self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1,stride=1, bias=False)self.bn1 = nn.BatchNorm2d(out_channels)self.conv2 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels, kernel_size=3,stride=stride, bias=False)self.bn2 = nn.BatchNorm2d(out_channels)self.conv3 = nn.Conv2d(in_channels=out_channels, out_channels=out_channels * self.expansion, kernel__size=1,stride=1, bias=False)self.bn3 = nn.BatchNorm2d(out_channels * self.expansion)self.relu = nn.ReLU()self.downsample = downsample@autocast()def forward(self, x):identity = xif self.downsample is not None:identity = self.downsample(x)out = self.conv1(x)out = self.bn1(out)out = self.relu(out)out = self.conv2(out)out = self.bn2(out)out = self.relu(out)out = self.conv3(out)out = self.bn3(out)out += identityout = self.relu(out)return outclass resnet(nn.Module):def __init__(self, block, blocks_num, num_classes=1000, include_top=True):super(resnet, self).__init__()self.include_top = include_topself.in_channels = 64self.conv1 = nn.Conv2d(in_channels=3, out_channels=self.in_channels, kernel_size=7, stride=2,padding=3, bias=False)self.bn1 = nn.BatchNorm2d(self.in_channels)self.relu = nn.ReLU()self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)self.layer1 = self._make_layer(block, 64, blocks_num[0])self.layer2 = self._make_layer(block, 128, blocks_num[1], stride=2)self.layer3 = self._make_layer(block, 256, blocks_num[2], stride=2)self.layer4 = self._make_layer(block, 512, blocks_num[3], stride=2)if self.include_top:self.avgpool = nn.AdaptiveAvgPool2d((1, 1))self.flatten = nn.Flatten()self.fc = nn.Linear(512 * block.expansion, num_classes)for m in self.modules():if isinstance(m, nn.Conv2d):nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')def _make_layer(self, block, channels, block_num, stride=1):downsample = Noneif stride != 1 or self.in_channels != channels * block.expansion:downsample = nn.Sequential(nn.Conv2d(in_channels=self.in_channels, out_channels=channels * block.expansion,kernel_size=1, stride=stride, bias=False),nn.BatchNorm2d(channels * block.expansion))layers = []layers.append(block(in_channels=self.in_channels, out_channels=channels, downsample=downsample, stride=stride))self.in_channels = channels * block.expansionfor _ in range(1, block_num):layers.append(block(in_channels=self.in_channels, out_channels=channels))return nn.Sequential(*layers)@autocast()def forward(self, x):x = self.conv1(x)x = self.bn1(x)x = self.relu(x)x = self.maxpool(x)x = self.layer1(x)x = self.layer2(x)x = self.layer3(x)x = self.layer4(x)if self.include_top:x = self.avgpool(x)x = self.flatten(x)x = self.fc(x)return xdef resnet18(num_classes=1000, pretrained=False, include_top=True):return resnet_('18', BasicBlock, [2, 2, 2, 2], num_classes, pretrained, include_top)def resnet34(num_classes=1000, pretrained=False, include_top=True):return resnet_('34', BasicBlock, [3, 4, 6, 3], num_classes, pretrained, include_top)def resnet50(num_classes=1000, pretrained=False, include_top=True):return resnet_('50', Bottleneck, [3, 4, 6, 3], num_classes, pretrained, include_top)def resnet101(num_classes=1000, pretrained=False, include_top=True):return resnet_('101', Bottleneck, [3, 4, 23, 3], num_classes, pretrained, include_top)def resnet152(num_classes=1000, pretrained=False, include_top=True):return resnet_('152', Bottleneck, [3, 8, 36, 3], num_classes, pretrained, include_top)

一些配置文件

utils/path.py

import os.pathimport sysBASE_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))sys.path.append(BASE_DIR)# Gets home dir cross platform# "/data/"MyName = "PycharmProject"Folder = "Simple-CV-Pytorch-master"# Path to store checkpoint modelCheckPoints = 'checkpoints'CheckPoints = os.path.join(BASE_DIR, MyName, Folder, CheckPoints)# Path to store tensorboard loadtensorboard_log = 'tensorboard'tensorboard_log = os.path.join(BASE_DIR, MyName, Folder, tensorboard_log)# Path to save loglog = 'log'log = os.path.join(BASE_DIR, MyName, Folder, log)# Path to save classification train logclassification_train_log = 'classification_train'# Path to save classification test logclassification_test_log = 'classification_test'# Path to save classification eval logclassification_eval_log = 'classification_eval'# Classification evaluate model pathclassification_evaluate = None# Images classification pathimage_cls = 'automobile.jpg'images_cls_path = 'images/classification'images_cls_path = os.path.join(BASE_DIR, MyName, Folder, images_cls_path, image_cls)# DataDATAPATH = BASE_DIR# ImageNet/ILSVRCImageNet = "ImageNet/ILSVRC"ImageNet_Train_path = os.path.join(DATAPATH, ImageNet, 'train')ImageNet_Eval_path = os.path.join(DATAPATH, ImageNet, 'val')# CIFAR10CIFAR = 'cifar'CIFAR_path = os.path.join(DATAPATH, CIFAR)

data/config.py

from utils import path# Path to save loglog = path.log# Path to save classification train logclassification_train_log = path.classification_train_log# Path to save classification test logclassification_test_log = path.classification_test_log# Path to save classification eval logclassification_eval_log = path.classification_eval_log# Path to store checkpoint modelcheckpoint_path = path.CheckPoints# Classification evaluate model pathclassification_evaluate = path.classification_evaluate# Classification test imagesimages_cls_root = path.images_cls_path# Path to save tensorboardtensorboard_log = path.tensorboard_log

