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---
datasets:
- Illia56/Military-Aircraft-Detection
license: apache-2.0
widget:
- src: https://www.thedrive.com/uploads/2022/11/10/MIG31-Ukraine-Russia.jpg
---
# Model Card: Military Aircraft Detection with Vision Transformer (ViT)
## Model Information
- **Model Name:** Military Aircraft Image Detection
- **Model Type:** Vision Transformer (ViT)
## Model Overview
- **Purpose:** The model is designed for the detection and classification of military aircraft in images.
- **Intended Use:** Military surveillance, object recognition, and security applications.
## Model Training
- **Training Data:** Dataset of military aircraft images collected from Illia56/Military-Aircraft-Detection.
- **Data Preprocessing:** Random oversampling for class balance, data augmentation (rotation, flip, sharpness adjustment).
- **Model Architecture:** Vision Transformer (ViT) for image classification.
- **Pre-trained Model:** google/vit-base-patch16-224-in21k.
## Model Evaluation
- **Evaluation Metrics:**
- Accuracy
- F1 Score
- Confusion Matrix
- **Evaluation Dataset:** Split from the original dataset for testing.
- | Class | Precision | Recall | F1-Score | Support |
|------------|-----------|--------|----------|---------|
| A10 | 0.6716 | 0.7368 | 0.7027 | 247 |
| A400M | 0.6217 | 0.6748 | 0.6472 | 246 |
| AG600 | 0.4512 | 0.9919 | 0.6203 | 247 |
| AV8B | 0.6618 | 0.7287 | 0.6936 | 247 |
| B1 | 0.9000 | 0.6194 | 0.7338 | 247 |
| B2 | 0.7862 | 0.9231 | 0.8492 | 247 |
| B52 | 0.9528 | 0.4089 | 0.5722 | 247 |
| Be200 | 0.8333 | 0.8300 | 0.8316 | 247 |
| C130 | 0.8600 | 0.1748 | 0.2905 | 246 |
| C17 | 0.5556 | 0.0405 | 0.0755 | 247 |
| C2 | 0.5845 | 0.8543 | 0.6941 | 247 |
| C5 | 0.3776 | 0.7490 | 0.5020 | 247 |
| E2 | 0.8447 | 0.9028 | 0.8728 | 247 |
| E7 | 0.6000 | 0.9595 | 0.7383 | 247 |
| EF2000 | 1.0000 | 0.0364 | 0.0703 | 247 |
| F117 | 0.6005 | 0.9433 | 0.7339 | 247 |
| F14 | 0.9773 | 0.1741 | 0.2955 | 247 |
| F15 | 0.2919 | 0.2186 | 0.2500 | 247 |
| F16 | 0.8333 | 0.0203 | 0.0397 | 246 |
| F18 | 0.9355 | 0.2348 | 0.3754 | 247 |
| F22 | 0.4624 | 0.4980 | 0.4795 | 247 |
| F35 | 0.5373 | 0.2915 | 0.3780 | 247 |
| F4 | 0.4317 | 0.2429 | 0.3109 | 247 |
| J10 | 0.8711 | 0.6842 | 0.7664 | 247 |
| J20 | 0.5049 | 0.6301 | 0.5606 | 246 |
| JAS39 | 0.4535 | 0.4737 | 0.4634 | 247 |
| KC135 | 0.8957 | 0.7683 | 0.8271 | 246 |
| MQ9 | 0.7358 | 0.8943 | 0.8073 | 246 |
| Mig31 | 0.6080 | 0.4899 | 0.5426 | 247 |
| Mirage2000 | 0.3245 | 0.6478 | 0.4324 | 247 |
| P3 | 0.9423 | 0.3968 | 0.5584 | 247 |
| RQ4 | 0.7166 | 0.8907 | 0.7942 | 247 |
| Rafale | 0.3063 | 0.3968 | 0.3457 | 247 |
| SR71 | 0.7824 | 0.7571 | 0.7695 | 247 |
| Su25 | 1.0000 | 0.3618 | 0.5313 | 246 |
| Su34 | 0.5340 | 0.8583 | 0.6584 | 247 |
| Su57 | 0.6143 | 0.7317 | 0.6679 | 246 |
| Tornado | 0.6883 | 0.2146 | 0.3272 | 247 |
| Tu160 | 0.8000 | 0.8421 | 0.8205 | 247 |
| Tu95 | 0.8340 | 0.8543 | 0.8440 | 247 |
| U2 | 0.9371 | 0.6032 | 0.7340 | 247 |
| US2 | 0.7074 | 0.6559 | 0.6807 | 247 |
| V22 | 0.7212 | 0.9109 | 0.8050 | 247 |
| Vulcan | 0.3343 | 0.8947 | 0.4868 | 247 |
| XB70 | 0.6657 | 0.9676 | 0.7888 | 247 |
| YF23 | 0.5490 | 0.7967 | 0.6501 | 246 |
| Accuracy | | | 0.6082 | 11353 |
| Macro Avg | 0.6804 | 0.6082 | 0.5787 | 11353 |
| Weighted Avg| 0.6803 | 0.6082 | 0.5787 | 11353 |
## Potential Bias
- **Bias in Training Data:** Possible biases related to the data collection process.
- **Limitations:** Potential biases due to the nature of the dataset and model architecture.
## Ethical Considerations
- **Fairness:** Address any concerns regarding fairness and potential bias in model predictions.
- **Privacy:** Describe any privacy considerations related to the model's deployment and use.
## Model Usage Guidelines
- **Recommended Use Cases:** Military surveillance, security applications.
- **Limitations:** Clearly outline model limitations and potential failure scenarios.
- **Legal and Ethical Considerations:** Compliance with legal and ethical standards. |