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README.md

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+---
+license: apache-2.0
+tags:
+  - image-classification
+  - object-detection
+  - instance-segmentation
+  - medical-image-segmentation
+  - graph-neural-network
+  - mobile
+  - vision-gnn
+  - pytorch
+datasets:
+  - imagenet-1k
+  - coco
+  - kvasir-seg
+  - dsb2018
+metrics:
+  - top_1_accuracy
+  - mean_average_precision
+  - dice_score
+  - hausdorff_distance
+library_name: pytorch
+pipeline_tag: image-classification
+---
+
+# GeoViG: Geometry-Aware Graph Reasoning for Mobile Vision Tasks
+
+## Model Variants
+
+### 🖼️ Image Classification — ImageNet-1K
+
+| Model | Params (M) | MACs (G) | Top-1 Acc (%) | Checkpoint (pth) |
+|:---|:---:|:---:|:---:|:---:|
+| GeoViG-Ti | 3.5 | 0.9 | 75.2 | [Download](https://huggingface.co/TODO/geovig/resolve/main/pth/geovig_ti.pth) |
+| GeoViG-S | 5.0 | 1.2 | 77.5 | [Download](https://huggingface.co/TODO/geovig/resolve/main/pth/geovig_s.pth) |
+| GeoViG-M | 10.3 | 2.2 | 80.7 | [Download](https://huggingface.co/TODO/geovig/resolve/main/pth/geovig_m.pth) |
+| GeoViG-B | 19.7 | 4.5 | 82.4 | [Download](https://huggingface.co/TODO/geovig/resolve/main/pth/geovig_b.pth) |
+
+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: 
+For IPA chechpoints, please check:
+---
+
+### 📦 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](https://huggingface.co/TODO/geovig/resolve/main/detection/geovig_m_coco.pth) |
+| GeoViG-B | 19.7 | 42.5 | 64.0 | 46.8 | 38.9 | 61.2 | 41.7 | [Download](https://huggingface.co/TODO/geovig/resolve/main/detection/geovig_b_coco.pth) |
+
+---
+
+### 🏥 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](https://huggingface.co/TODO/geovig/resolve/main/medical/geovig_m_kvasir.pth) |
+
+**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](https://huggingface.co/TODO/geovig/resolve/main/medical/geovig_m_dsb2018.pth) |
+
+---
+
+## 🚀 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](https://github.com/SLDGroup/MobileViG) and uses the [MMDetection](https://github.com/open-mmlab/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](LICENSE).