license:other region:us
---
license: other
license_name: corngrain
license_link: LICENSE
viewer: false
---
# CornGrain Dataset

**CornGrain** is a database with 1036 images of yellow corn grain collected on a sample basis. Grains are classified into classes: outsider, whole, standard, husk e black spot.

The CornGrain database has the following distribution:
| Class| Quantity | Class Proportion (%) |
| :---- | :----: | :----: |
| Outsider   |   2.062      | 15,2 |
| Whole          |   5.563    | 40,8 |
| Standard          |   5.503    | 40,4 |
| Husk          |   355    | 2,6 |
| Black Spot          |   141    | 1,0 |
| Total          |   13.624    | 100,0 |

## Training/Validation Distribution:

The division is made 70%/30%, detailed below:

| Class         | Training | Validation |
| :---- | :----: | :----: |
| Outsider | 1.484       | 578       |
| Whole        | 3.867       | 1.696     |
| Standard         | 3.808       | 1.695     |
| Husk       | 237         | 118       |
| Black Spot    | 106         | 35        |
| Total          | 9.502       | 4.122     |


## Language:
Image labels are in Portuguese.

### Label:
```
'corpo_estranho' (outsider), 'inteiro' (whole), 'padrao'(standard), 'pelicula'(husk), 'ponto_preto'(black spot)
```

## Format:
YOLO v8

## Annotation Process:

The CVAT (Computer Vision Annotation Tool) tool was used. First the labels were added: outsider, whole, standard, husk e black spot, identified with different colors and then bounding boxes were used to outline the corn grain and identify it according to its corresponding label.

## Data augmentation process:
The Albumentations library (https://albumentations.ai/) was used. First, the images were randomly cropped (RandomCrop) to size 640x640 to increase the number of images and highlight details without losing quality due to compression.
In addition to random cropping (RandomCrop) of 640px, traditional data augmentation techniques were used: HueSaturationValue, RandomBrigtnessContrast e HorizontalFlip. 
