Welcome to the Model-Centric Track of the Wake Vision Challenge! 🎉
This track challenges you to push the boundaries of tiny computer vision by designing innovative model architectures for the newly released Wake Vision Dataset.
🔗 Learn More: Wake Vision Challenge Details
Participants are invited to:
- Design novel model architectures to achieve high accuracy.
- Optimize for resource efficiency (e.g., memory, inference time).
- Evaluate models on the public test set of the Wake Vision dataset.
You can modify the model architecture freely, but the dataset must remain unchanged. 🛠️
First, install Docker on your machine:
Run the following command inside the directory where you cloned this repository:
sudo docker run -it --rm -v $PWD:/tmp -w /tmp andregara/wake_vision_challenge:cpu python model_centric_track.py- This trains the ColabNAS model, a state-of-the-art person detection model, on the Wake Vision dataset.
- Modify the
model_centric_track.pyscript to propose your own architecture.
💡 Note: The first execution may take several hours as it downloads the full dataset (~365 GB).
- Install the NVIDIA Container Toolkit.
- Verify your GPU drivers.
Run the following command inside the directory where you cloned this repository:
sudo docker run --gpus all -it --rm -v $PWD:/tmp -w /tmp andregara/wake_vision_challenge:gpu python model_centric_track.py- This trains the ColabNAS model on the Wake Vision dataset.
- Modify the
model_centric_track.pyscript to design your own model architecture.
💡 Note: The first execution may take several hours as it downloads the full dataset (~365 GB).
- Focus on Model Innovation: Experiment with architecture design, layer configurations, and optimization techniques.
- Stay Efficient: Resource usage is critical—consider model size, inference time, and memory usage.
- Collaborate: Join the community discussions on Discord to exchange ideas and insights!
Have questions or need help? Reach out on Discord.
🌟 Happy Innovating and Good Luck! 🌟