Trained for 300 epochs on 8× NVIDIA A100 GPUs, batch size 1024, AdamW optimizer (lr=5e-4, weight decay=0.05), cosine schedule with 5-epoch warmup. Augmentations: RandAugment, Mixup (p=0.8), CutMix (p=1.0).

For CoreML checkpoint, please check: https://huggingface.co/OmarAlasqa/GeoViG/tree/main/CoreML For IPA chechpoints, please check: https://huggingface.co/OmarAlasqa/GeoViG/tree/main/IPA

📦 Object Detection & Instance Segmentation — MS COCO 2017

Backbone used with Mask R-CNN, 1× schedule (12 epochs), pretrained on ImageNet-1K.

Backbone	Params (M)	Box AP	Box AP₅₀	Box AP₇₅	Mask AP	Mask AP₅₀	Mask AP₇₅	Checkpoint
GeoViG-M	10.3	40.7	62.4	44.1	37.7	59.6	40.5	Download
GeoViG-B	19.7	42.5	64.0	46.8	38.9	61.2	41.7	Download

🏥 Medical Image Segmentation

Kvasir-SEG — Polyp Segmentation

Model	Params (M)	mAP	Dice ↑	IoU ↑	Hausdorff Dist ↓	Checkpoint
GeoViG-M	29.57	0.990	0.945	0.909	12.94	Download

Data Science Bowl 2018 — Nuclei Segmentation

Model	Params (M)	mAP	Dice ↑	IoU ↑	Hausdorff Dist ↓	Checkpoint
GeoViG-M	29.57	0.859	0.908	0.839	5.19	Download

🚀 Usage

Please check the github repo: https://github.com/OmarAlsaqa/GeoViG

Citation

If you use GeoViG in your research, please cite:

bibtex

@article{alsaqa2026geovig,
  title     = {GeoViG: Geometry-Aware Graph Reasoning for Mobile Vision Tasks in Natural and Medical Images},
  author    = {Alsaqa, Omar and Mohammed, Emad and Aleem, Saiqa},
  journal   = {Under Review at IEEE EMBC},
  year      = {2026}
}

Acknowledgements

This work builds upon MobileViG and uses the MMDetection framework for detection and segmentation experiments. Training was performed on the Compute Canada A100 cluster.

License

This project is released under the Apache 2.0 License.