训练代码

tools/classification/train.py

import osimport loggingimport argparseimport warningswarnings.filterwarnings('ignore')import sysBASE_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))sys.path.append(BASE_DIR)import timeimport torchfrom data import *import torchvisionimport torch.nn as nnimport torch.nn.parallelimport torch.optim as optimfrom torchvision import transformsfrom utils.accuracy import accuracyfrom torch.utils.data import DataLoaderfrom utils.get_logger import get_loggerfrom models.basenets.lenet5 import lenet5from models.basenets.alexnet import alexnetfrom utils.AverageMeter import AverageMeterfrom torch.cuda.amp import autocast, GradScalerfrom models.basenets.vgg import vgg11, vgg13, vgg16, vgg19from models.basenets.resnet import resnet18, resnet34, resnet50, resnet101, resnet152def parse_args():parser = argparse.ArgumentParser(description='PyTorch Classification Training')parser.add_mutually_exclusive_group()parser.add_argument('--dataset',type=str,default='CIFAR',choices=['ImageNet', 'CIFAR'],help='ImageNet, CIFAR')parser.add_argument('--dataset_root',type=str,default=CIFAR_ROOT,choices=[ImageNet_Train_ROOT, CIFAR_ROOT],help='Dataset root directory path')parser.add_argument('--basenet',type=str,default='lenet',choices=['resnet', 'vgg', 'lenet', 'alexnet'],help='Pretrained base model')parser.add_argument('--depth',type=int,default=5,help='BaseNet depth, including: LeNet of 5, AlexNet of 0, VGG of 11, 13, 16, 19, ResNet of 18, 34, 50, 101, 152')parser.add_argument('--batch_size',type=int,default=32,help='Batch size for training')parser.add_argument('--resume',type=str,default=None,help='Checkpoint state_dict file to resume training from')parser.add_argument('--num_workers',type=int,default=8,help='Number of workers user in dataloading')parser.add_argument('--cuda',type=str,default=True,help='Use CUDA to train model')parser.add_argument('--momentum',type=float,default=0.9,help='Momentum value for optim')parser.add_argument('--gamma',type=float,default=0.1,help='Gamma update for SGD')parser.add_argument('--accumulation_steps',type=int,default=1,help='Gradient acumulation steps')parser.add_argument('--save_folder',type=str,default=config.checkpoint_path,help='Directory for saving checkpoint models')parser.add_argument('--tensorboard',type=str,default=False,help='Use tensorboard for loss visualization')parser.add_argument('--log_folder',type=str,default=config.log,help='Log Folder')parser.add_argument('--log_name',type=str,default=config.classification_train_log,help='Log Name')parser.add_argument('--tensorboard_log',type=str,default=config.tensorboard_log,help='Use tensorboard for loss visualization')parser.add_argument('--lr',type=float,default=1e-2,help='learning rate')parser.add_argument('--epochs',type=int,default=30,help='Number of epochs')parser.add_argument('--weight_decay',type=float,default=1e-4,help='weight decay')parser.add_argument('--milestones',type=list,default=[15, 20, 30],help='Milestones')parser.add_argument('--num_classes',type=int,default=10,help='the number classes, like ImageNet:1000, cifar:10')parser.add_argument('--image_size',type=int,default=32,help='image size, like ImageNet:224, cifar:32')parser.add_argument('--pretrained',type=str,default=True,help='Models was pretrained')parser.add_argument('--init_weights',type=str,default=False,help='Init Weights')return parser.parse_args()args = parse_args()# 1. Logget_logger(args.log_folder, args.log_name)logger = logging.getLogger(args.log_name)# 2. Torch choose cuda or cpuif torch.cuda.is_available():if args.cuda:torch.set_default_tensor_type('torch.cuda.FloatTensor')if not args.cuda:print("WARNING: It looks like you have a CUDA device, but you aren't using it" +"\n You can set the parameter of cuda to True.")torch.set_default_tensor_type('torch.FloatTensor')else:torch.set_default_tensor_type('torch.FloatTensor')if not os.path.exists(args.save_folder):os.mkdir(args.save_folder)def train():# 3. Create SummaryWriterif args.tensorboard:from torch.utils.tensorboard import SummaryWriter# tensorboard losswriter = SummaryWriter(args.tensorboard_log)# vgg16, alexnet and lenet5 need to resize image_size, because of fc.if args.basenet == 'vgg' or args.basenet == 'alexnet':args.image_size = 224elif args.basenet == 'lenet':args.image_size = 32# 4. Ready datasetif args.dataset == 'ImageNet':if args.dataset_root == CIFAR_ROOT:raise ValueError('Must specify dataset_root if specifying dataset ImageNet.')elif os.path.exists(ImageNet_Train_ROOT) is None:raise ValueError("WARNING: Using default ImageNet dataset_root because " +"--dataset_root was not specified.")dataset = torchvision.datasets.ImageFolder(root=args.dataset_root,transform=pose([transforms.Resize((args.image_size,args.image_size)),transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225]),]))elif args.dataset == 'CIFAR':if args.dataset_root == ImageNet_Train_ROOT:raise ValueError('Must specify dataset_root if specifying dataset CIFAR10.')elif args.dataset_root is None:raise ValueError("Must provide --dataset_root when training on CIFAR10.")dataset = torchvision.datasets.CIFAR10(root=args.dataset_root, train=True,transform=pose([transforms.Resize((args.image_size,args.image_size)),torchvision.transforms.ToTensor()]))else:raise ValueError('Dataset type not understood (must be ImageNet or CIFAR), exiting.')dataloader = torch.utils.data.DataLoader(dataset=dataset, batch_size=args.batch_size,shuffle=True, num_workers=args.num_workers,pin_memory=False, generator=torch.Generator(device='cuda'))top1 = AverageMeter()top5 = AverageMeter()losses = AverageMeter()# 5. Define train model# Unfortunately, Lenet5 and Alexnet don't provide pretrianed Model.if args.basenet == 'lenet':if args.depth == 5:model = lenet5(num_classes=args.num_classes,init_weights=args.init_weights)else:raise ValueError('Unsupported LeNet depth!')elif args.basenet == 'alexnet':model = alexnet(num_classes=args.num_classes,init_weights=args.init_weights)elif args.basenet == 'vgg':if args.depth == 11:model = vgg11(pretrained=args.pretrained,num_classes=args.num_classes,init_weights=args.init_weights)elif args.depth == 13:model = vgg13(pretrained=args.pretrained,num_classes=args.num_classes,init_weights=args.init_weights)elif args.depth == 16:model = vgg16(pretrained=args.pretrained,num_classes=args.num_classes,init_weights=args.init_weights)elif args.depth == 19:model = vgg19(pretrained=args.pretrained,num_classes=args.num_classes,init_weights=args.init_weights)else:raise ValueError('Unsupported VGG depth!')# Unfortunately for my resnet, there is no set init_weight, because I'm going to set object detection algorithmelif args.basenet == 'resnet':if args.depth == 18:model = resnet18(pretrained=args.pretrained,num_classes=args.num_classes)elif args.depth == 34:model = resnet34(pretrained=args.pretrained,num_classes=args.num_classes)elif args.depth == 50:model = resnet50(pretrained=args.pretrained,num_classes=args.num_classes) # False means the models was not trainedelif args.depth == 101:model = resnet101(pretrained=args.pretrained,num_classes=args.num_classes)elif args.depth == 152:model = resnet152(pretrained=args.pretrained,num_classes=args.num_classes)else:raise ValueError('Unsupported ResNet depth!')else:raise ValueError('Unsupported model type!')if args.cuda:if torch.cuda.is_available():model = model.cuda()model = torch.nn.DataParallel(model).cuda()else:model = torch.nn.DataParallel(model)# 6. Loading weightsif args.resume:other, ext = os.path.splitext(args.resume)if ext == '.pkl' or '.pth':print('Loading weights into state dict...')model_load = os.path.join(args.save_folder, args.resume)model.load_state_dict(torch.load(model_load))else:print('Sorry only .pth and .pkl files supported.')if args.init_weights:# initialize newly added models' weights with xavier methodif args.basenet == 'resnet':print("There is no set init_weight, because I'm going to set object detection algorithm.")else:print("Initializing weights...")else:print("Not Initializing weights...")if args.pretrained:if args.basenet == 'lenet' or args.basenet == 'alexnet':print("There is no available pretrained model on the website. ")else:print("Models was pretrained...")else:print("Pretrained models is False...")model.train()iteration = 0# 7. Optimizeroptimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum,weight_decay=args.weight_decay)criterion = nn.CrossEntropyLoss()scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=args.milestones, gamma=args.gamma)scaler = GradScaler()# 8. Lengthiter_size = len(dataset) // args.batch_sizeprint("len(dataset): {}, iter_size: {}".format(len(dataset), iter_size))logger.info(f"args - {args}")t0 = time.time()# 9. Create batch iteratorfor epoch in range(args.epochs):t1 = time.time()torch.cuda.empty_cache()# 10. Load train datafor data in dataloader:iteration += 1images, targets = data# 11. Backwardoptimizer.zero_grad()if args.cuda:images, targets = images.cuda(), targets.cuda()criterion = criterion.cuda()# 12. Forwardwith autocast():outputs = model(images)loss = criterion(outputs, targets)loss = loss / args.accumulation_stepsif args.tensorboard:writer.add_scalar("train_classification_loss", loss.item(), iteration)scaler.scale(loss).backward()scaler.step(optimizer)scaler.update()# 13. Measure accuracy and record lossacc1, acc5 = accuracy(outputs, targets, topk=(1, 5))top1.update(acc1.item(), images.size(0))top5.update(acc5.item(), images.size(0))losses.update(loss.item(), images.size(0))if iteration % 100 == 0:logger.info(f"- epoch: {epoch}, iteration: {iteration}, lr: {optimizer.param_groups[0]['lr']}, "f"top1 acc: {acc1.item():.2f}%, top5 acc: {acc5.item():.2f}%, "f"loss: {loss.item():.3f}, (losses.avg): {losses.avg:3f} ")scheduler.step(losses.avg)t2 = time.time()h_time = (t2 - t1) // 3600m_time = ((t2 - t1) % 3600) // 60s_time = ((t2 - t1) % 3600) % 60print("epoch {} is finished, and the time is {}h{}min{}s".format(epoch, int(h_time), int(m_time), int(s_time)))# 14. Save train modelif epoch != 0 and epoch % 10 == 0:print('Saving state, iter:', epoch)torch.save(model.state_dict(),args.save_folder + '/' + args.dataset +'_' + args.basenet + str(args.depth) + '_' + repr(epoch) + '.pth')torch.save(model.state_dict(),args.save_folder + '/' + args.dataset + "_" + args.basenet + str(args.depth) + '.pth')if args.tensorboard:writer.close()t3 = time.time()h = (t3 - t0) // 3600m = ((t3 - t0) % 3600) // 60s = ((t3 - t0) % 3600) % 60print("The Finished Time is {}h{}m{}s".format(int(h), int(m), int(s)))return top1.avg, top5.avg, losses.avgif __name__ == '__main__':torch.multiprocessing.set_start_method('spawn')logger.info("Program started")top1, top5, loss = train()print("top1 acc: {}, top5 acc: {}, loss:{}".format(top1, top5, loss))logger.info("Done!")

