---
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
### 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}
}
```