---
license: agpl-3.0
datasets:
- solo2307/osm-sketchmaps
language:
- en
metrics:
- accuracy
base_model:
- Ultralytics/YOLOv8
---


# Siamese YOLOv9e – Sketch Map Marking Detection (Final Checkpoints)

## Overview
This repository provides **final YOLO checkpoints** for detecting hand-drawn markings on printed maps.
The models use a **Siamese (dual-stream) YOLOv9e** design for change detection: a clean basemap and an annotated map are processed jointly to detect only newly added markings.

All final checkpoints were trained with:
1) **synthetic pretraining on OSM-based data**, followed by
2) **fine-tuning on hand-drawn datasets** (two basemap domains)

---

## Available Models (Final)

| Checkpoint file | Fine-tuning domain | Intended use |
|---|---|---|
| `siamese_yolov9e_osm_hand.pt` | Hand-drawn on **OSM** basemaps | Best for OSM/cartographic backgrounds |
| `siamese_yolov9e_ewi_hand.pt` | Hand-drawn on **EWI** basemaps | Best for satellite/imagery-style backgrounds |

> Notes:
> - Both models share the same synthetic OSM pretraining stage.
> - EWI-derived imagery used for fine-tuning cannot be redistributed; only the checkpoint is provided.

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## Input Format (6 channels)
The models expect a **6-channel input** representing a paired image:

- channels `[0,1,2]` = **clean basemap** (RGB)
- channels `[3,4,5]` = **annotated map** (RGB)

In other words: `RGB_clean + RGB_annotated` concatenated.

If your data is stored as multi-band TIFF:
- ensure channel order is **RGB** (not BGR)
- ensure the first 3 bands correspond to the clean map

---

## Dataset

Training and evaluation datasets are available at:
https://huggingface.co/datasets/solo2307/osm-sketchmaps