This repository contains documentation and code for the project READY: REal-time Ai Diagnosis for nYstagmus.
- 📄 Docs Getting started, debugging, testing, demos.
- 💾 Data: openEDS; mobious; novel
- 💻 Requirements holoscan-sdk, CUDA; apis; apis_webrtc_ready
- 🧠 Models developed in RTXA2000-8GB and trained in A100-80GB;
- 🤗 Models in HuggingFace here.
CONTRIBUTING is a good starting point for setting up the GitHub repository, managing dependencies, and guiding you through the development installation and debugging process.
uv venv --python 3.12
source .venv/bin/activate
uv pip install -e ".[test,learning,model_optimisation]"
uv pip list --verbose
uv run pre-commit run -a
See further details for installation here.
Run and/or edit bash scripts train that runs train_mobious.py with config in the terminal. See further details here
bash scripts/models/train_unet_with_mobious.bash
Python-based application were implemented with holoscan-sdk, where holoscan-sdk was built on host Laptop computer with NVIDIARTXA2000-8GB. The UNet models were trained in cricket with A100-80GB, using either ~27K images of 1 channel or ~1K colour images of 3 channels.
| Animation(s) | API, Data, Model(s) |
|---|---|
webrtc_client.py with model _weights_15-12-24_07-00-10-sim-BHWC.onnx, running drop_frames_op at different PeriodicCondition(self, recess_period=period_ns) 1 to 30 Hz and improving backpressure mechanism. The following animation was recorded using drop_frame_operator period condition of branch_hz = 15 using a mobile phone as the client where image resolution with the default resolution with tensor shape of (640x480xch3)  |
🔩 Launch & debug ⌛ flowbenchmarking ⏳ glass2glass_latency |
 ready.py with model _weights_10-09-24_06-35-14-sim-BHWC.onnx trained with ~1K images and tested with (right) three frames repeated 10 times each to create a 30fps video and (left) with v4l2 /dev/video4 usb-endoscope camera with resolution of width640xheight480) |
🔩 Launch & debug 💾 Mobious dataset 🧠 Models |
See more demos here. See apis for detailed instructions on running applications.
Cloning repository- Generate your SSH keys as suggested here
- Setup you commit signature verification as shown here
- Clone the repository by typing (or copying) the following lines in a terminal
mkdir $HOME/repositories/oocular && cd $HOME/repositories/oocular
git clone git@github.com:oocular/ready.git