测试代码

tools/classification/test.py

import loggingimport osimport argparseimport warningswarnings.filterwarnings('ignore')import sysBASE_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))sys.path.append(BASE_DIR)import timefrom data import *from PIL import Imageimport torch.nn.parallelfrom torchvision import transformsfrom utils.get_logger import get_loggerfrom models.basenets.lenet5 import lenet5from models.basenets.alexnet import alexnetfrom models.basenets.vgg import vgg11, vgg13, vgg16, vgg19from models.basenets.resnet import resnet18, resnet34, resnet50, resnet101, resnet152def parse_args():parser = argparse.ArgumentParser(description='PyTorch Classification Testing')parser.add_mutually_exclusive_group()parser.add_argument('--dataset',type=str,default='CIFAR',choices=['ImageNet', 'CIFAR'],help='ImageNet, CIFAR')parser.add_argument('--images_root',type=str,default=config.images_cls_root,help='Dataset root directory path')parser.add_argument('--basenet',type=str,default='alexnet',choices=['resnet', 'vgg', 'lenet', 'alexnet'],help='Pretrained base model')parser.add_argument('--depth',type=int,default=0,help='BaseNet depth, including: LeNet of 5, AlexNet of 0, VGG of 11, 13, 16, 19, ResNet of 18, 34, 50, 101, 152')parser.add_argument('--evaluate',type=str,default=config.classification_evaluate,help='Checkpoint state_dict file to evaluate training from')parser.add_argument('--save_folder',type=str,default=config.checkpoint_path,help='Directory for saving checkpoint models')parser.add_argument('--log_folder',type=str,default=config.log,help='Log Folder')parser.add_argument('--log_name',type=str,default=config.classification_test_log,help='Log Name')parser.add_argument('--cuda',type=str,default=True,help='Use CUDA to train model')parser.add_argument('--num_classes',type=int,default=10,help='the number classes, like ImageNet:1000, cifar:10')parser.add_argument('--image_size',type=int,default=32,help='image size, like ImageNet:224, cifar:32')parser.add_argument('--pretrained',type=str,default=False,help='Models was pretrained')return parser.parse_args()args = parse_args()# 1. Torch choose cuda or cpuif torch.cuda.is_available():if args.cuda:torch.set_default_tensor_type('torch.cuda.FloatTensor')if not args.cuda:print("WARNING: It looks like you have a CUDA device, but you aren't using it" +"\n You can set the parameter of cuda to True.")torch.set_default_tensor_type('torch.FloatTensor')else:torch.set_default_tensor_type('torch.FloatTensor')if not os.path.exists(args.save_folder):os.mkdir(args.save_folder)# 2. Logget_logger(args.log_folder, args.log_name)logger = logging.getLogger(args.log_name)def get_label_file(filename):if not os.path.exists(filename):print("The dataset label.txt is empty, We need to create a new one.")os.mkdir(filename)return filenamedef dataset_labels_results(filename, output):filename = os.path.join(BASE_DIR, 'data', filename + '_labels.txt')get_label_file(filename=filename)with open(file=filename, mode='r') as f:dict = f.readlines()output = output.cpu().numpy()output = output[0]output = dict[output]f.close()return outputdef test():# vgg16, alexnet and lenet5 need to resize image_size, because of fc.if args.basenet == 'vgg' or args.basenet == 'alexnet':args.image_size = 224elif args.basenet == 'lenet':args.image_size = 32# 3. Ready imageif args.images_root is None:raise ValueError("The images is None, you should load image!")image = Image.open(args.images_root)transform = pose([transforms.Resize((args.image_size,args.image_size)),transforms.ToTensor()])image = transform(image)image = image.reshape(1, 3, args.image_size, args.image_size)# 4. Define to train modeif args.basenet == 'lenet':if args.depth == 5:model = lenet5(num_classes=args.num_classes)else:raise ValueError('Unsupported LeNet depth!')elif args.basenet == 'alexnet':model = alexnet(num_classes=args.num_classes)elif args.basenet == 'vgg':if args.depth == 11:model = vgg11(pretrained=args.pretrained, num_classes=args.num_classes)elif args.depth == 13:model = vgg13(pretrained=args.pretrained, num_classes=args.num_classes)elif args.depth == 16:model = vgg16(pretrained=args.pretrained, num_classes=args.num_classes)elif args.depth == 19:model = vgg19(pretrained=args.pretrained, num_classes=args.num_classes)else:raise ValueError('Unsupported VGG depth!')elif args.basenet == 'resnet':if args.depth == 18:model = resnet18(pretrained=args.pretrained,num_classes=args.num_classes)elif args.depth == 34:model = resnet34(pretrained=args.pretrained,num_classes=args.num_classes)elif args.depth == 50:model = resnet50(pretrained=args.pretrained,num_classes=args.num_classes) # False means the models is not trainedelif args.depth == 101:model = resnet101(pretrained=args.pretrained,num_classes=args.num_classes)elif args.depth == 152:model = resnet152(pretrained=args.pretrained,num_classes=args.num_classes)else:raise ValueError('Unsupported ResNet depth!')else:raise ValueError('Unsupported model type!')if args.cuda:model = model.cuda()model = torch.nn.DataParallel(model).cuda()else:model = torch.nn.DataParallel(model)# 5. Loading modelif args.evaluate:other, ext = os.path.splitext(args.evaluate)if ext == '.pkl' or '.pth':print('Loading weights into state dict...')model_evaluate_load = os.path.join(args.save_folder, args.evaluate)model.load_state_dict(torch.load(model_evaluate_load))else:print('Sorry only .pth and .pkl files supported.')elif args.evaluate is None:print("Sorry, you should load weights! ")model.eval()# 6. printlogger.info(f"args - {args}")# 7. Testwith torch.no_grad():t0 = time.time()# 8. Forwardif args.cuda:image = image.cuda()output = model(image)output = output.argmax(1)t1 = time.time()m = (t1 - t0) // 60s = (t1 - t0) % 60folder_name = args.datasetoutput = dataset_labels_results(filename=folder_name, output=output)logger.info(f"output: {output}")print("It took a total of {}m{}s to complete the testing.".format(int(m), int(s)))return outputif __name__ == '__main__':torch.multiprocessing.set_start_method('spawn')logger.info("Program started")output = test()logger.info("Done!")

