How to change darknet53 network to fasternet network
jackiezhang11 opened this issue ยท 4 comments
Search before asking
- I have searched the YOLOv3 issues and discussions and found no similar questions.
Question
How to do that network is changed to Faternet in Yolov3? Is it having improved in accurate and acclerate?
Additional
No response
๐ Hello @jackiezhang11, thank you for your interest in YOLOv3 ๐! Please visit our โญ๏ธ 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 ๐ Bug Report, please provide a minimum reproducible example to help us debug it.
If this is a custom training โ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results.
Requirements
Python>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started:
git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # install
Environments
YOLOv3 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
- Notebooks with free GPU:
- Google Cloud Deep Learning VM. See GCP Quickstart Guide
- Amazon Deep Learning AMI. See AWS Quickstart Guide
- Docker Image. See Docker Quickstart Guide
Status
If this badge is green, all YOLOv3 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv3 training, validation, inference, export and benchmarks on MacOS, Windows, and Ubuntu every 24 hours and on every commit.
Introducing YOLOv8 ๐
We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - YOLOv8 ๐!
Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.
Check out our YOLOv8 Docs for details and get started with:
pip install ultralytics
๐ Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.
Access additional YOLOv3 ๐ resources:
- Wiki โ https://github.com/ultralytics/yolov5/wiki
- Tutorials โ https://github.com/ultralytics/yolov5#tutorials
- Docs โ https://docs.ultralytics.com
Access additional Ultralytics โก resources:
- Ultralytics HUB โ https://ultralytics.com/hub
- Vision API โ https://ultralytics.com/yolov5
- About Us โ https://ultralytics.com/about
- Join Our Team โ https://ultralytics.com/work
- Contact Us โ https://ultralytics.com/contact
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 YOLOv3 ๐ and Vision AI โญ!
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
@jackiezhang11 you can replace the Darknet-53 backbone with the Faternet by modifying the cfg
file in the YOLOv3 repository. However, it's important to note that this change may or may not result in improved accuracy and speed, as the performance can vary based on the specific use case and dataset. Always thoroughly evaluate any changes to the network architecture to ensure optimal performance.