
# 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.
a10 a400m ag600 av8b b1 b2 b52 be200 c130 c17 c2 c5 e2 e7 ef2000 f117 f14 f15 f16 f18 f22 f35 f4 j10 j20 jas39 kc135 mq9 mig31 mirage2000 p3 rq4 rafale sr71 su25 su34 su57 tornado tu160 tu95 u2 us2 v22 vulcan xb70 yf23