num_classes=1000): super(AlexNet, self).__init__() self.features = nn.Sequential( nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size
[resnet,alexnet,vgg,squeezenet,densenet,inception] 其他输入如下:num_classes为数据集的类别数,batch_size是训练的 batch 大小,可以根据您机器的计算能力进行调整,num_epochsis是 我们想要运行的训练 epoch 数,feature_extractis是定义我们选择微调还是特征提取的布尔值。如果feature_extract = False, 将微调模型,并...
import torch import torch.nn as nn # 定义AlexNet模型 class AlexNet(nn.Module): def __init__(self, num_classes=1000): super(AlexNet, self).__init__() # 第一层卷积:卷积核大小11x11,输入通道3,输出通道96,步幅4,填充值2 self.features = nn.Sequential( # 输入:[N, 3, 224, 224] nn....
imagenet_classes_chinese ImageNet 1000 中文数据集(适用于 TensorFlow.js),点此查看。 Tip 部分为机器翻译,可能存在个别不准确的翻译; 原文链接:https://blog.csdn.net/LegenDavid/article/details/73335578; Releases No releases published Packages No packages published...
Darknet displays information as it loads the config file and weights, then it classifies the image and prints the top-10 classes for the image. Kelp is a mixed breed dog but she has a lot of malamute in her so we’ll consider this a success!
speed if available if torch.cuda.is_available(): input_batch = input_batch.to('cuda') model.to('cuda') with torch.no_grad(): output = model(input_batch) # Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes print(output[0]) # The output has unnormalized ...
We trained a large, deep convolutional neural network to classify the1.2 million high-resolution images in the ImageNet LSVRC-2010 contestinto the 1000 different classes. On the test data, we achieved top-1 and top5 error rates of 37.5% and 17.0% which is considerably better than theprevious...
Category labels (1000 classes) for the input image. Instructions to Use Pretrained Models with TAO To use these models as pretrained weights for transfer learning, please use the snippet below as a template for themodelandtraincomponent of the experiment spec file to train a GCViT Classification...
# move the input and model toGPUforspeedifavailableiftorch.cuda.is_available():input_batch=input_batch.to('cuda')model.to('cuda')withtorch.no_grad():output=model(input_batch)# Tensorofshape1000,withconfidence scores over Imagenet's1000classesprint(output[0])# The output has unnormalized scor...
Imagenet是一个超过1500万张图像的机器学习数据集,属于22,000个类别。从2010年开始,作为Pascal视觉对象挑战的一部分,举办了名为ImageNet大规模视觉识别挑战(ILSVRC)的年度竞赛。ILSVRC使用ImageNet的一个子集,在1000个类别中分别拥有大约1000个图像。总共有大约120万个训练图像,50,000个验证图像和150,000个测试图像。