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README.md

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+---
+pretty_name: "Sarawak Wildlife Image Classification Dataset"
+task_categories:
+  - image-classification
+tags:
+  - image
+  - wildlife
+  - sarawak
+  - computer-vision
+size_categories:
+  - n<1K
+---
+
+[简体中文](README.zh-CN.md)
+
+# Sarawak Wildlife Image Classification Dataset
+
+## Overview
+
+This dataset contains 865 JPG camera-trap wildlife images organised for multi-class image classification. It was prepared for the COS30019 Introduction to Artificial Intelligence Assignment 2, Scenario A: Wildlife Species Classification in Sarawak's National Parks using Camera Trap Images.
+
+The dataset uses one folder per class. The folder name is the class label used by the project models.
+
+## Dataset Summary
+
+| Item | Value |
+| --- | --- |
+| Task | Multi-class image classification |
+| Images | 865 unique JPG images |
+| Classes | 10 |
+| On-disk size | Approximately 362 MB |
+| Image labels | Parent folder names |
+| Current included split | Training only |
+
+## Classes and Distribution
+
+| Class label | Images |
+| --- | ---: |
+| bearded pig | 161 |
+| leopard cat | 57 |
+| long tailed macaque | 56 |
+| malayan civet | 224 |
+| marbled cat | 50 |
+| munjact | 42 |
+| pig tailed macaque | 55 |
+| sambar deer | 91 |
+| sun bear | 63 |
+| yellow throated marten | 66 |
+| **Total** | **865** |
+
+`munjact` is retained exactly as the original project folder label. Changing it would change the label mapping used by the existing models.
+
+## Directory Structure
+
+```text
+dataset/
+├── README.md
+├── README.zh-CN.md
+└── train/
+    ├── bearded pig/
+    ├── leopard cat/
+    ├── long tailed macaque/
+    ├── malayan civet/
+    ├── marbled cat/
+    ├── munjact/
+    ├── pig tailed macaque/
+    ├── sambar deer/
+    ├── sun bear/
+    └── yellow throated marten/
+```
+
+## Assignment Context and Compliance
+
+The assignment scenario asks for wildlife classification in Sarawak using camera-trap images. It specifies at least five classes with 150 training images per class, and a typical development workflow with training, validation, and testing data.
+
+This dataset has ten classes, but only `bearded pig` (161 images) and `malayan civet` (224 images) meet the 150-images-per-class training target. The remaining eight classes have fewer images. Therefore, the dataset does not yet meet that per-class requirement.
+
+## Split Notice
+
+The original project also contains a separate `val` directory outside this dataset folder. Its 178 images are all byte-for-byte duplicates of images in `train`, so it is not included here as an independent validation split.
+
+Before reporting model performance, create non-overlapping training, validation, and test splits from the 865 unique images. Images from the same camera-trap sequence should preferably stay in the same split to reduce leakage from near-duplicate frames.
+
+## Intended Use
+
+This dataset is intended for:
+
+- COS30019 coursework and demonstrations.
+- Educational experiments in image classification.
+- Prototype research on automated wildlife-image categorisation.
+
+It must not be used as the only evidence for conservation, wildlife-management, law-enforcement, or safety decisions.
+
+## Limitations and Biases
+
+- The classes are strongly imbalanced, from 42 `munjact` images to 224 `malayan civet` images.
+- The dataset is small for deep learning and may encourage overfitting.
+- The images may not represent all locations, seasons, lighting conditions, camera types, poses, or animal ages in Sarawak.
+- The dataset has no independent validation or test split in its current published structure.
+- Performance on this dataset may not generalise to other wildlife datasets or real-world deployments.
+
+## Data Source, Rights, and License
+
+The image source, copyright holder, and redistribution permission have not been recorded in this repository. Do not publish the images publicly or select a Hugging Face license until the data owner and redistribution rights have been confirmed.
+
+## Citation
+
+If you use this dataset, cite the original image source and the COS30019 Assignment 2 project. Add a formal citation here once the original data source is confirmed.

