Scaplen Lab Drosophila Segmentation
Example output on a real frame: raw footage, then stage 1 whole-fly segmentation, then stage 2 body parts (blue = head, green = thorax, red = abdomen).
Model source
- Kind
- Other
- File size
- 51.9–52.1 MB
- Version
- vRevision 8b5d5ad
- Updated
- Oct 10, 2026
- Downloads
- 40
- Source
- Hugging Face
Source description
Scaplen Lab Drosophila Segmentation
two-stage segmentation: raw footage, stage 1 whole-fly, stage 2 body parts
Example output on a real frame: raw footage, then stage 1 whole-fly segmentation, then stage 2 body parts (blue = head, green = thorax, red = abdomen).
Two YOLO26m instance-segmentation models for tracking Drosophila
Models
stage1_wholefly_seg.pt: whole-fly segmentation, one class (fly). Finds and segments every fly in the full frame.stage2_bodyparts_seg.pt: body-part segmentation (head, thorax, abdomen), run on single-fly crops taken from stage 1.
Both are YOLO26m (medium). Validation mask mAP@50 is about 0.995 for each.
Usage
from ultralytics import YOLO
model = YOLO("stage1_wholefly_seg.pt")
results = model.predict("frame.png")
Full pipeline, training scripts, datasets, and analysis code are on GitHub: https://github.com/ciij05/scaplen-lab
Built by Christian Khoury, Scaplen Lab, 2026.
Sources
1 source
Sources
1 sourceVerified Oct 10
Model artifacts
2 artifacts
Model artifacts
2 artifactsSource excerpts
3 excerpts
Source excerpts
3 excerpts--- tags: - instance-segmentation - yolo - drosophila - object-detection - biology library_name: ultralytics --- # Scaplen Lab Drosophila Segmentation  Example output on a real frame: raw footage, then stage 1 whole-fly segmentation, then stage 2 body parts (blue = head, green = thorax, red = abdomen). Two YOLO26m instance-segmentation models for tracking Drosophila ## Models - `stage1_wholefly_seg.pt`: whole-fly segmentation, one class (fly). Finds and segments every fly in the full frame. - `stage2_bodyparts_seg.pt`: body-part segmentation (head, thorax, abdomen), run on single-fly crops taken from stage 1. Both are YOLO26m (medium). Validation mask mAP@50 is about 0.995 for each. ## Usage ```python from ultralytics import YOLO model = YOLO("stage1_wholefly_seg.pt") results = model.predict("frame.png") ``` Full pipeline, training scripts, datasets, and analysis code are on GitHub: https://github.com/ciij05/scaplen-lab Built by Christian Khoury, Scaplen Lab, 2026.
Source context: 40 downloads · 0 likes · Pipeline object-detection · Library ultralytics · Repo christiankhoury05/scaplen-lab-drosophila-segmentation
Source context: 103 downloads · 0 likes · Pipeline object-detection · Library ultralytics · Repo christiankhoury05/scaplen-lab-drosophila-segmentation
