---
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.