print1Username/COS30019-Assignment-2.dataset

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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

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.