README.zh-CN.md

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+[English](README.md)
+
+# 砂拉越野生动物图像分类数据集
+
+## 概述
+
+本数据集包含 865 张用于多类别图像分类的 JPG 野生动物相机陷阱图片。它为 COS30019 Introduction to Artificial Intelligence 的 Assignment 2 Scenario A（使用相机陷阱图像进行砂拉越国家公园野生动物物种分类）准备。
+
+数据集以每个类别一个文件夹的方式组织。文件夹名称就是项目模型使用的类别标签。
+
+## 数据集摘要
+
+| 项目 | 数值 |
+| --- | --- |
+| 任务 | 多类别图像分类 |
+| 图片数量 | 865 张唯一 JPG 图片 |
+| 类别数量 | 10 |
+| 磁盘大小 | 约 362 MB |
+| 图片标签 | 父级文件夹名称 |
+| 当前包含的数据划分 | 仅训练集 |
+
+## 类别与分布
+
+| 类别标签 | 图片数量 |
+| --- | ---: |
+| bearded pig | 161 |
+| leopard cat | 57 |
+| long tailed macaque | 56 |
+| malayan civet | 224 |
+| marbled cat | 50 |
+| munjact | 42 |
+| pig tailed macaque | 55 |
+| sambar deer | 91 |
+| sun bear | 63 |
+| yellow throated marten | 66 |
+| **总计** | **865** |
+
+`munjact` 会完全保留为原始项目文件夹标签。修改它会改变现有模型使用的标签映射。
+
+## 目录结构
+
+```text
+dataset/
+├── README.md
+├── README.zh-CN.md
+└── train/
+    ├── bearded pig/
+    ├── leopard cat/
+    ├── long tailed macaque/
+    ├── malayan civet/
+    ├── marbled cat/
+    ├── munjact/
+    ├── pig tailed macaque/
+    ├── sambar deer/
+    ├── sun bear/
+    └── yellow throated marten/
+```
+
+## 作业背景与符合情况
+
+作业情境要求使用相机陷阱图像进行砂拉越野生动物分类。要求至少有五个类别，并且每个类别有 150 张训练图片；典型开发流程还应包含训练、验证与测试数据。
+
+本数据集有十个类别，但只有 `bearded pig`（161 张）和 `malayan civet`（224 张）达到每类 150 张训练图片的目标，其余八个类别的图片数量不足。因此，数据集目前尚未满足该每类图片数量要求。
+
+## 数据划分说明
+
+原项目在本数据集文件夹外还有一个独立的 `val` 目录。其 178 张图片全部与 `train` 中的图片逐字节相同，因此这里不会把它当作独立验证集发布。
+
+在报告模型表现前，应使用 865 张唯一图片建立互不重叠的训练集、验证集与测试集。来自同一个相机陷阱序列的图片最好保留在同一个数据划分中，以减少相近连续帧造成的数据泄漏。
+
+## 预期用途
+
+本数据集适用于：
+
+- COS30019 课程作业与演示。
+- 图像分类教学实验。
+- 野生动物图像自动分类的原型研究。
+
+它不应作为保护管理、野生动物管理、执法或安全决策的唯一依据。
+
+## 限制与偏差
+
+- 类别数量明显不均衡，从 42 张 `munjact` 到 224 张 `malayan civet`。
+- 对深度学习而言，数据集规模较小，可能导致过拟合。
+- 图片未必覆盖砂拉越所有地点、季节、光照条件、相机类型、动物姿势或年龄。
+- 目前发布的目录结构没有独立的验证集或测试集。
+- 在本数据集上的表现不一定能泛化到其他野生动物数据集或真实部署环境。
+
+## 数据来源、权利与许可证
+
+此仓库尚未记录图片来源、版权拥有者与再发布许可。在确认数据拥有者与再发布权利之前，请不要公开发布图片，也不要为 Hugging Face 选择许可证。
+
+## 引用
+
+如使用本数据集，请引用原始图片来源与 COS30019 Assignment 2 项目。确认原始数据来源后，请在此补充正式引用。