JabutiVisionData - Annotated Image Dataset for Morphological Differentiation of Chelonoidis carbonaria and Chelonoidis denticulata

The JabutiVisionData dataset is an annotated image collection designed to support computer vision research focused on the morphological differentiation between Chelonoidis carbonaria (red-footed tortoise) and Chelonoidis denticulata (yellow-footed tortoise). These two species exhibit high morphological similarity and partial geographic overlap in South America, making visual identification challenging, particularly in fine-grained classification scenarios. The dataset consists of 1,150 RGB images collected from the citizen science platform iNaturalist, representing photographic records captured in natural environments. A rigorous manual curation process was applied to ensure taxonomic correctness, remove duplicate records, and exclude images with insufficient visual quality. The curated dataset contains a total of 1,173 annotated objects, distributed in a balanced manner between the two species. Annotations were performed manually using bounding boxes that fully encompass the visible portions of each individual, including the carapace, limbs, and head when present. Images containing multiple individuals were annotated with independent bounding boxes for each specimen. In cases of partial occlusion, only clearly visible and identifiable regions were annotated to avoid inference of non-visible areas. All annotations were reviewed to ensure spatial consistency and label accuracy.
The dataset is provided in both COCO and YOLO annotation formats, ensuring interoperability with a wide range of deep learning frameworks and object detection architectures. The data are partitioned into training (74%), validation (14%), and test (12%) subsets, preserving class balance across all splits. Statistical analyses of bounding box dimensions reveal substantial variability in object scale and aspect ratio, reflecting realistic variations in capture distance, pose, and framing. JabutiVisionData is suitable for training and evaluating object detection and classification models, benchmarking deep learning architectures under fine-grained visual discrimination conditions, and supporting non-invasive environmental monitoring applications. Its structure also enables integration with camera trap systems and edge AI devices, facilitating real-world biodiversity monitoring and ecological research.

To organize the dataset clearly and professionally for YOLOv12 training, the folder structure should be arranged following the COCO/YOLO standard as follows:

JabutiVisionData/
├── test/
│ ├── _annotations.coco.json # train metadata
│ └── images/ # 141 images
├── train/
│ ├── _annotations.coco.json
│ └── images/ # 849 images
└── valid/
├── _annotations.coco.json
└── images/ # 160 images

JabutiVisionData数据集是一套带标注的图像集合，旨在支持针对红腿陆龟（Chelonoidis carbonaria）与黄腿陆龟（Chelonoidis denticulata）的形态区分相关计算机视觉研究。这两个物种种群形态相似度极高，且在南美洲存在部分地理分布重叠，导致视觉识别难度较大，在细粒度分类（fine-grained classification）场景中尤为突出。
该数据集包含1150张RGB图像，采集自公民科学平台iNaturalist，均为自然环境下拍摄的影像记录。研究团队采用了严格的人工审核流程，以确保分类学准确性、去除重复记录，并剔除视觉质量不达标的图像。经整理后的数据集共包含1173个标注对象，在两个物种间实现了均衡分布。
标注工作均通过人工完成，使用完全覆盖每个个体可见区域的边界框（bounding box），涵盖背甲、四肢及头部（若可见）。对于包含多只个体的图像，会为每个样本单独标注独立的边界框。若存在局部遮挡的情况，仅对清晰可见且可识别的区域进行标注，避免对不可见区域进行推断。所有标注均经过复核，以确保空间一致性与标签准确性。
该数据集同时提供COCO与YOLO两种标注格式，可兼容绝大多数深度学习框架与目标检测架构。数据被划分为训练集（74%）、验证集（14%）与测试集（12%），且所有划分均保持类别均衡。对边界框尺寸的统计分析显示，对象的尺度与宽高比存在显著差异，这反映了拍摄距离、姿态与取景方式的真实变化。
JabutiVisionData数据集适用于训练与评估目标检测及分类模型、在细粒度视觉判别场景下对深度学习架构进行基准测试，以及支持非侵入式环境监测应用。其结构还可与相机陷阱系统及边缘AI设备集成，助力实际的生物多样性监测与生态学研究。
为适配YOLOv12训练的清晰专业的数据集组织方式，需遵循COCO/YOLO标准设置如下文件夹结构：
JabutiVisionData/
├── test/
│ ├── _annotations.coco.json # 训练集元数据
│ └── images/ # 141张图像
├── train/
│ ├── _annotations.coco.json
│ └── images/ # 849张图像
└── valid/
├── _annotations.coco.json
└── images/ # 160张图像