glenn-jocher
commented
3 months ago @zhangsanliisi hello!
Thank you for your question and for checking the existing issues and discussions before posting. Yes, you can integrate an Atrous Spatial Pyramid Pooling (ASPP) module into YOLOv5. The ASPP module is often used to capture multi-scale information, which can be beneficial for object detection tasks.
To add an ASPP module to YOLOv5, you would need to modify the model architecture. Here’s a high-level approach to get you started:
- Define the ASPP Module: Create a new class for the ASPP module in the
models/common.pyfile. - Integrate ASPP into YOLOv5: Modify the
models/yolov5s.yaml(or the specific model configuration file you are using) to include the ASPP module in the appropriate place.
Here’s a simple example of how you might define an ASPP module:
import torch
import torch.nn as nn
class ASPP(nn.Module):
def __init__(self, in_channels, out_channels):
super(ASPP, self).__init__()
self.aspp1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1)
self.aspp2 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=6, dilation=6)
self.aspp3 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=12, dilation=12)
self.aspp4 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=18, dilation=18)
self.global_avg_pool = nn.AdaptiveAvgPool2d((1, 1))
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1)
self.conv2 = nn.Conv2d(out_channels * 5, out_channels, kernel_size=1, stride=1)
self.relu = nn.ReLU()
def forward(self, x):
x1 = self.aspp1(x)
x2 = self.aspp2(x)
x3 = self.aspp3(x)
x4 = self.aspp4(x)
x5 = self.global_avg_pool(x)
x5 = self.conv1(x5)
x5 = nn.Upsample((x.shape[2], x.shape[3]), mode='bilinear', align_corners=True)(x5)
x = torch.cat((x1, x2, x3, x4, x5), dim=1)
x = self.conv2(x)
return self.relu(x)After defining the ASPP module, you can integrate it into the YOLOv5 model by modifying the configuration file and the model definition.
Regarding the effectiveness, the impact of adding an ASPP module can vary depending on the specific use case and dataset. It’s recommended to experiment with and without the ASPP module and compare the results to see if it improves performance for your particular application.
If you encounter any issues during the integration, please provide a minimum reproducible example so we can assist you better. You can refer to our minimum reproducible example guide for more details.
Also, ensure you are using the latest versions of torch and YOLOv5 to avoid any compatibility issues.
Best of luck with your implementation! If you have any further questions, feel free to ask. 😊
zhangsanliisi
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Question
ASPP 模块可以添加到yolov5 7.0 里边吗，效果怎么样？
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