标签

CIFAR_label.txt

{0: 'airplane',1: 'automobile',2: 'bird',3: 'cat',4: 'deer',5: 'dog',6: 'frog',7: 'horse',8: 'ship',9: 'truck'}

ImageNet_label.txt

{0: 'tench, Tinca tinca',1: 'goldfish, Carassius auratus',2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',3: 'tiger shark, Galeocerdo cuvieri',4: 'hammerhead, hammerhead shark',5: 'electric ray, crampfish, numbfish, torpedo',6: 'stingray',7: 'cock',8: 'hen',9: 'ostrich, Struthio camelus',10: 'brambling, Fringilla montifringilla',11: 'goldfinch, Carduelis carduelis',12: 'house finch, linnet, Carpodacus mexicanus',13: 'junco, snowbird',14: 'indigo bunting, indigo finch, indigo bird, Passerina cyanea',15: 'robin, American robin, Turdus migratorius',16: 'bulbul',17: 'jay',18: 'magpie',19: 'chickadee',20: 'water ouzel, dipper',21: 'kite',22: 'bald eagle, American eagle, Haliaeetus leucocephalus',23: 'vulture',24: 'great grey owl, great gray owl, Strix nebulosa',25: 'European fire salamander, Salamandra salamandra',26: 'common newt, Triturus vulgaris',27: 'eft',28: 'spotted salamander, Ambystoma maculatum',29: 'axolotl, mud puppy, Ambystoma mexicanum',30: 'bullfrog, Rana catesbeiana',31: 'tree frog, tree-frog',32: 'tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui',33: 'loggerhead, loggerhead turtle, Caretta caretta',34: 'leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea',35: 'mud turtle',36: 'terrapin',37: 'box turtle, box tortoise',38: 'banded gecko',39: 'common iguana, iguana, Iguana iguana',40: 'American chameleon, anole, Anolis carolinensis',41: 'whiptail, whiptail lizard',42: 'agama',43: 'frilled lizard, Chlamydosaurus kingi',44: 'alligator lizard',45: 'Gila monster, Heloderma suspectum',46: 'green lizard, Lacerta viridis',47: 'African chameleon, Chamaeleo chamaeleon',48: 'Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis',49: 'African crocodile, Nile crocodile, Crocodylus niloticus',50: 'American alligator, Alligator mississipiensis',51: 'triceratops',52: 'thunder snake, worm snake, Carphophis amoenus',53: 'ringneck snake, ring-necked snake, ring snake',54: 'hognose snake, puff adder, sand viper',55: 'green snake, grass snake',56: 'king snake, kingsnake',57: 'garter snake, grass snake',58: 'water snake',59: 'vine snake',60: 'night snake, Hypsiglena torquata',61: 'boa constrictor, Constrictor constrictor',62: 'rock python, rock snake, Python sebae',63: 'Indian cobra, Naja naja',64: 'green mamba',65: 'sea snake',66: 'horned viper, cerastes, sand viper, horned asp, Cerastes cornutus',67: 'diamondback, diamondback rattlesnake, Crotalus adamanteus',68: 'sidewinder, horned rattlesnake, Crotalus cerastes',69: 'trilobite',70: 'harvestman, daddy longlegs, Phalangium opilio',71: 'scorpion',72: 'black and gold garden spider, Argiope aurantia',73: 'barn spider, Araneus cavaticus',74: 'garden spider, Aranea diademata',75: 'black widow, Latrodectus mactans',76: 'tarantula',77: 'wolf spider, hunting spider',78: 'tick',79: 'centipede',80: 'black grouse',81: 'ptarmigan',82: 'ruffed grouse, partridge, Bonasa umbellus',83: 'prairie chicken, prairie grouse, prairie fowl',84: 'peacock',85: 'quail',86: 'partridge',87: 'African grey, African gray, Psittacus erithacus',88: 'macaw',89: 'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita',90: 'lorikeet',91: 'coucal',92: 'bee eater',93: 'hornbill',94: 'hummingbird',95: 'jacamar',96: 'toucan',97: 'drake',98: 'red-breasted merganser, Mergus serrator',99: 'goose',100: 'black swan, Cygnus atratus',101: 'tusker',102: 'echidna, spiny anteater, anteater',103: 'platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus',104: 'wallaby, brush kangaroo',105: 'koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus',106: 'wombat',107: 'jellyfish',108: 'sea anemone, anemone',109: 'brain coral',110: 'flatworm, platyhelminth',111: 'nematode, nematode worm, roundworm',112: 'conch',113: 'snail',114: 'slug',115: 'sea slug, nudibranch',116: 'chiton, coat-of-mail shell, sea cradle, polyplacophore',117: 'chambered nautilus, pearly nautilus, nautilus',118: 'Dungeness crab, Cancer magister',119: 'rock crab, Cancer irroratus',120: 'fiddler crab',121: 'king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica',122: 'American lobster, Northern lobster, Maine lobster, Homarus americanus',123: 'spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish',124: 'crayfish, crawfish, crawdad, crawdaddy',125: 'hermit crab',126: 'isopod',127: 'white stork, Ciconia ciconia',128: 'black stork, Ciconia nigra',129: 'spoonbill',130: 'flamingo',131: 'little blue heron, Egretta caerulea',132: 'American egret, great white heron, Egretta albus',133: 'bittern',134: 'crane',135: 'limpkin, Aramus pictus',136: 'European gallinule, Porphyrio porphyrio',137: 'American coot, marsh hen, mud hen, water hen, Fulica americana',138: 'bustard',139: 'ruddy turnstone, Arenaria interpres',140: 'red-backed sandpiper, dunlin, Erolia alpina',141: 'redshank, Tringa totanus',142: 'dowitcher',143: 'oystercatcher, oyster catcher',144: 'pelican',145: 'king penguin, Aptenodytes patagonica',146: 'albatross, mollymawk',147: 'grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus',148: 'killer whale, killer, orca, grampus, sea wolf, Orcinus orca',149: 'dugong, Dugong dugon',150: 'sea lion',151: 'Chihuahua',152: 'Japanese spaniel',153: 'Maltese dog, Maltese terrier, Maltese',154: 'Pekinese, Pekingese, Peke',155: 'Shih-Tzu',156: 'Blenheim spaniel',157: 'papillon',158: 'toy terrier',159: 'Rhodesian ridgeback',160: 'Afghan hound, Afghan',161: 'basset, basset hound',162: 'beagle',163: 'bloodhound, sleuthhound',164: 'bluetick',165: 'black-and-tan coonhound',166: 'Walker hound, Walker foxhound',167: 'English foxhound',168: 'redbone',169: 'borzoi, Russian wolfhound',170: 'Irish wolfhound',171: 'Italian greyhound',172: 'whippet',173: 'Ibizan hound, Ibizan Podenco',174: 'Norwegian elkhound, elkhound',175: 'otterhound, otter hound',176: 'Saluki, gazelle hound',177: 'Scottish deerhound, deerhound',178: 'Weimaraner',179: 'Staffordshire bullterrier, Staffordshire bull terrier',180: 'American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier',181: 'Bedlington terrier',182: 'Border terrier',183: 'Kerry blue terrier',184: 'Irish terrier',185: 'Norfolk terrier',186: 'Norwich terrier',187: 'Yorkshire terrier',188: 'wire-haired fox terrier',189: 'Lakeland terrier',190: 'Sealyham terrier, Sealyham',191: 'Airedale, Airedale terrier',192: 'cairn, cairn terrier',193: 'Australian terrier',194: 'Dandie Dinmont, Dandie Dinmont terrier',195: 'Boston bull, Boston terrier',196: 'miniature schnauzer',197: 'giant schnauzer',198: 'standard schnauzer',199: 'Scotch terrier, Scottish terrier, Scottie',200: 'Tibetan terrier, chrysanthemum dog',201: 'silky terrier, Sydney silky',202: 'soft-coated wheaten terrier',203: 'West Highland white terrier',204: 'Lhasa, Lhasa apso',205: 'flat-coated retriever',206: 'curly-coated retriever',207: 'golden retriever',208: 'Labrador retriever',209: 'Chesapeake Bay retriever',210: 'German short-haired pointer',211: 'vizsla, Hungarian