Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

QTNet - image resolution #5

Open
BehdadSDP opened this issue Nov 14, 2023 · 3 comments
Open

QTNet - image resolution #5

BehdadSDP opened this issue Nov 14, 2023 · 3 comments
Labels
question Further information is requested

Comments

@BehdadSDP
Copy link

Hi, I'm trying to train the QTNet, and I noticed that when I change the format of the training pictures, the quality and resolution decrease. Is this normal?

@BehdadSDP BehdadSDP added the question Further information is requested label Nov 14, 2023
Copy link

👋 Hello @BehdadSDP, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

If this is a 🐛 Bug Report, please provide screenshots and minimum viable code to reproduce your issue, otherwise we can not help you.

If this is a custom training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online W&B logging if available.

For business inquiries or professional support requests please visit https://ultralytics.com or email Glenn Jocher at [email protected].

Requirements

Python>=3.6.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

$ git clone https://github.com/ultralytics/yolov5
$ cd yolov5
$ pip install -r requirements.txt

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

Status

CI CPU testing

If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), validation (val.py), inference (detect.py) and export (export.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.

@2029686068
Copy link

您好,请问数据集下载的多大的,我看里面好多数据集,还有那个vgg16的预训练权重是干什么的

@qinhongda8
Copy link
Owner

Hi, I'm trying to train the QTNet, and I noticed that when I change the format of the training pictures, the quality and resolution decrease. Is this normal?

That's normal. In YOLO training, we've set the image resize for training to 416. To reduce computational resources, we pre-resize the images to 416 in QTNet. This won't impact the subsequent detector training. You can also configure QTNet to not resize the images and reduce resolution if needed.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
question Further information is requested
Projects
None yet
Development

No branches or pull requests

3 participants