Open-Source AI Model Released for Detecting UK Mammals in Camera Trap Images
Researchers have released a free, open-source AI object detection model capable of identifying 28 common UK mammal and bird species from camera trap images, trained on over 48,000 labelled instances collected across a decade of field deployment. The model, based on the YOLO26x architecture, achieves a mean Average Precision of 0.984 at standard detection thresholds, with a false-negative rate of just 0.17%. The release aims to democratise wildlife monitoring by giving ecologists without machine-learning expertise access to high-performance tools previously locked behind commercial platforms.
A team of researchers from multiple UK institutions has published an open-source AI model for automated detection of wildlife in camera trap imagery, targeting the 31 classes most relevant to British biodiversity monitoring — 28 mammal and bird species plus humans, calibration poles, and vehicles. The model was trained on a curated dataset of 48,165 labelled instances gathered from multiple field sites over ten years through the Conservation AI platform and its successor, Trap Tracker. Using a YOLO26x detector and an 80/10/10 class-stratified train/validation/test split, the system achieved a mean Average Precision of 0.984 at IoU 0.5 and 0.956 at the stricter IoU 0.5–0.95 threshold, with precision of 0.988 and recall of 0.965. Per-species confidence on the held-out test set ranged from 0.96 to 0.99 across all 31 classes, with the small residual error concentrated in challenging night-time, distant, or partially occluded images. The trained weights are released in ONNX format under a non-commercial licence, with support for local desktop use and real-time camera feeds, explicitly designed for ecologists with no machine-learning background. The authors frame the release as a deliberate counterweight to the proliferation of paid commercial models that have dominated the field over the past decade.
What's missing
The authors explicitly acknowledge that all performance metrics derive from data drawn from the same pool of sites and cameras used in training; generalisation performance at entirely new, unseen field sites has not yet been evaluated and is left to future work. It is also unclear how the model performs across different camera trap hardware brands or in ecosystems outside the British Isles.
What different sources said
- arXiv cs.LGCenter
Democratising Camera Trap AI: An Open-Source Model for Detecting UK Mammals
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