README.md

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¿

Dataset Card for Material Classification

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: https://semillerocv.github.io/proyectos.html
• Repository:
• Paper:
• Leaderboard:
• Point of Contact:

Dataset Summary

The Material_classification_2U dataset consists of 150 256x256 colour images in 5 classes, with 30 images per class. There are 120 training images and 30 test images.

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

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

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

[More Information Needed]

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}
}