dataeval.utils.datasets.MILCO

class dataeval.utils.datasets.MILCO(root, image_set='train', transforms=None, download=False, verbose=False)

A side-scan sonar dataset focused on mine-like object detection.

The dataset comes from the paper Side-scan sonar imaging data of underwater vehicles for mine detection by N.P. Santos et. al. (2024).

The full dataset contains 1170 side-scan sonar images collected using a 900-1800 kHz Marine Sonic dual frequency side-scan sonar of a Teledyne Marine Gavia Autonomous Underwater Vehicle. All the images were carefully analyzed and annotated, including the image coordinates of the Bounding Box (BB) of the detected objects divided into NOn-Mine-like BOttom Objects (NOMBO) and MIne-Like COntacts (MILCO) classes.

This dataset is consists of 345 images from 2010, 120 images from 2015, 93 images from 2017, 564 images from 2018, and 48 images from 2021). In these 1170 images, there are 432 MILCO objects, and 235 NOMBO objects. The class “0” corresponds to a MILCO object and the class “1” corresponds to a NOMBO object. The raw BB coordinates provided in the downloaded text files are (x, y, w, h), given as percentages of the image (x_BB = x/img_width, y_BB = y/img_height, etc.). The images come in 2 sizes, 416 x 416 or 1024 x 1024.

Parameters:
root : str or pathlib.Path

Root directory where the data should be downloaded to or the milco folder of the already downloaded data.

image_set : "train", "operational", or "base", default "train"

If “train”, then the images from 2015, 2017 and 2021 are selected, resulting in 315 MILCO objects and 177 NOMBO objects. If “operational”, then the images from 2010 and 2018 are selected, resulting in 117 MILCO objects and 58 NOMBO objects. If “base”, then the full dataset is selected.

transforms : Transform, Sequence[Transform] or None, default None

Transform(s) to apply to the data.

download : bool, default False

If True, downloads the dataset from the internet and puts it in root directory. Class checks to see if data is already downloaded to ensure it does not create a duplicate download.

verbose : bool, default False

If True, outputs print statements.

path

Location of the folder containing the data.

Type:

pathlib.Path

image_set

The selected image set from the dataset.

Type:

“train”, “operational” or “base”

index2label

Dictionary which translates from class integers to the associated class strings.

Type:

dict[int, str]

label2index

Dictionary which translates from class strings to the associated class integers.

Type:

dict[str, int]

Return type:

dict[str, int]

metadata

Typed dictionary containing dataset metadata, such as id which returns the dataset class name.

Type:

DatasetMetadata

transforms

The transforms to be applied to the data.

Type:

Sequence[Transform]

size

The size of the dataset.

Type:

int

Note

Data License: CC BY 4.0