# Model Training Report

## Model Details
- **Model**: ResNet50
- **Number of Classes**: 12
- **Total Epochs**: 15
- **Optimizer**: Adam
- **Learning Rate**: 0.001
- **Loss Function**: CrossEntropyLoss

## Training Accuracy per Epoch

| Epoch | Training Accuracy (%) |
|-------|-----------------------|
| 1     | 58.32                 |
| 2     | 63.15                 |
| 3     | 69.45                 |
| 4     | 74.21                 |
| 5     | 77.58                 |
| 6     | 80.32                 |
| 7     | 82.01                 |
| 8     | 83.50                 |
| 9     | 84.72                 |
| 10    | 85.30                 |
| 11    | 86.85                 |
| 12    | 87.20                 |
| 13    | 88.10                 |
| 14    | 89.25                 |
| 15    | 90.30                 |

## Test Results
- **Average Test Loss**: 0.1985
- **Test Accuracy**: 91.76%

## Additional Notes
- The model achieved 91.76% on the test set, exceeding the 90% target.
- Training improvements were consistent, with significant gains in accuracy from epoch 5 onwards.
- This was achieved using a **pre-trained ResNet50 model** fine-tuned on the building classification dataset.
- Further fine-tuning or dataset expansion might improve the model even more.
- the dataset that is used for training is from kaggle :https://www.kaggle.com/datasets/balabaskar/wonders-of-the-world-image-classification?resource=download
"""