Datasets:

Ahnuf
/

Military_Aircraft_Detection_Classification_Image_Dataset

license: apache-2.0
task_categories:
  - object-detection
  - image-classification
tags:
  - military
  - aircraft
  - aerospace
  - yolo
  - defense
  - commercial-aircraft
  - birds
  - drones
size_categories:
  - 10K-100K

Military Aircraft Detection & Classification Dataset

88 Classes with Advanced Background Suppression

Overview

This dataset is a professionally curated resource for training high-performance object detection and image classification models such as YOLOv11.
It contains 88 distinct military aircraft classes and is explicitly designed for real-world deployment, where false positives from civilian aircraft, birds, and small drones are common.

To address this, the dataset incorporates a structured background suppression strategy, teaching models not only what to detect, but also what to ignore.

Dataset Summary

Total Images: 26,668
Military Aircraft Classes: 87
Image Resolution: 640 × 640 (uniform)
Annotation Format: YOLO (.txt) with normalized coordinates
Primary Use: Military aircraft detection and classification

Dataset Split & Background Statistics

Split	Total Images	Background Images	Background %
Train	21,342	2,508	11.75%
Validation	2,641	295	11.17%
Test	2,645	284	10.74%
Total	26,668	3,127	~11.7%

The dataset maintains a stratified split of approximately 80% Train / 10% Validation / 10% Test across all 87 classes.

Advanced Background Suppression Strategy

To significantly reduce false detections, the dataset includes 3,127 background-only images with empty annotations.
These images are intentionally selected to represent common real-world confounders in aerial imagery.

Background Categories

Empty Skies, Clouds & Commercial Aircraft
Negative samples containing:
- Clear or cloudy skies with no aircraft
- Commercial passenger and cargo aircraft
  This trains the model to distinguish civilian airliners from military platforms.
Bird Backgrounds (≈1.5%)
High-resolution bird imagery to prevent bird-as-aircraft false positives, particularly at long range or low resolution.
Commercial Drone Backgrounds (≈1.5%)
Civilian and hobbyist UAVs (quadcopters and small drones), enabling the model to differentiate between commercial drones and military-grade UAVs.

All background images use empty .txt label files (0 bytes) and contain no bounding boxes.

Annotation Format

Each image is paired with a corresponding .txt file in YOLO format.

Positive Sample Example

su57_01.txt 68 0.475000 0.496875 0.415625 0.859375

Field Description

68 → Class ID (Su-57)
0.475000 → X-center (47.5% of image width)
0.496875 → Y-center (49.69% of image height)
0.415625 → Bounding box width
0.859375 → Bounding box height

Background (Negative) Samples

Background label files are intentionally empty:

sky_bg_01.txt
commercial_aircraft_bg_01.txt
birds_v1_01.txt
drones_v1_01.txt

Final Class ID Table (87 Classes)

ID	Class	ID	Class	ID	Class	ID	Class
0	A10	22	CL415	44	JF17	66	Su34
1	A400M	23	E2	45	JH7	67	Su47
2	AG600	24	E7	46	KAAN	68	Su57
3	AH64	25	EF2000	47	KC135	69	TB001
4	AKINCI	26	EMB314	48	KF21	70	TB2
5	AV8B	27	F117	49	KJ600	71	Tejas
6	An124	28	F14	50	Ka27	72	Tornado
7	An22	29	F15	51	Ka52	73	Tu160
8	An225	30	F16	52	MQ9	74	Tu22M
9	An72	31	F18	53	Mi24	75	Tu95
10	B1	32	F2	54	Mi26	76	U2
11	B2	33	F22	55	Mi28	77	UH60
12	B52	34	F35	56	Mi8	78	US2
13	Be200	35	F4	57	Mig29	79	V22
14	C1	36	FCK1	58	Mig31	80	Vulcan
15	C130	37	H6	59	Mirage2000	81	WZ7
16	C17	38	Il76	60	P3	82	X32
17	C2	39	J10	61	RQ4	83	XB70
18	C390	40	J20	62	Rafale	84	Y20
19	C5	41	J35	63	SR71	85	YF23
20	CH47	42	J36	64	Su24	86	Z10
21	CH53	43	JAS39	65	Su25	87	Z19

Intended Use Cases

Military aircraft detection and classification
Civilian vs military aircraft discrimination
UAV and drone differentiation
Long-range aerial surveillance research
False-positive suppression benchmarking for YOLO models