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---
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
### 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
The dataset is divided into two main subsets: Train and Test.
- **Train Split:**
- **Number of Images:** 120
- **Distribution:** 24 images per class
- **Test Split:**
- **Number of Images:** 30
- **Distribution:** 6 images per class
Both splits are stratified, ensuring that each class is proportionally represented in both the Train and Test subsets. This means that the percentage of images for each class remains consistent across both splits, providing a balanced and representative distribution for model training and evaluation.
### Citation Information
```
@TECHREPORT{
author = {Brayan Sneider Sánchez, Dana Meliza Villamizar, Cesar Vanegas, Juan Jose Calderón},
title = {BMWP2},
institution = {Universidad Industrial de Santander},
year = {2024}
}
``` |