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ssd檢測不到怎么辦 ssd檢測

云知道ssd檢測


ssd檢測不到怎么辦 ssd檢測


目標檢測由兩個獨立的任務組成，即分類和定位 。R-CNN 系列目標檢測器由兩個階段組成，分別是區(qū)域提議網(wǎng)絡和分類和框細化頭 。然而，這種2階段的檢測模型已經(jīng)基本被單階段的模型替代了 。在本文中，我想介紹 Single Shot MultiBox Detector (SSD) 。
邊界框回歸
與 Faster R-CNN 一樣，作者回歸到默認邊界框 (d) 的中心 (cx, cy) 及其寬度 (w) 和高度 (h) 的偏移量 。因此，公式如下所示：
架構(gòu)
上圖展示了基于 VGG-16 作為主干的架構(gòu) 。我將通過將其分解為 3 個部分來解釋該架構(gòu)：主干、輔助卷積和預測卷積 。為了您的方便，我還將提供一些代碼 。
基礎網(wǎng)絡class VGGBase(nn.Module):
"""
VGG base convolutions to produce lower-level feature maps.
"""
def __init__(self):
super(VGGBase, self).__init__()
# Standard convolutional layers in VGG16
self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, padding=1) # stride = 1, by default
self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=True) # ceiling (not floor) here for even dims
self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
self.pool5 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1) # retains size because stride is 1 (and padding)
# Replacements for FC6 and FC7 in VGG16
self.conv6 = nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6) # atrous convolution
self.conv7 = nn.Conv2d(1024, 1024, kernel_size=1)
# Load pretrained layers
self.load_pretrained_layers()
def forward(self, image):
"""
Forward propagation.
:param image: images, a tensor of dimensions (N, 3, 300, 300)
:return: lower-level feature maps conv4_3 and conv7
"""
out = F.relu(self.conv1_1(image)) # (N, 64, 300, 300)
out = F.relu(self.conv1_2(out)) # (N, 64, 300, 300)
out = self.pool1(out) # (N, 64, 150, 150)
out = F.relu(self.conv2_1(out)) # (N, 128, 150, 150)
out = F.relu(self.conv2_2(out)) # (N, 128, 150, 150)
out = self.pool2(out) # (N, 128, 75, 75)
out = F.relu(self.conv3_1(out)) # (N, 256, 75, 75)
out = F.relu(self.conv3_2(out)) # (N, 256, 75, 75)
out = F.relu(self.conv3_3(out)) # (N, 256, 75, 75)
out = self.pool3(out) # (N, 256, 38, 38), it would have been 37 if not for ceil_mode = True
out = F.relu(self.conv4_1(out)) # (N, 512, 38, 38)
out = F.relu(self.conv4_2(out)) # (N, 512, 38, 38)
out = F.relu(self.conv4_3(out)) # (N, 512, 38, 38)
conv4_3_feats = out # (N, 512, 38, 38)
out = self.pool4(out) # (N, 512, 19, 19)
out = F.relu(self.conv5_1(out)) # (N, 512, 19, 19)
out = F.relu(self.conv5_2(out)) # (N, 512, 19, 19)
out = F.relu(self.conv5_3(out)) # (N, 512, 19, 19)
out = self.pool5(out) # (N, 512, 19, 19), pool5 does not reduce dimensions
out = F.relu(self.conv6(out)) # (N, 1024, 19, 19)

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