Goal: Research the Impact of Imbalanced and Balanced Maritime Code Flag Datasets on the Performance of Two-Stage Image Detectors (R-CNN, Faster R-CNN and Mask R-CNN).
Main metrics:
- intersection over union (IoU),
- precision and recall,
- average precision (AP),
- mean average precision (mAP),
- F1 score (trade-off between precision and recall).
TODO
├── documentation <- UML diagrams
├── balancers <- Package with balancers and utils
│ ├── __init__.py <- Package identicator
│ ├── smote.py <- SMOTE balancer (interpolation)
│ ├── adasyn.py <- ADASYN balancer (interpolation)
│ ├── augmentation.py <- Augmentation balancer (augmenting images like rotations, etc.)
│ ├── autoencoder.py <- Autoencoder balancer (learning needed!)
│ ├── dgan.py <- DGAN balancer (learning needed!)
│ ├── balancer.py <- General balancer with all balancers (aggregating all of the above)
│ ├── annotations.py <- Annotations module
│ └── configuration_reader.py <- Balancer configuration reader
├── maritime-flags-dataset <- Source and balanced flags (A-Z)
│ ├── ADASYN_balanced_flags <- Balanced flags by using ADASYN balancer
│ ├── SMOTE_balanced_flags <- Balanced flags by using SMOTE balancer
│ ├── AUGMENTATION_balanced_flags <- Balanced flags by using Augmentation balancer
│ ├── DGAN_balanced_flags <- Balanced flags by using DGAN balancer
│ ├── AE_balanced_flags <- Balanced flags by using Autoencoder balancer
│ ├── combined_flags <- Combined/test images
│ ├── two_flags <- Balanced two flags (A and B) per 1000 images
│ └── imbalanced_flags <- Source folder with imbalanced flags
├── balance.py <- Balancing dataset by using balancers package (BALANCING)
├── balancer_configuration.json <- Balancer configuration
└── detection.py <- Training and testing image detectors (EVALUATING)