pointer',212: 'English setter',213: 'Irish setter, red setter',214: 'Gordon setter',215: 'Brittany spaniel',216: 'clumber, clumber spaniel',217: 'English springer, English springer spaniel',218: 'Welsh springer spaniel',219: 'cocker spaniel, English cocker spaniel, cocker',220: 'Sussex spaniel',221: 'Irish water spaniel',222: 'kuvasz',223: 'schipperke',224: 'groenendael',225: 'malinois',226: 'briard',227: 'kelpie',228: 'komondor',229: 'Old English sheepdog, bobtail',230: 'Shetland sheepdog, Shetland sheep dog, Shetland',231: 'collie',232: 'Border collie',233: 'Bouvier des Flandres, Bouviers des Flandres',234: 'Rottweiler',235: 'German shepherd, German shepherd dog, German police dog, alsatian',236: 'Doberman, Doberman pinscher',237: 'miniature pinscher',238: 'Greater Swiss Mountain dog',239: 'Bernese mountain dog',240: 'Appenzeller',241: 'EntleBucher',242: 'boxer',243: 'bull mastiff',244: 'Tibetan mastiff',245: 'French bulldog',246: 'Great Dane',247: 'Saint Bernard, St Bernard',248: 'Eskimo dog, husky',249: 'malamute, malemute, Alaskan malamute',250: 'Siberian husky',251: 'dalmatian, coach dog, carriage dog',252: 'affenpinscher, monkey pinscher, monkey dog',253: 'basenji',254: 'pug, pug-dog',255: 'Leonberg',256: 'Newfoundland, Newfoundland dog',257: 'Great Pyrenees',258: 'Samoyed, Samoyede',259: 'Pomeranian',260: 'chow, chow chow',261: 'keeshond',262: 'Brabancon griffon',263: 'Pembroke, Pembroke Welsh corgi',264: 'Cardigan, Cardigan Welsh corgi',265: 'toy poodle',266: 'miniature poodle',267: 'standard poodle',268: 'Mexican hairless',269: 'timber wolf, grey wolf, gray wolf, Canis lupus',270: 'white wolf, Arctic wolf, Canis lupus tundrarum',271: 'red wolf, maned wolf, Canis rufus, Canis niger',272: 'coyote, prairie wolf, brush wolf, Canis latrans',273: 'dingo, warrigal, warragal, Canis dingo',274: 'dhole, Cuon alpinus',275: 'African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus',276: 'hyena, hyaena',277: 'red fox, Vulpes vulpes',278: 'kit fox, Vulpes macrotis',279: 'Arctic fox, white fox, Alopex lagopus',280: 'grey fox, gray fox, Urocyon cinereoargenteus',281: 'tabby, tabby cat',282: 'tiger cat',283: 'Persian cat',284: 'Siamese cat, Siamese',285: 'Egyptian cat',286: 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor',287: 'lynx, catamount',288: 'leopard, Panthera pardus',289: 'snow leopard, ounce, Panthera uncia',290: 'jaguar, panther, Panthera onca, Felis onca',291: 'lion, king of beasts, Panthera leo',292: 'tiger, Panthera tigris',293: 'cheetah, chetah, Acinonyx jubatus',294: 'brown bear, bruin, Ursus arctos',295: 'American black bear, black bear, Ursus americanus, Euarctos americanus',296: 'ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus',297: 'sloth bear, Melursus ursinus, Ursus ursinus',298: 'mongoose',299: 'meerkat, mierkat',300: 'tiger beetle',301: 'ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle',302: 'ground beetle, carabid beetle',303: 'long-horned beetle, longicorn, longicorn beetle',304: 'leaf beetle, chrysomelid',305: 'dung beetle',306: 'rhinoceros beetle',307: 'weevil',308: 'fly',309: 'bee',310: 'ant, emmet, pismire',311: 'grasshopper, hopper',312: 'cricket',313: 'walking stick, walkingstick, stick insect',314: 'cockroach, roach',315: 'mantis, mantid',316: 'cicada, cicala',317: 'leafhopper',318: 'lacewing, lacewing fly',319: "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",320: 'damselfly',321: 'admiral',322: 'ringlet, ringlet butterfly',323: 'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus',324: 'cabbage butterfly',325: 'sulphur butterfly, sulfur butterfly',326: 'lycaenid, lycaenid butterfly',327: 'starfish, sea star',328: 'sea urchin',329: 'sea cucumber, holothurian',330: 'wood rabbit, cottontail, cottontail rabbit',331: 'hare',332: 'Angora, Angora rabbit',333: 'hamster',334: 'porcupine, hedgehog',335: 'fox squirrel, eastern fox squirrel, Sciurus niger',336: 'marmot',337: 'beaver',338: 'guinea pig, Cavia cobaya',339: 'sorrel',340: 'zebra',341: 'hog, pig, grunter, squealer, Sus scrofa',342: 'wild boar, boar, Sus scrofa',343: 'warthog',344: 'hippopotamus, hippo, river horse, Hippopotamus amphibius',345: 'ox',346: 'water buffalo, water ox, Asiatic buffalo, Bubalus bubalis',347: 'bison',348: 'ram, tup',349: 'bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis',350: 'ibex, Capra ibex',351: 'hartebeest',352: 'impala, Aepyceros melampus',353: 'gazelle',354: 'Arabian camel, dromedary, Camelus dromedarius',355: 'llama',356: 'weasel',357: 'mink',358: 'polecat, fitch, foulmart, foumart, Mustela putorius',359: 'black-footed ferret, ferret, Mustela nigripes',360: 'otter',361: 'skunk, polecat, wood pussy',362: 'badger',363: 'armadillo',364: 'three-toed sloth, ai, Bradypus tridactylus',365: 'orangutan, orang, orangutang, Pongo pygmaeus',366: 'gorilla, Gorilla gorilla',367: 'chimpanzee, chimp, Pan troglodytes',368: 'gibbon, Hylobates lar',369: 'siamang, Hylobates syndactylus, Symphalangus syndactylus',370: 'guenon, guenon monkey',371: 'patas, hussar monkey, Erythrocebus patas',372: 'baboon',373: 'macaque',374: 'langur',375: 'colobus, colobus monkey',376: 'proboscis monkey, Nasalis larvatus',377: 'marmoset',378: 'capuchin, ringtail, Cebus capucinus',379: 'howler monkey, howler',380: 'titi, titi monkey',381: 'spider monkey, Ateles geoffroyi',382: 'squirrel monkey, Saimiri sciureus',383: 'Madagascar cat, ring-tailed lemur, Lemur catta',384: 'indri, indris, Indri indri, Indri brevicaudatus',385: 'Indian elephant, Elephas maximus',386: 'African elephant, Loxodonta africana',387: 'lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens',388: 'giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca',389: 'barracouta, snoek',390: 'eel',391: 'coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch',392: 'rock beauty, Holocanthus tricolor',393: 'anemone fish',394: 'sturgeon',395: 'gar, garfish, garpike, billfish, Lepisosteus osseus',396: 'lionfish',397: 'puffer, pufferfish, blowfish, globefish',398: 'abacus',399: 'abaya',400: "academic gown, academic robe, judge's robe",401: 'accordion, piano accordion, squeeze box',402: 'acoustic guitar',403: 'aircraft carrier, carrier, flattop, attack aircraft carrier',404: 'airliner',405: 'airship, dirigible',406: 'altar',407: 'ambulance',408: 'amphibian, amphibious vehicle',409: 'analog clock',410: 'apiary, bee house',411: 'apron',412: 'ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin',413: 'assault rifle, assault gun',414: 'backpack, back pack, knapsack, packsack, rucksack, haversack',415: 'bakery, bakeshop, bakehouse',416: 'balance beam, beam',417: 'balloon',418: 'ballpoint, ballpoint pen, ballpen, Biro',419: 'Band Aid',420: 'banjo',421: 'bannister, banister, balustrade, balusters, handrail',422: 'barbell',423: 'barber chair',424: 'barbershop',425: 'barn',426: 'barometer',427: 'barrel, cask',428: 'barrow, garden cart, lawn cart, wheelbarrow',429: 'baseball',430: 'basketball',431: 'bassinet',432: 'bassoon',433: 'bathing cap, swimming cap',434: 'bath towel',435: 'bathtub, bathing tub, bath, tub',436: 'beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon',437: 'beacon, lighthouse, beacon light, pharos',438: 'beaker',439: 'bearskin, busby, shako',440: 'beer bottle',441: 'beer glass',442: 'bell cote, bell cot',443: 'bib',444: 'bicycle-built-for-two, tandem bicycle, tandem',445: 'bikini, two-piece',446: 'binder, ring-binder',447: 'binoculars, field glasses, opera glasses',448: 'birdhouse',449: 'boathouse',450: 'bobsled, bobsleigh, bob',451: 'bolo tie, bolo, bola tie, bola',452: 'bonnet, poke bonnet',453: 'bookcase',454: 'bookshop, bookstore, bookstall',455: 'bottlecap',456: 'bow',457: 'bow tie, bow-tie, bowtie',458: 'brass, memorial tablet, plaque',459: 'brassiere, bra, bandeau',460: 'breakwater, groin, groyne, mole, bulwark, seawall, jetty',461: 'breastplate, aegis, egis',462: 'broom',463: 'bucket, pail',464: 'buckle',465: 'bulletproof vest',466: 'bullet train, bullet',467: 'butcher shop, meat market',468: 'cab, hack, taxi, taxicab',469: 'caldron, cauldron',470: 'candle, taper, wax light',471: 'cannon',472: 'canoe',473: 'can opener, tin opener',474: 'cardigan',475: 'car mirror',476: 'carousel, carrousel, merry-go-round, roundabout, whirligig',477: "carpenter's kit, tool kit",478: 'carton',479: 'car wheel',480: 'cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM',481: 'cassette',482: 'cassette player',483: 'castle',484: 'catamaran',485: 'CD player',486: 'cello, violoncello',487: 'cellular telephone, cellular phone, cellphone, cell, mobile phone',488: 'chain',489: 'chainlink fence',490: 'chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour',491: 'chain saw, chainsaw',492: 'chest',493: 'chiffonier, commode',494: 'chime, bell, gong',495: 'china cabinet, china closet',496: 'Christmas stocking',497: 'church, church building',498: 'cinema, movie theater, movie theatre, movie house, picture palace',499: 'cleaver, meat cleaver, chopper',500: 'cliff dwelling',501: 'cloak',502: 'clog, geta, patten, sabot',503: 'cocktail shaker',504: 'coffee mug',505: 'coffeepot',506: 'coil, spiral, volute, whorl, helix',507: 'combination lock',508: 'computer keyboard, keypad',509: 'confectionery, confectionary, candy store',510: 'container ship, containership, container vessel',511: 'convertible',512: 'corkscrew, bottle screw',513: 'cornet, horn, trumpet, trump',514: 'cowboy boot',515: 'cowboy hat, ten-gallon hat',516: 'cradle',517: 'crane',518: 'crash helmet',519: 'crate',520: 'crib, cot',521: 'Crock Pot',522: 'croquet ball',523: 'crutch',524: 'cuirass',525: 'dam, dike, dyke',526: 'desk',527: 'desktop computer',528: 'dial telephone, dial phone',529: 'diaper, nappy, napkin',530: 'digital clock',531: 'digital watch',532: 'dining table, board',533: 'dishrag, dishcloth',534: 'dishwasher, dish washer, dishwashing machine',535: 'disk brake, disc brake',536: 'dock, dockage, docking facility',537: 'dogsled, dog sled, dog sleigh',538: 'dome',539: 'doormat, welcome mat',540: 'drilling platform, offshore rig',541: 'drum, membranophone, tympan',542: 'drumstick',543: 'dumbbell',544: 'Dutch oven',545: 'electric fan, blower',546: 'electric guitar',547: 'electric locomotive',548: 'entertainment center',549: 'envelope',550: 'espresso maker',551: 'face powder',552: 'feather boa, boa',553: 'file, file cabinet, filing cabinet',554: 'fireboat',555: 'fire engine, fire truck',556: 'fire screen, fireguard',557: 'flagpole, flagstaff',558: 'flute, transverse flute',559: 'folding chair',560: 'football helmet',561: 'forklift',562: 'fountain',563: 'fountain pen',564: 'four-poster',565: 'freight car',566: 'French horn, horn',567: 'frying pan, frypan, skillet',568: 'fur coat',569: 'garbage truck, dustcart',570: 'gasmask, respirator, gas helmet',571: 'gas pump, gasoline pump, petrol pump, island dispenser',572: 'goblet',573: 'go-kart',574: 'golf ball',575: 'golfcart, golf cart',576: 'gondola',577: 'gong, tam-tam',578: 'gown',579: 'grand piano, grand',580: 'greenhouse, nursery, glasshouse',581: 'grille, radiator grille',582: 'grocery store, grocery, food market, market',583: 'guillotine',584: 'hair slide',585: 'hair spray',586: 'half track',587: 'hammer',588: 'hamper',589: 'hand blower, blow dryer, blow drier, hair dryer, hair drier',590: 'hand-held computer, hand-held microcomputer',591: 'handkerchief, hankie, hanky, hankey',592: 'hard disc, hard disk, fixed disk',593: 'harmonica, mouth organ, harp, mouth harp',594: 'harp',595: 'harvester, reaper',596: 'hatchet',597: 'holster',598: 'home theater, home theatre',599: 'honeycomb',600: 'hook, claw',601: 'hoopskirt, crinoline',602: 'horizontal bar, high bar',603: 'horse cart, horse-cart',604: 'hourglass',605: 'iPod',606: 'iron, smoothing iron',607: "jack-o'-lantern",608: 'jean, blue jean, denim',609: 'jeep, landrover',610: 'jersey, T-shirt, tee shirt',611: 'jigsaw puzzle',612: 'jinrikisha, ricksha, rickshaw',613: 'joystick',614: 'kimono',615: 'knee pad',616: 'knot',617: 'lab coat, laboratory coat',618: 'ladle',619: 'lampshade, lamp shade',620: 'laptop, laptop computer',621: 'lawn mower, mower',622: 'lens cap, lens cover',623: 'letter opener, paper knife, paperknife',624: 'library',625: 'lifeboat',626: 'lighter, light, igniter, ignitor',627: 'limousine, limo',628: 'liner, ocean liner',629: 'lipstick, lip rouge',630: 'Loafer',631: 'lotion',632: 'loudspeaker, speaker, speaker unit, loudspeaker system, speaker system',633: "loupe, jeweler's loupe",634: 'lumbermill, sawmill',635: 'magnetic compass',636: 'mailbag, postbag',637: 'mailbox, letter box',638: 'maillot',639: 'maillot, tank suit',640: 'manhole cover',641: 'maraca',642: 'marimba, xylophone',643: 'mask',644: 'matchstick',645: 'maypole',646: 'maze, labyrinth',647: 'measuring cup',648: 'medicine chest, medicine cabinet',649: 'megalith, megalithic structure',650: 'microphone, mike',651: 'microwave, microwave oven',652: 'military uniform',653: 'milk can',654: 'minibus',655: 'miniskirt, mini',656: 'minivan',657: 'missile',658: 'mitten',659: 'mixing bowl',660: 'mobile home, manufactured home',661: 'Model T',662: 'modem',663: 'monastery',664: 'monitor',665: 'moped',666: 'mortar',667: 'mortarboard',668: 'mosque',669: 'mosquito net',670: 'motor scooter, scooter',671: 'mountain bike, all-terrain bike, off-roader',672: 'mountain tent',673: 'mouse, computer mouse',674: 'mousetrap',675: 'moving van',676: 'muzzle',677: 'nail',678: 'neck brace',679: 'necklace',680: 'nipple',681: 'notebook, notebook computer',682: 'obelisk',683: 'oboe, hautboy, hautbois',684: 'ocarina, sweet potato',685: 'odometer, hodometer, mileometer, milometer',686: 'oil filter',687: 'organ, pipe organ',688: 'oscilloscope, scope, cathode-ray oscilloscope, CRO',689: 'overskirt',690: 'oxcart',691: 'oxygen mask',692: 'packet',693: 'paddle, boat paddle',694: 'paddlewheel, paddle wheel',695: 'padlock',696: 'paintbrush',697: "pajama, pyjama, pj's, jammies",698: 'palace',699: 'panpipe, pandean pipe, syrinx',700: 'paper towel',701: 'parachute, chute',702: 'parallel bars, bars',703: 'park bench',704: 'parking meter',705: 'passenger car, coach, carriage',706: 'patio, terrace',707: 