AmigoCDT / Salient-Net

SalientNet for cifar classification

Salient-Net, SENet 以及 ResNet 在 cifar10 和 cifar100 效果比较

Salient-Block结构示意图及实现

示意图：

将Salient结构应用于ResNet中：

Implement(Pytorch)：

class SalientBlock(nn.Module):
    def __init__(self, in_planes):
        super(SalientBlock, self).__init__()
        self.globalAvgPool = nn.AdaptiveAvgPool2d(1)
        self.bn = nn.BatchNorm2d(1)
        self.sigmoid = nn.Sigmoid()
    
    def forward(self, x):
        size_n, _, size_h, size_w = list(x.size())
        w = self.globalAvgPool(x)
        w = torch.mean(w*x, 1).view((size_n, 1, size_h, size_w))
        spatial_w = self.sigmoid(self.bn(w))
        out = spatial_w * x
        return out

Image Classification on CIFAR-10/100

We first conduct experiments on small datasets: CIFAR-10 and CIFAR-100. CIFAR-10 has 10 different classes, 6000 images per class, total about 50000 images as training data, 10000 images used for testing. 100 classes in CIFAR-100 dataset, 500 training images and 100 testing images per class. We train ResNet-50, SE-ResNet-50 and Salient-ResNet-50 on CIFAR-10 and CIFAR-100, report the top-1 and top-5 accuracy on the testing set.

Table.1 CIFAR-10

Networks	CIFAR10 Top-1 Acc	Parameters (M)	GFLOPs
ResNet50	94.38%	25.6	3.86
SE-ResNet50(ratio=16)	94.83%	28.1	3.87
Salient-ResNet50	94.91%	25.6	3.87

Table.2 CIFAR-100

Networks	CIFAR100 Top-1 Acc	Parameters (M)	GFLOPs
ResNet50	77.26%	25.6	3.86
SE-ResNet50(ratio=16)	77.13%	28.1	3.87
Salient-ResNet50	78.35%	25.6	3.87

Image Classification on Imagenet-1K

In this experiment, we train networks on large dataset - ImageNet-2012-1K, this dataset comprise 1000 classes, 1300 images for training in each class, total 1.28 million training images and 50K images for validation. We report top-1 and top-5 accuracy on validation set.

Table.3

Networks	Top-1 Acc	Top-5 Acc	Parameters (M)	GFLOPs
ResNet50	75.24%	92.36%	25.6	3.86
SE-ResNet50(ratio=16)	76.75%	93.41%	28.1	3.87
Salient-ResNet50	76.61%	93.29%	25.6	3.87

Salient-module in mobilenet

Networks	Top-1 Acc	Top-5 Acc	Parameters (M)	GFLOPs
mobilenet-1.0	70.6%	-	3.4	0.569
SE-mobilenet-1.0(ratio=16)	73.6%	91.6%	3.7	0.572
Salient-mobilenet-1.0	73.3%	91.4%	3.4	0.573