Butterfly & Moths Image Classification 100 species
Butterfly & Moths Image Classification 100 species
Datasets
Butterfly & Moths Image Classification 100 species
File
Butterfly & Moths Image Classification 100 species
Use Case
Butterfly & Moths Image Classification 100 species
Description
Explore the Butterfly & Moths Image Classification Dataset, featuring 100 species with 12,594 training images, 500 test images, and 500 validation images. Each image is 224x224 pixels in jpg format
Description:
This dataset has been meticulously updated to correct issues identified in the previous version, ensuring higher quality for research and model training. It is designed for the classification of 100 butterfly and moth species and is organized into training, testing, and validation sets. The training set contains 12,594 images, the test set includes 500 images, and the validation set also comprises 500 images. Each image is in jpg format with dimensions of 224×224 pixels, and all images are categorized into 100 subdirectories, each representing a unique species.
Download Dataset
A comprehensive CSV file accompanies the dataset, containing 4 columns and 13,595 rows. This file includes the class ID, filepaths, labels, and dataset type (train, test, or validation) for each image. The organized structure and detailed labeling make this dataset an invaluable resource for developing machine learning models for species classification, contributing significantly to the fields of entomology and computer vision. Researchers and practitioners can utilize this dataset to enhance the study and conservation of butterflies and moths through advanced image classification techniques.
Dataset Structure and Organization
The dataset is systematically organized into training, testing, and validation sets, ensuring a robust framework for model development and evaluation. Each of the 100 butterfly and moth species is stored in its own subdirectory, making it easy to manage and use for classification tasks.
Additionally, the accompanying CSV file provides structured metadata, including class IDs, file paths, labels, and dataset splits, which simplifies data handling and integration into machine learning workflows.
Key Features of the Dataset
- 100 Species Classes: Wide variety of butterfly and moth species.
- Large Training Set: Over 12,000 images for effective model learning.
- Standardized Image Size: 224×224 pixels, ideal for CNN architectures.
- Detailed CSV Annotations: Includes labels, file paths, and dataset splits.
- Well-Structured Data Splits: Supports proper training, validation, and testing.
Applications
This dataset can be used in various AI and research applications:
- Species Classification Models: Identify butterfly and moth species accurately.
- Biodiversity Monitoring: Support conservation and ecological studies.
- Computer Vision Research: Develop and benchmark image classification models.
- Educational Projects: Learn and experiment with multi-class classification.
Conclusion
This dataset provides a strong foundation for building high-performance models for butterfly and moth species classification. With its well-organized structure, diverse classes, and detailed annotations, it supports advancements in biodiversity research, computer vision, and ecological conservation.
This dataset is sourced from Kaggle.
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