Apiarist Queen Classifier

--- 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...