| --- |
| license: apache-2.0 |
| task_categories: |
| - object-detection |
| - image-classification |
| tags: |
| - military |
| - aircraft |
| - aerospace |
| - yolo |
| - defense |
| - birds |
| - drones |
| size_categories: |
| - 10K-100K |
| --- |
| |
| # Military Aircraft Detection & Classification Dataset (87 Classes + Advanced Backgrounds) |
|
|
| ## Overview |
| This dataset is a professionally prepared resource for training high-performance object detection models like **YOLOv11** and classification models. It features a balanced distribution across **87 distinct military aircraft classes**, augmented with a specialized background strategy to handle real-world "noise" like wildlife and small commercial UAVs. |
|
|
| ## Key Technical Specifications |
| * **Total Images**: 26,668 (Updated with 1.5% Bird & 1.5% Drone injection). |
| * **Resolution**: Uniform **640x640 pixels**. |
| * **Annotation Format**: **YOLO-Ready** (.txt) with normalized coordinates. |
| * **Stratified Split**: Approximately **80% Train / 10% Val / 10% Test** maintained across all 87 classes. |
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|
|
| ## Enhanced Background Strategy |
| To significantly reduce false positives, the dataset includes **3,127 background images** (approx. 13% of total). These are empty labels that teach the model to ignore: |
| 1. **Empty Skies & Clouds & Commercial AriCrafts**: Standard negative samples. |
| 2. **Birds**: 1.5% injection to prevent "Bird-as-Plane" false detections. |
| 3. **Commercial Drones**: 1.5% injection to help the model distinguish between small quadcopters and military-grade UAVs. |
|
|
| ## Understanding the Annotation Format |
| Each image has a matching `.txt` file containing the detection labels. |
|
|
| ### Positive Sample Example |
| A file named `su57_01.txt` containing: `68 0.475000 0.496875 0.415625 0.859375` |
| * **68**: **Class ID**. Matches **Su57** in our 87-class table. |
| * **0.475000**: **X-Center**. Horizontal center at 47.5% of image width. |
| * **0.496875**: **Y-Center**. Vertical center at 49.6% of image height. |
|
|
| ### Background (Negative) Samples |
| These files contain **0 bytes** (empty). |
| * **Aircraft Backgrounds**: `sky_bg_01.txt` — Standard sky/clouds. |
| * **Bird Backgrounds**: `Birds_v1_01.txt` — High-resolution bird imagery. |
| * **Drone Backgrounds**: `Drones_v1_01.txt` — Commercial quadcopters and hobbyist drones. |
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| --- |
|
|
| ## 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 | |
