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Apiarist Honey Bee Detector

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.

Sourcehuggingface.co/maryammeda/apiarist-honey-bee-detector/tree/8883b3eb8d6f637a407354cf414e2d1600241058
KindOtherVersionRevision 8883b3e

Model source

Kind
Other
File size
21.5 MB
Version
vRevision 8883b3e
Updated
Sep 21, 2026
Downloads
49
Source
Hugging Face

Source description

Apiarist Honey-Bee Detector (YOLOv8s)

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.

Classes

Trained on the hendricks_ricky 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

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):

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 data: hendricks_ricky/bee-project, Roboflow Universe

Sources

1 source
Hugging FacePrimaryVersion 8883b3eb8d6f637a407354cf414e2d1600241058
huggingface.co/maryammeda/apiarist-honey-bee-detector/tree/8883b3eb8d6f637a407354cf414e2d1600241058

Verified Sep 9

Model artifacts

1 artifact

honey_bee_detector.pt

pt · 21.5 MB · SHA-256 894b7a41c9ad…c2b9 · Hugging Face

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Source excerpts

2 excerpts
ReadmeSep 9huggingface.co/maryammeda/apiarist-honey-bee-detector

--- 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...

NoteSep 9huggingface.co/maryammeda/apiarist-honey-bee-detector

Source context: 49 downloads · 0 likes · Pipeline object-detection · Library ultralytics · Repo maryammeda/apiarist-honey-bee-detector