Apiarist Queen Classifier
Binary image classifier (EfficientNet-B0, 5M params) trained to distinguish queen bees from worker bees on cropped bee images.
Model source
- Kind
- Other
- File size
- 15.6 MB
- Version
- vRevision 2266d86
- Updated
- Sep 17, 2026
- Downloads
- 0
- Source
- Hugging Face
Source description
Apiarist Queen-vs-Worker Bee Classifier
Binary image classifier (EfficientNet-B0, ~5M params) trained to distinguish queen bees from worker bees on cropped bee images.
Built as part of Apiarist, an offline AI hive inspector for backyard beekeepers, made for the Build Small Hackathon.
Why a dedicated classifier?
Multi-class YOLO detectors fight two problems at once (localize + classify) and queens lose because they're rare and visually subtle. A focused binary classifier on cropped bee images is the right architecture: small, fast, trained specifically for one decision.
Training
- Backbone:
efficientnet_b0(ImageNet pretrained) - Training data: bee crops extracted from labelled bounding boxes in two Roboflow datasets (Matt Nudi honey bees + Hendricks Ricky bee-project)
- 1,146 queen crops + 29,825 worker crops, balanced via weighted sampling
- Heavy augmentation: rotations, flips, color jitter
- 90/10 train/val split, weighted random sampling for class balance
- AdamW + cosine schedule, mixed precision on a single T4 GPU
- Trained on Modal
Validation metrics
- Accuracy: 0.997
- Precision (queen): 0.991
- Recall (queen): 0.934
- F1: 0.962
Recommended use
Pair with a bee detector (e.g. YOLOv8). Run the detector first, then classify each cropped bee through this model. Threshold queen probability at 0.85 for high-precision flagging.
import torch, timm
from torchvision import transforms
ckpt = torch.load("queen_classifier.pt", map_location="cpu")
model = timm.create_model(ckpt["arch"], pretrained=False, num_classes=2)
model.load_state_dict(ckpt["state_dict"])
model.eval()
tf = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize([0.485,0.456,0.406], [0.229,0.224,0.225]),
])
with torch.no_grad():
probs = torch.softmax(model(tf(crop).unsqueeze(0)), dim=1)
queen_idx = ckpt["class_to_idx"]["queen"]
queen_prob = probs[0, queen_idx].item()
Caveats
The training distribution leans toward close-up macro photos of bees on honeycomb. Generalization to wide-angle inspection photos (with hands / background visible) is weaker, since YOLO's bee bounding boxes on those photos are often smaller and less precise than the training crops.
License
Apache 2.0. Trained on data released under CC BY 4.0.
Sources
1 source
Sources
1 sourceVerified Sep 17
Model artifacts
1 artifact
Model artifacts
1 artifactSource excerpts
2 excerpts
Source excerpts
2 excerpts--- license: apache-2.0 library_name: timm tags: - bees - beekeeping - image-classification - efficientnet pipeline_tag: image-classification --- # Apiarist Queen-vs-Worker Bee Classifier Binary image classifier (EfficientNet-B0, ~5M params) trained to distinguish queen bees from worker bees on cropped bee images. Built as part of [Apiarist](https://huggingface.co/spaces/build-small-hackathon/Apiarist), an offline AI hive inspector for backyard beekeepers, made for the [Build Small Hackathon](https://huggingface.co/build-small-hackathon). ## Why a dedicated classifier? Multi-class YOLO detectors fight two problems at once (localize + classify) and queens lose because they're rare and visually subtle. A focused binary classifier on cropped bee images is the right architecture: small, fast, trained specifically for one decision. ## Training - Backbone: `efficientnet_b0` (ImageNet pretrained) - Training data: bee crops extracted from labelled bounding boxes in two Roboflow datasets (Matt Nudi honey bees + Hendricks Ricky bee-project) - 1,146 queen crops + 29,825 worker crops, balanced via weighted sampling - Heavy augmentation: rotations, flips, color jitter - 90/10 train/val split, weighted random sampling for class balance - AdamW + cosine schedule, mixed precision on a single T4 GPU - Trained on [Modal](https://modal.com) ## Validation metrics - Accuracy: 0.997 - Precision (queen): 0.991 - Recall (queen): 0.934 - **F1: 0.962** ## Recommended use Pair with a bee detector (e.g. YOLOv8). Run the detector first, then classify each cropped bee through this model. Threshold queen probability at 0.85 for high-precision flagging. ```python import torch, timm from torchvision import transforms ckpt = torch.load("queen_classifier.pt", map_location="cpu") model = timm.create_model(ckpt["arch"], pretrained=False, num_classes=2) model.load_state_dict(ckpt["state_dict"]) model.eval() tf = transforms.Compose([ transforms.Resize((224, 224)), transforms.ToTensor(), transforms.Normalize([0.485,0.456,0.406], [0.229,0.224,0.225]), ]) with torch.no_grad(): probs = torch.softmax(model(tf(crop).unsqueeze(0)), dim=1) queen_idx = ckpt["class_to_idx"]["queen"] queen_prob = probs[0, queen_idx].item() ``` ## Caveats The training distribution leans toward close-up macro photos of bees on honeycomb. Generalization to wide-angle inspection photos (with hands / bac...
Source context: 0 downloads · 0 likes · Pipeline image-classification · Library timm · Repo maryammeda/apiarist-queen-classifier
