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README.md
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---
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datasets:
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- Illia56/Military-Aircraft-Detection
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license: apache-2.0
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metrics:
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- accuracy
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- f1
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- recall
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pipeline_tag: object-detection
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---
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# Model Card: Military Aircraft Detection with Vision Transformer (ViT)
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## Model Information
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- **Model Name:** Military Aircraft Image Detection
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- **Model Type:** Vision Transformer (ViT)
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## Model Overview
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- **Purpose:** The model is designed for the detection and classification of military aircraft in images.
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- **Intended Use:** Military surveillance, object recognition, and security applications.
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## Model Training
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- **Training Data:** Dataset of military aircraft images collected from Illia56/Military-Aircraft-Detection.
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- **Data Preprocessing:** Random oversampling for class balance, data augmentation (rotation, flip, sharpness adjustment).
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- **Model Architecture:** Vision Transformer (ViT) for image classification.
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- **Pre-trained Model:** google/vit-base-patch16-224-in21k.
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## Model Evaluation
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- **Evaluation Metrics:**
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- Accuracy
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- F1 Score
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- Confusion Matrix
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- **Evaluation Dataset:** Split from the original dataset for testing.
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- | Class | Precision | Recall | F1-Score | Support |
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|------------|-----------|--------|----------|---------|
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| A10 | 0.6716 | 0.7368 | 0.7027 | 247 |
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| A400M | 0.6217 | 0.6748 | 0.6472 | 246 |
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| AG600 | 0.4512 | 0.9919 | 0.6203 | 247 |
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| AV8B | 0.6618 | 0.7287 | 0.6936 | 247 |
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| B1 | 0.9000 | 0.6194 | 0.7338 | 247 |
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| B2 | 0.7862 | 0.9231 | 0.8492 | 247 |
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| B52 | 0.9528 | 0.4089 | 0.5722 | 247 |
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| Be200 | 0.8333 | 0.8300 | 0.8316 | 247 |
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| C130 | 0.8600 | 0.1748 | 0.2905 | 246 |
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| C17 | 0.5556 | 0.0405 | 0.0755 | 247 |
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| C2 | 0.5845 | 0.8543 | 0.6941 | 247 |
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| C5 | 0.3776 | 0.7490 | 0.5020 | 247 |
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| E2 | 0.8447 | 0.9028 | 0.8728 | 247 |
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| E7 | 0.6000 | 0.9595 | 0.7383 | 247 |
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| EF2000 | 1.0000 | 0.0364 | 0.0703 | 247 |
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| F117 | 0.6005 | 0.9433 | 0.7339 | 247 |
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| F14 | 0.9773 | 0.1741 | 0.2955 | 247 |
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| F15 | 0.2919 | 0.2186 | 0.2500 | 247 |
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| F16 | 0.8333 | 0.0203 | 0.0397 | 246 |
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| F18 | 0.9355 | 0.2348 | 0.3754 | 247 |
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| F22 | 0.4624 | 0.4980 | 0.4795 | 247 |
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| F35 | 0.5373 | 0.2915 | 0.3780 | 247 |
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| F4 | 0.4317 | 0.2429 | 0.3109 | 247 |
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| J10 | 0.8711 | 0.6842 | 0.7664 | 247 |
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| J20 | 0.5049 | 0.6301 | 0.5606 | 246 |
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| JAS39 | 0.4535 | 0.4737 | 0.4634 | 247 |
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| KC135 | 0.8957 | 0.7683 | 0.8271 | 246 |
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| MQ9 | 0.7358 | 0.8943 | 0.8073 | 246 |
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| Mig31 | 0.6080 | 0.4899 | 0.5426 | 247 |
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| Mirage2000 | 0.3245 | 0.6478 | 0.4324 | 247 |
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| P3 | 0.9423 | 0.3968 | 0.5584 | 247 |
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| RQ4 | 0.7166 | 0.8907 | 0.7942 | 247 |
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| Rafale | 0.3063 | 0.3968 | 0.3457 | 247 |
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| SR71 | 0.7824 | 0.7571 | 0.7695 | 247 |
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| Su25 | 1.0000 | 0.3618 | 0.5313 | 246 |
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| Su34 | 0.5340 | 0.8583 | 0.6584 | 247 |
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| Su57 | 0.6143 | 0.7317 | 0.6679 | 246 |
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| Tornado | 0.6883 | 0.2146 | 0.3272 | 247 |
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| Tu160 | 0.8000 | 0.8421 | 0.8205 | 247 |
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| Tu95 | 0.8340 | 0.8543 | 0.8440 | 247 |
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| U2 | 0.9371 | 0.6032 | 0.7340 | 247 |
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| US2 | 0.7074 | 0.6559 | 0.6807 | 247 |
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| V22 | 0.7212 | 0.9109 | 0.8050 | 247 |
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| Vulcan | 0.3343 | 0.8947 | 0.4868 | 247 |
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| XB70 | 0.6657 | 0.9676 | 0.7888 | 247 |
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| YF23 | 0.5490 | 0.7967 | 0.6501 | 246 |
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| Accuracy | | | 0.6082 | 11353 |
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| Macro Avg | 0.6804 | 0.6082 | 0.5787 | 11353 |
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| Weighted Avg| 0.6803 | 0.6082 | 0.5787 | 11353 |
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## Potential Bias
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- **Bias in Training Data:** Possible biases related to the data collection process.
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- **Limitations:** Potential biases due to the nature of the dataset and model architecture.
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## Ethical Considerations
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- **Fairness:** Address any concerns regarding fairness and potential bias in model predictions.
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- **Privacy:** Describe any privacy considerations related to the model's deployment and use.
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## Model Usage Guidelines
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- **Recommended Use Cases:** Military surveillance, security applications.
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- **Limitations:** Clearly outline model limitations and potential failure scenarios.
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- **Legal and Ethical Considerations:** Compliance with legal and ethical standards.
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