metadata
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:
- synthetic pretraining on OSM-based data, followed by
- 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.
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