Camera traps have become a cornerstone of biodiversity monitoring, but the artificial intelligence that turns vast quantities of images into usable ecological data is often locked behind commercial platforms or trained on fauna that does not match that of the British Isles. In an attempt to remove barriers and increase uptake, we release an open-source object detection model for 31 classes, 28 common UK mammal and bird species, plus utility classes for humans, calibration poles, and vehicles, drawn from a curated dataset of 48,165 labelled instances assembled from multiple sites over a decade of operational deployment through Conservation AI and its successor, Trap Tracker. The model, a YOLO26x detector trained and tested on an 80/10/10 class-stratified split, achieves a mean Average Precision of 0.984 at Intersection over Union (IoU) of 0.5 (0.956 at IoU 0.5-0.95) on the held-out validation set, with precision 0.988 and recall 0.965. On an unseen held-out test split, mean per-species confidence ranged from 0.96 to 0.99 across the 31 classes, with a 0.17% false-negative rate concentrated in difficult night-time, distant, or occluded images. These metrics are from data from the same pool of sites and cameras as training, so performance at entirely new sites is left to future work. We release the trained weights in ONNX format under a non-commercial licence, with local desktop and real-time camera support, aimed explicitly at ecologists with no machine-learning experience. This release is a deliberate counterweight to the multiple paid for models that have developed over the last decade.

翻译：相机陷阱已成为生物多样性监测的基石，但将海量图像转化为可用生态数据的AI技术往往被锁定在商业平台中，或基于与不列颠群岛本地动物群不匹配的物种训练。为消除障碍并提升普及度，我们发布了一个开源目标检测模型，涵盖31个类别，包括28种常见英国哺乳动物和鸟类物种，以及人类、校准杆和车辆等实用类别。该模型基于从多个地点编纂的、经过十年运行部署（通过Conservation AI及其后继系统Trap Tracker）的48,165个标注样本数据集。该模型采用YOLO26x检测器，按80/10/10的类别分层划分进行训练和测试，在保留验证集上的平均精度均值（mAP）在交并比（IoU）为0.5时达到0.984（IoU 0.5-0.95时达到0.956），精确率0.988，召回率0.965。在未见过的独立测试集上，31个类别的每个物种平均置信度范围在0.96至0.99之间，假阴性率为0.17%，主要集中于夜间、远距离或遮挡图像中的困难样本。这些指标基于与训练数据相同的站点和相机池，因此在新站点上的性能有待未来研究验证。我们以非商业许可证形式发布ONNX格式的预训练权重，支持本地桌面和实时相机部署，明确面向无机器学习经验的生态学家。本发布旨在对过去十年间涌现的多个付费模型形成制衡。