We introduce a lightweight experimentation pipeline designed to lower the barrier for applying machine learning (ML) methods for classifying images in ecological research. We enable ecologists to experiment with ML models independently, thus they can move beyond off-the-shelf models and generate insights tailored to local datasets and specific classification tasks and target variables. Our tool combines a simple command-line interface for preprocessing, training, and evaluation with a graphical interface for annotation, error analysis, and model comparison. This design enables ecologists to build and iterate on compact, task-specific classifiers without requiring advanced ML expertise. As a proof of concept, we apply the pipeline to classify red deer (Cervus elaphus) by age and sex from 3392 camera trap images collected in the Veldenstein Forest, Germany. Using 4352 cropped images containing individual deer labeled by experts, we trained and evaluated multiple backbone architectures with a wide variety of parameters and data augmentation strategies. Our best-performing models achieved 90.77% accuracy for age classification and 96.15% for sex classification. These results demonstrate that reliable demographic classification is feasible even with limited data to answer narrow, well-defined ecological problems. More broadly, the framework provides ecologists with an accessible tool for developing ML models tailored to specific research questions, paving the way for broader adoption of ML in wildlife monitoring and demographic analysis.

翻译：本文介绍了一种轻量级实验流程，旨在降低生态学研究中应用机器学习方法进行图像分类的门槛。该流程使生态学家能够独立进行机器学习模型实验，从而超越现成模型，针对本地数据集及特定分类任务与目标变量生成定制化洞见。我们的工具结合了用于预处理、训练和评估的简易命令行界面，以及用于标注、错误分析和模型比较的图形界面。这一设计使得生态学家无需掌握高级机器学习专业知识，即可构建并迭代开发紧凑的任务专用分类器。作为概念验证，我们将该流程应用于对德国费尔登施泰因森林采集的3392张相机陷阱图像中的马鹿进行年龄与性别分类。利用专家标注的4352张包含单个马鹿的裁剪图像，我们训练并评估了多种骨干架构，涵盖了广泛的参数设置与数据增强策略。性能最佳的模型在年龄分类任务中达到90.77%的准确率，在性别分类中达到96.15%的准确率。这些结果表明，即使数据有限，针对明确界定的具体生态问题实现可靠的人口统计分类是可行的。更广泛而言，该框架为生态学家提供了一个可访问的工具，用于开发契合特定研究问题的机器学习模型，为机器学习在野生动物监测与种群统计分析中的更广泛应用铺平了道路。