| --- |
| 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) |
|
|
| ```python |
| 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) |
