DISCLAIMER: Please use the provided test.py or model_centric_track.py script for model evaluation - the output of the model is linear uint8 and has to be converted properly
The full implementation of the method for finding the proposed model architecture can be found in uNAS Elios Repository. In this forked repository you will find:
- The
.tflitefile corresponding to the submitted model (quantized) - The
.h5file corresponding to the model found by uNAS, before quantization train.py- Script used to resume the training phase of the found modeltest.py- Slightly modified version of the original evaluation file to accomodate linear uint8 output of our model- A brief tecnical report (
wv_report.pdf)
For any question or issue, please send an email at my institutional address
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! 🌟