https://github.com/ultralytics/xview-docker
xView 2018 Object Detection Challenge: Docker container submission code
https://github.com/ultralytics/xview-docker
ai challenge docker ml ultralytics xview yolov3
Last synced: 5 months ago
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xView 2018 Object Detection Challenge: Docker container submission code
- Host: GitHub
- URL: https://github.com/ultralytics/xview-docker
- Owner: ultralytics
- License: agpl-3.0
- Created: 2018-07-20T01:59:13.000Z (about 7 years ago)
- Default Branch: main
- Last Pushed: 2025-03-28T01:35:06.000Z (6 months ago)
- Last Synced: 2025-03-28T02:30:51.445Z (6 months ago)
- Topics: ai, challenge, docker, ml, ultralytics, xview, yolov3
- Language: Python
- Homepage: https://challenge.xviewdataset.org/
- Size: 30.9 MB
- Stars: 8
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Introduction :wave:
Welcome to the Ultralytics software directory! Our codebase is open-source and đ **distributed under the AGPL-3.0 license**. Explore more about Ultralytics and our cutting-edge projects at [our website](http://www.ultralytics.com).
[](https://github.com/ultralytics/xview-docker/actions/workflows/format.yml) 
# Project Overview :page_facing_up:
This repository, located at https://github.com/ultralytics/xview-docker, is designed to help you dockerize and deploy machine learning models for the xView detection challenge đ. It should be used in combination with the trained models from our [xView YOLOv3 repository](https://github.com/ultralytics/xview-yolov3).
# Prerequisites :bookmark_tabs:
To get started, you need Docker and Python 3.6 (or newer). Ensure that you have all the required packages by installing them via pip:
```bash
pip3 install -U -r requirements.txt
```
You should have the following packages:
- `numpy` for numerical operations,
- `scipy` for scientific computations,
- `torch` as the deep learning framework, and
- `opencv-python` for computer vision tasks.
# Dockerization Steps :whale:
Below are the steps to containerize this repository for the xView challenge using `run.sh`.
## Step 1: Copy Best Model Checkpoint
Get the best performing checkpoint from our [xView YOLOv3 model](https://github.com/ultralytics/xview-yolov3):
```bash
mkdir xview-docker/checkpoints
cp xview-yolov3/checkpoints/best.pt xview-docker/checkpoints
```
## Step 2: Build and Tag The Docker Container
Here's how to remove older images, set the right permissions for the script, build the image, and tag it appropriately (e.g., `ultralytics/xview:v30`):
```bash
sudo docker image prune -a
cd xview-docker && chmod +x run.sh
sudo docker build -t friendlyhello .
sudo docker tag friendlyhello ultralytics/xview:v30
```
## Step 3: Run Container on Example Image
Test the container with the example image while specifying CPU and memory usage:
```bash
time sudo docker run -it --memory=8g --cpus=1 ultralytics/xview:v30 bash -c './run.sh /1047.tif /tmp && cat /tmp/1047.tif.txt'
```
Here's a sample output:
## Step 4: Push to Docker Hub
Finally, upload the container to Docker Hub ready for challenge submission:
```bash
sudo docker push ultralytics/xview:v30
```
You can find the container now hosted at [Docker Hub](https://hub.docker.com/r/ultralytics/xview/), prepared for the challenge đ.
# đ¤ Contribute
We welcome contributions from the community! Whether you're fixing bugs, adding new features, or improving documentation, your input is invaluable. Take a look at our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) to get started. Also, we'd love to hear about your experience with Ultralytics products. Please consider filling out our [Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey). A huge đ and thank you to all of our contributors!
# Šī¸ License
Ultralytics is excited to offer two different licensing options to meet your needs:
- **AGPL-3.0 License**: Perfect for students and hobbyists, this [OSI-approved](https://opensource.org/license) open-source license encourages collaborative learning and knowledge sharing. Please refer to the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for detailed terms.
- **Enterprise License**: Ideal for commercial use, this license allows for the integration of Ultralytics software and AI models into commercial products without the open-source requirements of AGPL-3.0. For use cases that involve commercial applications, please contact us via [Ultralytics Licensing](https://www.ultralytics.com/license).
# đŦ Contact Us
For bug reports, feature requests, and contributions, head to [GitHub Issues](https://github.com/ultralytics/xview-docker/issues). For questions and discussions about this project and other Ultralytics endeavors, join us on [Discord](https://discord.com/invite/ultralytics)!