'pay-phone, pay-station',708: 'pedestal, plinth, footstall',709: 'pencil box, pencil case',710: 'pencil sharpener',711: 'perfume, essence',712: 'Petri dish',713: 'photocopier',714: 'pick, plectrum, plectron',715: 'pickelhaube',716: 'picket fence, paling',717: 'pickup, pickup truck',718: 'pier',719: 'piggy bank, penny bank',720: 'pill bottle',721: 'pillow',722: 'ping-pong ball',723: 'pinwheel',724: 'pirate, pirate ship',725: 'pitcher, ewer',726: "plane, carpenter's plane, woodworking plane",727: 'planetarium',728: 'plastic bag',729: 'plate rack',730: 'plow, plough',731: "plunger, plumber's helper",732: 'Polaroid camera, Polaroid Land camera',733: 'pole',734: 'police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria',735: 'poncho',736: 'pool table, billiard table, snooker table',737: 'pop bottle, soda bottle',738: 'pot, flowerpot',739: "potter's wheel",740: 'power drill',741: 'prayer rug, prayer mat',742: 'printer',743: 'prison, prison house',744: 'projectile, missile',745: 'projector',746: 'puck, hockey puck',747: 'punching bag, punch bag, punching ball, punchball',748: 'purse',749: 'quill, quill pen',750: 'quilt, comforter, comfort, puff',751: 'racer, race car, racing car',752: 'racket, racquet',753: 'radiator',754: 'radio, wireless',755: 'radio telescope, radio reflector',756: 'rain barrel',757: 'recreational vehicle, RV, R.V.',758: 'reel',759: 'reflex camera',760: 'refrigerator, icebox',761: 'remote control, remote',762: 'restaurant, eating house, eating place, eatery',763: 'revolver, six-gun, six-shooter',764: 'rifle',765: 'rocking chair, rocker',766: 'rotisserie',767: 'rubber eraser, rubber, pencil eraser',768: 'rugby ball',769: 'rule, ruler',770: 'running shoe',771: 'safe',772: 'safety pin',773: 'saltshaker, salt shaker',774: 'sandal',775: 'sarong',776: 'sax, saxophone',777: 'scabbard',778: 'scale, weighing machine',779: 'school bus',780: 'schooner',781: 'scoreboard',782: 'screen, CRT screen',783: 'screw',784: 'screwdriver',785: 'seat belt, seatbelt',786: 'sewing machine',787: 'shield, buckler',788: 'shoe shop, shoe-shop, shoe store',789: 'shoji',790: 'shopping basket',791: 'shopping cart',792: 'shovel',793: 'shower cap',794: 'shower curtain',795: 'ski',796: 'ski mask',797: 'sleeping bag',798: 'slide rule, slipstick',799: 'sliding door',800: 'slot, one-armed bandit',801: 'snorkel',802: 'snowmobile',803: 'snowplow, snowplough',804: 'soap dispenser',805: 'soccer ball',806: 'sock',807: 'solar dish, solar collector, solar furnace',808: 'sombrero',809: 'soup bowl',810: 'space bar',811: 'space heater',812: 'space shuttle',813: 'spatula',814: 'speedboat',815: "spider web, spider's web",816: 'spindle',817: 'sports car, sport car',818: 'spotlight, spot',819: 'stage',820: 'steam locomotive',821: 'steel arch bridge',822: 'steel drum',823: 'stethoscope',824: 'stole',825: 'stone wall',826: 'stopwatch, stop watch',827: 'stove',828: 'strainer',829: 'streetcar, tram, tramcar, trolley, trolley car',830: 'stretcher',831: 'studio couch, day bed',832: 'stupa, tope',833: 'submarine, pigboat, sub, U-boat',834: 'suit, suit of clothes',835: 'sundial',836: 'sunglass',837: 'sunglasses, dark glasses, shades',838: 'sunscreen, sunblock, sun blocker',839: 'suspension bridge',840: 'swab, swob, mop',841: 'sweatshirt',842: 'swimming trunks, bathing trunks',843: 'swing',844: 'switch, electric switch, electrical switch',845: 'syringe',846: 'table lamp',847: 'tank, army tank, armored combat vehicle, armoured combat vehicle',848: 'tape player',849: 'teapot',850: 'teddy, teddy bear',851: 'television, television system',852: 'tennis ball',853: 'thatch, thatched roof',854: 'theater curtain, theatre curtain',855: 'thimble',856: 'thresher, thrasher, threshing machine',857: 'throne',858: 'tile roof',859: 'toaster',860: 'tobacco shop, tobacconist shop, tobacconist',861: 'toilet seat',862: 'torch',863: 'totem pole',864: 'tow truck, tow car, wrecker',865: 'toyshop',866: 'tractor',867: 'trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi',868: 'tray',869: 'trench coat',870: 'tricycle, trike, velocipede',871: 'trimaran',872: 'tripod',873: 'triumphal arch',874: 'trolleybus, trolley coach, trackless trolley',875: 'trombone',876: 'tub, vat',877: 'turnstile',878: 'typewriter keyboard',879: 'umbrella',880: 'unicycle, monocycle',881: 'upright, upright piano',882: 'vacuum, vacuum cleaner',883: 'vase',884: 'vault',885: 'velvet',886: 'vending machine',887: 'vestment',888: 'viaduct',889: 'violin, fiddle',890: 'volleyball',891: 'waffle iron',892: 'wall clock',893: 'wallet, billfold, notecase, pocketbook',894: 'wardrobe, closet, press',895: 'warplane, military plane',896: 'washbasin, handbasin, washbowl, lavabo, wash-hand basin',897: 'washer, automatic washer, washing machine',898: 'water bottle',899: 'water jug',900: 'water tower',901: 'whiskey jug',902: 'whistle',903: 'wig',904: 'window screen',905: 'window shade',906: 'Windsor tie',907: 'wine bottle',908: 'wing',909: 'wok',910: 'wooden spoon',911: 'wool, woolen, woollen',912: 'worm fence, snake fence, snake-rail fence, Virginia fence',913: 'wreck',914: 'yawl',915: 'yurt',916: 'web site, website, internet site, site',917: 'comic book',918: 'crossword puzzle, crossword',919: 'street sign',920: 'traffic light, traffic signal, stoplight',921: 'book jacket, dust cover, dust jacket, dust wrapper',922: 'menu',923: 'plate',924: 'guacamole',925: 'consomme',926: 'hot pot, hotpot',927: 'trifle',928: 'ice cream, icecream',929: 'ice lolly, lolly, lollipop, popsicle',930: 'French loaf',931: 'bagel, beigel',932: 'pretzel',933: 'cheeseburger',934: 'hotdog, hot dog, red hot',935: 'mashed potato',936: 'head cabbage',937: 'broccoli',938: 'cauliflower',939: 'zucchini, courgette',940: 'spaghetti squash',941: 'acorn squash',942: 'butternut squash',943: 'cucumber, cuke',944: 'artichoke, globe artichoke',945: 'bell pepper',946: 'cardoon',947: 'mushroom',948: 'Granny Smith',949: 'strawberry',950: 'orange',951: 'lemon',952: 'fig',953: 'pineapple, ananas',954: 'banana',955: 'jackfruit, jak, jack',956: 'custard apple',957: 'pomegranate',958: 'hay',959: 'carbonara',960: 'chocolate sauce, chocolate syrup',961: 'dough',962: 'meat loaf, meatloaf',963: 'pizza, pizza pie',964: 'potpie',965: 'burrito',966: 'red wine',967: 'espresso',968: 'cup',969: 'eggnog',970: 'alp',971: 'bubble',972: 'cliff, drop, drop-off',973: 'coral reef',974: 'geyser',975: 'lakeside, lakeshore',976: 'promontory, headland, head, foreland',977: 'sandbar, sand bar',978: 'seashore, coast, seacoast, sea-coast',979: 'valley, vale',980: 'volcano',981: 'ballplayer, baseball player',982: 'groom, bridegroom',983: 'scuba diver',984: 'rapeseed',985: 'daisy',986: "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",987: 'corn',988: 'acorn',989: 'hip, rose hip, rosehip',990: 'buckeye, horse chestnut, conker',991: 'coral fungus',992: 'agaric',993: 'gyromitra',994: 'stinkhorn, carrion fungus',995: 'earthstar',996: 'hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa',997: 'bolete',998: 'ear, spike, capitulum',999: 'toilet tissue, toilet paper, bathroom tissue'}

