Update README.md

README.md

@@ -1,60 +1,108 @@
 ---
 license: apache-2.0
 task_categories:
-- object-detection
-- image-classification
+  - object-detection
+  - image-classification
 tags:
-- military
-- aircraft
-- aerospace
-- yolo
-- defense
-- birds
-- drones
+  - military
+  - aircraft
+  - aerospace
+  - yolo
+  - defense
+  - commercial-aircraft
+  - birds
+  - drones
 size_categories:
-- 10K-100K
+  - 10K-100K
 ---
 
-# Military Aircraft Detection & Classification Dataset (87 Classes + Advanced Backgrounds)
+# Military Aircraft Detection & Classification Dataset  
+### 88 Classes with Advanced Background Suppression
 
 ## 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.
+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.
 
-## 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.
+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
 
-## 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.
+1. **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.
 
-## Understanding the Annotation Format
-Each image has a matching `.txt` file containing the detection labels. 
+2. **Bird Backgrounds (≈1.5%)**  
+   High-resolution bird imagery to prevent *bird-as-aircraft* false positives, particularly at long range or low resolution.
+
+3. **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
-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.
+
+`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
-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.
+
+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 |
@@ -76,4 +124,13 @@ These files contain **0 bytes** (empty).
 | 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 |
+| 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