'-e', metavar='E', type=int, default=300, help='Number of epochs') parser.add_argument...
The number of parameters and number of multiply-adds can be modified by using the `alpha` parameter, which increases/decreases the number of filters in each layer. By altering the image size and `alpha` parameter, all 22 models from the paper can be built, with ImageNet weights provided. ...
- multiplier: Float multiplier for the depth (number of channels) for all convolution ops. The value must be greater than zero. Typical usage will be to set this value in (0, 1) to reduce the number of parameters or computation cost of the model. - output_stride: will ensure that the...
The number of parameters and number of multiply-adds can be modified by using the `alpha` parameter, which increases/decreases the number of filters in each layer. By altering the image size and `alpha` parameter, all 22 models from the paper can be built, with ImageNet weights provide...
On the other hand, MobileNetV2 follows a narrow->wide->narrow approach. The first step widens the network using a 1x1 convolution because the following 3x3 depthwise convolution already greatly reduces the number of parameters. Afterwards another 1x1 convolution squeezes the network in order to matc...
不管如何,其目标在保持模型性能(accuracy)的前提下降低模型大小(parameters size),同时提升模型速度(speed, low latency)。本文的主角MobileNet属于后者,其是Google最近提出的一种小巧而高效的CNN模型,其在accuracy和latency之间做了折中。 MobileNet 需要尽可能维持其中发展较快的计算机视觉和深度学习领域与移动环境局限性之...
parameters(), lr=learning_rate) # 交叉熵损失函数 loss_fn = torch.nn.CrossEntropyLoss() def evaluate_accuracy(data_iter,model): ''' 模型预测精度 ''' total = 0 correct = 0 with torch.no_grad(): model.eval() for images,labels in data_iter: images = images.to(device) labels = ...
Tradeoff hyper parameters—超参数的权衡 MobileNet v2同样使用MobileNet V1中的两个超参数,宽度系数α和分辨率系数ρ; 与MobileNet v1的不同是,对于小于1的乘数,本文将宽度系数α应用于除最后一个卷积层之外的所有层。这提高了较小模型的性能。 五、Implementation Notes—实现细节说明 ...
Static quantizationquantizes the weights and activations of the model. It fuses activations into preceding layers where possible. Itrequires calibration with a representative dataset to determine optimal quantization parameters for activations.
【摘要】 基于MobileNetv2的垃圾分类 MobileNetv2模型原理介绍MobileNet网络是由Google团队于2017年提出的专注于移动端、嵌入式或IoT设备的轻量级CNN网络,相比于传统的卷积神经网络,MobileNet网络使用深度可分离卷积(Depthwise Separable Convolution)的思想在准确率小幅度降低的前提下,大大减小了模型参数与运算量。并引入宽度系...