README.md

metadata

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.