Usage

train.py -- Train classifier. inference.py -- Inference without GradCAM visualization (uses trainer.test()) prediction.py -- (GradCAM vizualization on during validation without training (epochs 0))

Place all settings in config file and use train.py --config [config file name here]

Original solver settings

OPT = 'adam'  # adam, sgd
WEIGHT_DECAY = 0.0001
MOMENTUM = 0.9  # only when OPT is sgd
BASE_LR = 0.001
LR_SCHEDULER = 'step'  # step, multistep, reduce_on_plateau
LR_DECAY_RATE = 0.1
LR_STEP_SIZE = 5  # only when LR_SCHEDULER is step
LR_STEP_MILESTONES = [10, 15]  # only when LR_SCHEDULER is multistep

Wandb logging

Set to True in config file.

Directory Structure

EagleID/
├── data/                  # Dataset cache and data-related scripts
├── dataset/               # Dataset and annotations for datasets
├── models/                # Model architecture code
├── configs/               # Configuration files (e.g., hyperparameters, dataset info)
├── experiments/           # Logging and experiment management (results, logs, etc.)
├── notebooks/             # Jupyter notebooks for prototyping and demo
├── scripts/               # BASH scripts for running programs
├── utils/                 # Utility functions and helper scripts
├── checkpoints/           # Model checkpoints and saved weights
├── results/               #Generated results (e.g., GradCAM outputs, predictions)
├── train.py               # Training
├── test.py                # Testing
├── prediction.py          # Prediction
├── README.md              # Project description and usage guide
├── requirements.txt       # Dependencies list
└── .gitignore             # Files to ignore in version control