README.md

metadata

title: CM-Oculomics
emoji: 👁️
colorFrom: indigo
colorTo: blue
sdk: gradio
sdk_version: 6.16.0
python_version: '3.10'
app_file: app.py
pinned: false
license: apache-2.0

CM-Oculomics

Upload a color fundus photograph to obtain:

1. an anti-VEGF intolerance risk score (0–1),
2. a Grad-CAM explanation heatmap, and
3. three interpretable vascular biomarkers (density, skeleton length, fractal dimension).

Built on the generalist vision foundation model DINOv2 (ViT-L/14), fine-tuned for anti-VEGF intolerance prediction. Weights are released under Apache-2.0. Research prototype — not a medical device. Not for clinical use. No patient data are bundled with this Space.

Model weights

The fine-tuned weights (dino_deploy.pth, ~1.2 GB) are downloaded at startup from a Hugging Face model repository. Set a Space variable:

• WEIGHTS_URL — direct download URL, e.g. https://huggingface.co/<your-user>/CM-Oculomics/resolve/main/dino_deploy.pth

(or WEIGHTS_PATH if you upload the file directly into the Space). If no weights are found the demo still runs but clearly labels its output as a placeholder.

Links

• Code & full reproducibility: https://github.com/23008613g/CM-Oculomics
• Archive (DOI): https://doi.org/10.5281/zenodo.20537894

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Deploy to a Hugging Face Space (step by step)

Prerequisites: a free Hugging Face account and the CLI (pip install -U huggingface_hub); log in once with huggingface-cli login.

1. Host the weights in a HF model repo (one-time):

huggingface-cli repo create CM-Oculomics --type model           # -> <your-user>/CM-Oculomics
huggingface-cli upload <your-user>/CM-Oculomics \
    "path/to/dino_deploy.pth" dino_deploy.pth                    # uploads the 1.2 GB checkpoint (LFS)

2. Create the Space: huggingface.co → New → Space → SDK Gradio, hardware CPU basic (free) is enough. This creates https://huggingface.co/spaces/<your-user>/CM-Oculomics.

3. Push the app to the Space:

git clone https://huggingface.co/spaces/<your-user>/CM-Oculomics space && cd space
cp ../app.py ../requirements.txt .
cp -r ../src .
cp ../README_HFSpace.md README.md          # the Space README MUST be named README.md
# (optional) cp -r ../assets .
git add . && git commit -m "CM-Oculomics demo (DINOv2)" && git push

4. Point the Space at the weights: Space → Settings → Variables and secrets → add variable WEIGHTS_URL = https://huggingface.co/<your-user>/CM-Oculomics/resolve/main/dino_deploy.pth.

The Space builds, downloads the weights on first boot (a few minutes for 1.2 GB; cached afterwards), and serves a public URL. On free CPU, inference is a few seconds per image — fine for a demo; upgrade to a small GPU for snappier response.

timm, pytorch-grad-cam (grad-cam), opencv-python, scikit-image are all in requirements.txt, so the Space installs everything it needs.