metadata

task_categories:
  - image-classification
language:
  - en
tags:
  - Images
pretty_name: 'Material Classification Hands On '
size_categories:
  - n<1K
dataset_info:
  config_name: plain_text
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            '0': Brick
            '1': Metal
            '2': Paper
            '3': Plastic
            '4': Wood
  splits:
    - name: train
      num_examples: 120
    - name: test
      num_examples: 30
license: mit

Dataset Card for Material Classification

Dataset Description

Homepage: https://semillerocv.github.io/proyectos.html
Repository: https://github.com/Sneider-exe/Clasificacion_Materiales

Dataset Summary

The Material_classification_2U dataset consists of 150 256x256 color images, categorized into 5 classes with 30 images per class. The dataset is divided into two main subsets: 120 images for training and 30 images for testing. Each image is labeled into one of the following five categories: Brick, Metal, Paper, Plastic, and Wood.

Supported Tasks and Leaderboards

image-classification: The goal of this task is to classify a given image into one of 5 classes.

Languages

English

Dataset Structure

Total Images: 150
- Image Size: 256x256 pixels
- Color: RGB (Color images)
Classes: 5
- Brick: 30 images
- Metal: 30 images
- Paper: 30 images
- Plastic: 30 images
- Wood: 30 images
Splits:
- Train: 120 images (24 per class)
- Test: 30 images (6 per class)

Data Instances

A sample from the training set is provided below:

{
  'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=256x256>,
  'label': 1
}

Data Fields

image: A PIL.Image.Image object containing the 256x256 image. Note that when accessing the image column: dataset['train']["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time.
label: 0-4 with the following correspondence '0': Brick '1': Metal '2': Paper '3': Plastic '4': Wood

Data Splits

Train and Test

Citation Information

@TECHREPORT{
    author = {Brayan Sneider Sánchez, Dana Meliza Villamizar, Cesar Vanegas, Juan Jose Calderón},
    title = {Material Classification},
    institution = {Universidad Industrial de Santander},
    year = {2024}
}