license: apache-2.0
task_categories:
- object-detection
- image-classification
tags:
- military
- aircraft
- aerospace
size_categories:
- 10K-100K
Military Aircraft Classification Dataset
Overview
This dataset is designed for fine-grained object detection of military aircraft and encompasses 96 different military aircraft types. Some types are merged as one class along with their variants because their airframes or external features differ only slightly, making them difficult to distinguish—especially when only parts of the aircraft are visible.
Supported Aircraft Classes
A-10, A-400M, AG-600, AH-64, AKINCI, AV-8B, An-124, An-22, An-225, An-72, B-1, B-2, B-21, B-52, Be-200, C-1, C-130, C-17, C-2, C-390, C-5, CH-47, CH-53, CL-415, E-2, E-7, EF-2000, EMB-314, F-117, F-14, F-15, F-16, F-2, F-22, F-35, F-4, F/A-18, F-CK-1, H-6, Il-76, J-10, J-20, J-35, J-36, J-50, JAS-39, JF-17, JH-7, KAAN, KC-135, KF-21, KJ-600, Ka-27, Ka-52, MQ-25, MQ-9, Mi-24, Mi-26, Mi-28, Mi-8, Mig-29, Mig-31, Mirage2000, P-3, RQ-4, Rafale, SR-71, Su-24, Su-25, Su-34, Su-47, Su-57, TB-001, TB-2, Tejas, Tornado, Tu-160, Tu-22M, Tu-95, U-2, UH-60, US-2, V-22, V-280, Vulcan, WZ-10, WZ-7, WZ-9, X-29, X-32, XB-70, XQ-58, Y-20, YF-23, Z-10, Z-19.
Dataset Structure
The primary data is provided as a single compressed archive for efficient downloading:
datasetz.zip: Contains all images and annotation files.
Data Partitioning (80-20 Split)
The dataset is divided into train and test folders. To ensure that both folders maintain a consistent 80-20 split for every single aircraft model, we used a technique called Stratified Splitting.
- Train Set (80%): Used for model training and feature learning.
- Test Set (20%): Used for unbiased evaluation of the final model.
This approach ensures that even rare aircraft types are represented proportionally in both the training and testing phases.
Content
The archive contains JPEG images (.jpg) and corresponding annotation files in CSV format with matching filenames. Each aircrafts image with thier information.
Annotations
Each annotation CSV file details the objects within an image using the PASCAL VOC format. The columns include:
- filename: The identifier for both the image and its corresponding annotation file.
- width and height: The dimensions of the image in pixels.
- class: The aircraft type.
- xmin, ymin, xmax, ymax: The coordinates of the bounding box.
Sample Annotation
| filename | width | height | class | xmin | ymin | xmax | ymax |
|---|---|---|---|---|---|---|---|
| 000aa01b25574f28b654718db0700f72 | 2048 | 1365 | F35 | 852 | 177 | 1998 | 503 |
| 000aa01b25574f28b654718db0700f72 | 2048 | 1365 | JAS39 | 169 | 769 | 549 | 893 |
| 000aa01b25574f28b654718db0700f72 | 2048 | 1365 | JAS39 | 125 | 908 | 440 | 1009 |
| 000aa01b25574f28b654718db0700f72 | 2048 | 1365 | B52 | 277 | 901 | 1288 | 1177 |