ImageNet Classification You can use Darknet to classify images for the 1000-classImageNet challenge. If you haven't installed Darknet yet, you shoulddo that first. Classifying With Pre-Trained Models Here are the commands to install Darknet, download a classification weights file, and run a c...
NUM_CLASSES = 1000 NUM_TEST_IMAGES = 50 * NUM_CLASSES 1. 2. 3. 4. ImageNet数据集中包含1000个类别图像,因此,NUM_CLASSES等于1000。为了得到我们的test数据集,我们需要从train数据集中提取一些子集作为test数据集。我们将NUM_TEST_IMAGES设置为50 * 1000 = 50000张图像。 接下来,我们定义保存TFRecord文件...
class AlexNet(nn.Module): def __init__(self, num_classes=1000, init_weights=False): 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=3, stride=2), nn.Conv...
我们的神经网络架构具有6000万个参数。尽管ILSVRC的1000个类别使每个训练样本从图像到标签的映射上强加了10位约束,但事实证明这不足以学习这么多的参数而没有相当大的过拟合。下面,我们描述了克服过拟合的两种主要方法。 4.1 数据增强 减少图像数据过拟合最简单最常见的方法是使用标签保留变换来人工扩大...
maps inputs to class labels1. However, it takes many more bits to specify theinput image, so each example provides far more constraint on the parameters.输入数据的维度很复杂,其包含的信息很多,而输出结果,即分类标签是很简单的语句,如此结构的样本可以训练包含更多的参数的模型而不产生过拟合。 4.1 ...
print'prediction shape:', prediction[0].shape plt.plot(prediction[0]) print'predicted class:', prediction[0].argmax() prediction shape: (1000) predicted class: 281 结果为1000维,因为imageNet有1000类
识别图片1000种分类。 支持分类如下: tench, Tinca tinca goldfish, Carassius auratus great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias tiger shark, Galeocerdo cuvieri hammerhead, hammerhead shark electric ray, crampfish, numbfish, torpedo ...
CNN. As depicted in Figure 2, the net contains eight layers with weights; the first five are convolutional and the remaining three are fully-connected. The output of the last fully-connected layer is fed to a 1000-way softmax which produces a distribution over the 1000 class ...
'Shepherds Purse': 9, 'Small-flowered Cranesbill': 10, 'Sugar beet': 11} class Seedling...
The output of the last fully-connected layer is fed to a 1000-way softmax which produces a distribution over the 1000 class labels. Our network maximizes the multinomial logistic regression objective, which is equivalent to maximizing the average across training cases of the log-probability of ...