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A custom-trained YOLOv8s specialist detector for honeycomb frame inspection. Built as part of Apiarist, a fully-offline AI hive inspector for backyard beekeepers, made for the Build Small Hackathon.
Fuente del modelo
Descripción de la fuente
A custom-trained YOLOv8s specialist detector for honeycomb frame inspection. Built as part of Apiarist, a fully-offline AI hive inspector for backyard beekeepers, made for the Build Small Hackathon.
Trained on the dataset (3,308 labeled images, including 892 queen-bee examples). Four classes:
Fuentes
1 fuenteVerificado 9 sept
Artefactos del modelo
1 artefactoExtractos de fuentes
2 extractos
Queen, queen bee (larger, elongated abdomen)Worker, worker / forager bees (the majority class)Drone, male / drone beesVarroa, varroa destructor mites visible on bees or combfrom ultralytics import YOLO
from PIL import Image
model = YOLO("honey_bee_detector.pt")
results = model(Image.open("hive_frame.jpg"), conf=0.10, device="cpu")
for box in results[0].boxes:
cls = model.names[int(box.cls.item())]
conf = float(box.conf.item())
print(f"{cls}: {conf:.0%}")
Recommended per-class confidence thresholds (tuned empirically on real frame photos):
PER_CLASS_CONF = {
"Worker": 0.25,
"Drone": 0.55, # higher, drones false-positive on fingers, shadows
"Varroa": 0.30, # mites are small
"Queen": 0.15, # lower, try harder to find her
}
yolov8s.ptThe training set includes ~892 queen images, which is far more than the typical bee-detection dataset but still limited compared to the millions of bees in worker class. Queen detection precision is moderate, when the model labels a queen, verify visually. Use as a candidate flag, not as ground truth.
Varroa mite detection works on close-up frames where mites are visible on the dorsal side of bees or in cells, but will miss mites hidden under abdomens.
Apache 2.0. Trained on data released under CC BY 4.0 by the original dataset authors.
If you use this model, please credit:
--- license: apache-2.0 library_name: ultralytics tags: - yolo - yolov8 - object-detection - bees - beekeeping - computer-vision pipeline_tag: object-detection --- # Apiarist Honey-Bee Detector (YOLOv8s) A custom-trained **YOLOv8s** specialist detector for honeycomb frame inspection. Built as part of [Apiarist](https://huggingface.co/spaces/build-small-hackathon/Apiarist), a fully-offline AI hive inspector for backyard beekeepers, made for the [Build Small Hackathon](https://huggingface.co/build-small-hackathon). ## Classes Trained on the [hendricks_ricky bee-project](https://universe.roboflow.com/hendricks_ricky-hotmail-de/bee-project) dataset (3,308 labeled images, including 892 queen-bee examples). Four classes: - `Queen`, queen bee (larger, elongated abdomen) - `Worker`, worker / forager bees (the majority class) - `Drone`, male / drone bees - `Varroa`, varroa destructor mites visible on bees or comb ## Usage ```python from ultralytics import YOLO from PIL import Image model = YOLO("honey_bee_detector.pt") results = model(Image.open("hive_frame.jpg"), conf=0.10, device="cpu") for box in results[0].boxes: cls = model.names[int(box.cls.item())] conf = float(box.conf.item()) print(f"{cls}: {conf:.0%}") ``` Recommended per-class confidence thresholds (tuned empirically on real frame photos): ```python PER_CLASS_CONF = { "Worker": 0.25, "Drone": 0.55, # higher, drones false-positive on fingers, shadows "Varroa": 0.30, # mites are small "Queen": 0.15, # lower, try harder to find her } ``` ## Training - Base weights: `yolov8s.pt` - Epochs: 60 - Image size: 640×640 - Batch size: 32 - Hardware: 1× NVIDIA T4 (Modal) - Cost: ~$0.40 of free hackathon credit ## Limitations The training set includes ~892 queen images, which is far more than the typical bee-detection dataset but still limited compared to the millions of bees in worker class. Queen detection precision is moderate, when the model labels a queen, verify visually. Use as a **candidate flag**, not as ground truth. Varroa mite detection works on close-up frames where mites are visible on the dorsal side of bees or in cells, but will miss mites hidden under abdomens. ## License Apache 2.0. Trained on data released under CC BY 4.0 by the original dataset authors. ## Citation If you use this model, please credit: - This work: Apiarist (Build Small Hackathon entry, 2026) - Training d...
Source context: 49 downloads · 0 likes · Pipeline object-detection · Library ultralytics · Repo maryammeda/apiarist-honey-bee-detector