Bark_Merged

This dataset is a unified bark-image collection for tree species classification, built by merging three publicly available bark datasets into a single, consistently labeled benchmark. It contains 32,078 labeled images spanning 66 distinct tree species, captured across different geographic regions to improve species diversity and model generalization. The merged dataset integrates images from three sources: BarkNet 1.0 Images: 23,000 Classes: 23 BarkVN-50 Images: 5,578 Classes: 50 Wood Species (Bangalore) Images: 3,500 Classes: 22 Merged: Images 32,078 Classes:66 Images relabeled and reorganized into 66 consistent classes (labels 0–65). Only classes with approximately 100–300 images retained for balance. All images resized to 224 × 224 pixels for standard CNN input. Suggested split: 70% training / 15% validation / 15% testing. If you use this dataset please cite the work: @inproceedings{ali2025bark, author = {Ali, Aroshi and Jahan, Nusrat and Sagor, Anite Halim and Alam, S. M. Jahangir and Ahmed, Saad}, title = {Lightweight and Interpretable {CNNs} for Bark-Based Tree Species Classification}, booktitle = {2025 28th International Conference on Computer and Information Technology (ICCIT)}, year = {2025}, address = {Cox's Bazar, Bangladesh}, doi = {https://doi.org/10.1109/ICCIT68739.2025.11490516} }本数据集为面向树木种类分类任务的统一树皮图像基准数据集，由三个公开树皮数据集整合而成，具备单一统一的标注体系。该数据集包含32078张带标注图像，涵盖66个不同树木种类，采集自多个地理区域，以提升物种多样性并增强模型泛化能力。 该合并数据集整合了三个来源的图像： 1. BarkNet 1.0 图像（BarkNet 1.0 Images）：共计23000张，对应23个类别 2. BarkVN-50 图像（BarkVN-50 Images）：共计5578张，对应50个类别 3. 班加罗尔木材种类图像（Wood Species (Bangalore) Images）：共计3500张，对应22个类别 合并后总图像数为32078张，总计66个类别。 所有图像均经重新标注与重组，划分为66个统一类别（标签取值范围为0至65）。为保障数据集的类别平衡性，仅保留图像数量约为100至300张的类别。所有图像均被统一调整至224×224像素尺寸，以适配标准卷积神经网络（Convolutional Neural Network, CNN）的输入需求。 推荐数据集划分比例为：70%用于训练集、15%用于验证集、15%用于测试集。 若使用本数据集，请引用如下文献： @inproceedings{ali2025bark, author = {Ali, Aroshi and Jahan, Nusrat and Sagor, Anite Halim and Alam, S. M. Jahangir and Ahmed, Saad}, title = {Lightweight and Interpretable {CNNs} for Bark-Based Tree Species Classification}, booktitle = {2025 28th International Conference on Computer and Information Technology (ICCIT)}, year = {2025}, address = {Cox's Bazar, Bangladesh}, doi = {https://doi.org/10.1109/ICCIT68739.2025.11490516} }