LeafSnap30

LeafSnap30 is a (modified) subset of the images from the 30 species with the highest number of images from the LeafSnap dataset. LeafSnap is an electronic field guide for identifying tree species from photos of their leaves. The original dataset consists of images  taken from 2 different sources as well as their segmented versions using the LeafSnap segmentation algorithm. The two sources are: high quality "lab" images of pressed leaves from the Smithsonian collection and "field" images taken by mobile devices in outdoor environments.

The original "lab" images contain size and color calibration rulers, which interfere with the training of end-to-end Deep Learning (DL)  models for automatic tree species classification. Therefore, we have semi-manually cropped the "lab" images of the 30 species with most number of images in order to keep only the leaves and we do not include the segmentation masks. The original "lab" leaf images are also included in the dataset, but the file paths point only to the cropped ones.

The original dataset has been released in 2012 (before the DL revolution in Computer Vision) in order to promote further research in leaf recognition. The authors ask their paper to be sited (see original link above) if the dataset is used.

We are releasing the cropped subset as the LeafSnap30 dataset in order to demonstrate the performance of eXplainable AI (XAI) methods applied on DL models trained to solve simple, yet realistic scientific problem.

LeafSnap30是从LeafSnap数据集（LeafSnap dataset）中选取的30个图像量最多的树种的（经过修改的）图像子集。LeafSnap是一款用于通过树叶照片识别树种的电子野外指南。原始数据集包含两类来源的图像，以及使用LeafSnap分割算法（LeafSnap segmentation algorithm）生成的分割版本。两类来源分别为：史密森尼学会藏品中压制叶片的高质量“实验室”图像，以及由移动设备在户外环境中拍摄的“野外”图像。

原始“实验室”图像带有尺寸与颜色校准标尺，这会干扰用于自动树种分类的端到端深度学习（Deep Learning, DL）模型的训练。因此，我们对半手动裁剪了图像量最多的30个树种的“实验室”图像，仅保留叶片区域且未保留分割掩码（segmentation masks）。数据集中也包含原始的“实验室”叶片图像，但文件路径仅指向裁剪后的版本。

该原始数据集于2012年发布（早于计算机视觉（Computer Vision）领域的深度学习革命），旨在推动叶片识别方向的进一步研究。若使用该数据集，请引用其相关学术论文（详见上方原始链接）。

我们将该裁剪后的子集作为LeafSnap30数据集发布，旨在演示可解释人工智能（eXplainable AI, XAI）方法在针对简单且贴合实际的科学问题训练的深度学习模型上的应用效果。