运行结果

1.LeNet5

basenet: lenet5 (image size: 32 * 32 * 3)dataset: cifarlen(dataset): 50000, iter_size: 1562 batch_size: 32optim: SGDscheduler: MultiStepLRmilestones: [15, 20, 30]weight_decay: 1e-4gamma: 0.1momentum: 0.9lr: 0.01epoch: 30

共计

2.AlexNet

basenet: alexnet (image size: 224 * 224 * 3)dataset: cifarlen(dataset): 50000, iter_size: 1562 batch_size: 32optim: SGDscheduler: MultiStepLRmilestones: [15, 20, 30]weight_decay: 1e-4gamma: 0.1momentum: 0.9lr:0.01epoch: 30

共计

3.VGG

basenet: vgg16 (image size: 224 * 224 * 3)dataset: cifarlen(dataset): 50000, iter_size: 1562 batch_size: 32optim: SGDscheduler: MultiStepLRmilestones: [15, 20, 30]weight_decay: 1e-4gamma: 0.1momentum: 0.9lr:0.01epoch: 30

共计

4.ResNet

basenet: resnet18dataset: ImageNetimage size: 224 * 224 * 3 (可自定义)batch_size: 32optim: SGDscheduler: MultiStepLRmilestones: [15, 20, 30]weight_decay: 1e-4gamma: 0.1momentum: 0.9lr:0.001epoch: 30

下一话

CV+Deep Learning——网络架构Pytorch复现系列——classification(二：ResNeXt，GoogLeNet，MobileNet)/XiaoyYidiaodiao/article/details/125692368?csdn_share_tail=%7B%22type%22%3A%22blog%22%2C%22rType%22%3A%22article%22%2C%22rId%22%3A%22125692368%22%2C%22source%22%3A%22XiaoyYidiaodiao%22%7D&ctrtid=yBcgN

参考文献

[1] LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.

[2] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks[J]. Advances in neural information processing systems, , 25.

[3] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv:1409.1556, .

[4] He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. : 770-778.