Comments (4)
@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. Hereโs a high-level approach to get you started:
- Define the ASPP Module: Create a new class for the ASPP module in the
models/common.py
file. - 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.
Hereโs 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. Itโs 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. ๐
from yolov5.
when i run โyolo.pyโ:
from n params module arguments
0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2]
1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
2 -1 1 18816 models.common.C3 [64, 64, 1]
3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
4 -1 2 115712 models.common.C3 [128, 128, 2]
5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
6 -1 3 625152 models.common.C3 [256, 256, 3]
7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
8 -1 1 1182720 models.common.C3 [512, 512, 1]
Traceback (most recent call last):
File "D:/YOLO/yolov5-7.0/models/yolo.py", line 386, in
model = Model(opt.cfg).to(device)
File "D:/YOLO/yolov5-7.0/models/yolo.py", line 185, in init
self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist
File "D:/YOLO/yolov5-7.0/models/yolo.py", line 354, in parse_model
m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module
TypeError: init() takes 3 positional arguments but 4 were given
from yolov5.
Hello @zhangsanliisi,
Thank you for reaching out and providing detailed information about the issue you're encountering. It looks like there's a TypeError
related to the __init__
method in your model definition.
To assist you better, could you please provide a minimum reproducible example of your code? This will help us understand the context and reproduce the issue on our end. You can refer to our minimum reproducible example guide for more details on how to create one. Reproducing the issue is crucial for us to investigate and provide a solution effectively.
Additionally, please ensure that you are using the latest versions of torch
and the YOLOv5 repository. Sometimes, issues can be resolved by updating to the latest versions. You can update YOLOv5 by running the following commands:
git pull
pip install -U -r requirements.txt
If the issue persists after updating, please share the reproducible code example, and we will look into it promptly.
Thank you for your cooperation and understanding. We look forward to resolving this issue for you! ๐
from yolov5.
๐ Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.
For additional resources and information, please see the links below:
- Docs: https://docs.ultralytics.com
- HUB: https://hub.ultralytics.com
- Community: https://community.ultralytics.com
Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!
Thank you for your contributions to YOLO ๐ and Vision AI โญ
from yolov5.
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