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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/OmarAlasqa/GeoViG/blob/main/pth/geovig_ti_5e4_8G_300_75_22/checkpoint.pth) |
| GeoViG-S | 5.0 | 1.2 | 77.5 | [Download](https://huggingface.co/OmarAlasqa/GeoViG/blob/main/pth/geovig_s_5e4_8G_300_77_48/checkpoint.pth) |
| GeoViG-M | 10.3 | 2.2 | 80.7 | [Download](https://huggingface.co/OmarAlasqa/GeoViG/blob/main/pth/geovig_m_5e4_8G_300_80_70/checkpoint.pth) |
| GeoViG-B | 19.7 | 4.5 | 82.4 | [Download](https://huggingface.co/OmarAlasqa/GeoViG/blob/main/pth/geovig_b_5e4_8G_300_82_38/checkpoint.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: 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](https://huggingface.co/OmarAlasqa/GeoViG/blob/main/coco_det_seg_pth/geovig_m_det_seg/epoch_12.pth) |
| GeoViG-B | 19.7 | 42.5 | 64.0 | 46.8 | 38.9 | 61.2 | 41.7 | [Download](https://huggingface.co/OmarAlasqa/GeoViG/blob/main/coco_det_seg_pth/geovig_b_det_seg/epoch_12.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/OmarAlasqa/GeoViG/blob/main/medical/kvasir_geovig_m/checkpoint.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/OmarAlasqa/GeoViG/blob/main/medical/dsb_geovig_m/checkpoint.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).
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