Meta_Album_INS_2_Mini: 4,080 Images of 102 Insect Pest Species

4,080 square-cropped images of 102 insect pest categories, preprocessed to 128x128 pixels for computer vision tasks. This mini version is derived from the large-scale IP102 benchmark dataset originally created for Insect Pest Recognition. The dataset was released under the Meta-Album project in March 2022 by Ihsan Ullah and colleagues.

Use Cases

• Training few-shot image classification models based on the 102 insect pest categories.
• Benchmarking computer vision algorithms for agricultural pest recognition based on the preprocessed 128x128 images.
• Developing automated monitoring systems for insect pests that affect specific crops, based on the hierarchical taxonomy mentioned in the description.

Strengths

• Contains 4,080 images across 102 distinct insect pest classes, providing a multi-class classification challenge.
• Images are consistently preprocessed into a uniform 128x128 pixel square format.
• Derived from a large-scale benchmark (IP102) containing over 75,000 images, suggesting a substantial original source.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.
• The mini version's 4,080 images represent a subset of the original dataset; the selection methodology is not described.

Provenance

Source

IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition by Xiaoping Wu et al., preprocessed and released via the Meta-Album project.

Collection Method

Images were cropped into perfect squares and resized from the original IP102 dataset.