bird-detect-v0

YOLO11n detect model fine-tuned on CUB-200-2011 bird bounding boxes (single class bird). Companion to synthet/eye-pose-v0 for subject localization when eye keypoints are not required (species crops, gating, counting).

Used with the image-scoring-model eye-quality detect CLI.

Class

Index	Name
0	bird

Training

• Base: YOLO11n (yolo11n.pt)
• Dataset: CUB-200-2011 boxes via data/wildlife_bird_det (~10k train / 1.7k val)
• Epochs: 100 (imgsz 640, batch 16)
• Final validation (epoch 100):
  • Box mAP50: 0.994
  • Box mAP50-95: 0.892
  • Precision: 0.993
  • Recall: 0.997

Usage

from ultralytics import YOLO

model = YOLO("hf://synthet/bird-detect-v0/bird_detect_v0.pt")
results = model.predict("bird.jpg", imgsz=640)

Or with the eye_quality package:

pip install -e "git+https://github.com/synthet/image-scoring-model.git"
huggingface-cli download synthet/bird-detect-v0 bird_detect_v0.pt --local-dir models/
python -m eye_quality detect bird.jpg --weights models/bird_detect_v0.pt

Limitations

• Single-class bird boxes only; not a multi-species detector.
• Trained on CUB-200 studio/Flickr-style photos; validate on your field library.
• CUB labels are typically one bird per image; crowded frames need care.