生态学家和野生动物监测团队终于有了免费、高精度的AI工具，可以直接在本地运行，无需依赖付费平台。做生物多样性研究的建议试试这个模型，能大幅减少手动标注图像的时间。
研究人员发布了一款开源目标检测模型YOLO26x，专门用于识别英国常见的28种哺乳动物和鸟类，以及人类、校准杆和车辆等3个实用类别。该模型基于10年运营部署中收集的48,165个标注实例训练，在验证集上平均精度达到0.984（IoU 0.5），召回率0.965。模型以ONNX格式发布，支持本地桌面和实时摄像头，面向无机器学习经验的生态学家，旨在打破商业平台对相机陷阱AI的垄断。
Democratising Camera Trap AI: An Open-Source Model for Detecting UK Mammals
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.