the number of multiply-adds and thereby reduce inference cost on mobile devices. MobileNets support any input size greater than 32 x 32, with larger image sizes offering better performance. The number of parameters and number of multiply-adds can be modified by using the `alpha` parameter, whic...
If you want to change hyper-parameters, you can check "python main.py --help" Options: --dataset-mode(str) - which dataset you use, (example: CIFAR10, CIFAR100), (default: CIFAR100). --epochs(int) - number of epochs, (default: 100). ...
基于SSD-MobilenetV3模型的车辆检测
The CBAM-TealeafNet model increased the accuracy by 2.15%, whereas, the number of parameters decreased by 25.12%, compared with the NobiNetV3. Misidentification images and confusion matrix indicated that the CBAM-TealeafNet shared the better performance to highly distin...
# Parametersnc: 20 # number of classesdepth_multiple: 1.0 # model depth multiplewidth_multiple: 1.0 # layer channel multipleanchors:- [10,13, 16,30, 33,23] # P3/8- [30,61, 62,45, 59,119] # P4/16- [116,90, 156,198, 373,326] # P5/32# YOLOv5 v6.0 backbonebackbone:[[-1...
add_argument('--epochs', '-e', metavar='E', type=int, default=300, help='Number of ...
We compared the flops of different models with different parameter settings and found that the number of parameters and flops are strongly correlated. As expected, as the number of parameters increases, so does the flops count. However, there is a trade-off between parameter count and flops, ...
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 = ...
ducing the number of parameters. More recent works shifts the focus from reducing parameters to reducing the number of operations (MAdds) and the actual mea- sured latency. MobileNetV1[17] employs depthwise sepa- rable convolution to substantially improve computation ef- ...
# Learning parameters #checkpoint=None checkpoint = 'weights/MobilenetV3_Large-ssd300.pth.tar' #这个是导入预训练权值。 batch_size = 16 # batch size # iterations = 120000 # number of iterations to train 120000 workers = 8 #导入数据的进程数。进程数越多,导入得更快。