README.md

metadata

license: mit
library_name: ultralytics
pipeline_tag: object-detection
tags:
  - birdlense
  - yolov11
  - object-detection
  - birds
  - rodents
  - feeder-camera

BirdLense Detector (3-class)

YOLO detector weights for BirdLense Hub (CV/ML roadmap #368).

Classes (model.names)

0. Bird
1. Rodent
2. Background

Intended use

• Binary detector stage in BirdLense two-stage pipeline
• Feeder-camera wildlife monitoring
• Local/on-device inference

Integration in BirdLense

Default detector path in repository:

app/processor/models/detection/weights/best.pt

Relevant config keys:

• processor.models.binary
• processor.detector_weight_contract (off | warn | enforce)
• optional OpenVINO path:
  • processor.models.binary_openvino
  • BIRDLENSE_BINARY_OPENVINO_PATH

Quick start (Ultralytics)

from ultralytics import YOLO

model = YOLO("best.pt")
print(model.names)  # expected: {0:'Bird', 1:'Rodent', 2:'Background'}
results = model.predict("sample.jpg", conf=0.25)