Aerial Natural Disasters Unet

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• Natural distaster images (taken from drones) semantic segmentation using U-Net.

• I was assigned to work on this project during my time as a summer intern at IPM.

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How to use?

Training:

python train.py

arguements:

• --train_dir - address to training and validation images directory.
• --lr / -l - Learning rate.
• --base_lr - Base learning rate.
• --max_lr - Maximum learning rate.
• --lr_sched - Learning rate schedular. Valid values: reduce_on_plateau and cyclic for CyclicLR.
• --epochs / -e - Number of training epochs.
• --validation_split / -v, - Validation split ratio during training.
• --batch_size / -b, - Batch size for training dataset.
• --encoder - Encoder's backbone architecture. Default is resnet18.
• --debug - Show debug info (logs and stuff).
• --checkpoints_dir - Checkpoints directory.
• --loss_fn - Loss function.
• --save_best_only -
• --resume_from_best -

Testing / Evaluating:

python evaluate.py

Predicting:

python predict.py

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Dataset

• Dataset was from the LPCV2023 challenge Link.
• The dataset includes images of natural disasters taken from drone.
• Dataset was challenging, due to reasons including:
  • Severe class imballance in the images, which is a common thing for semantic segmentation tasks.
  • Very small (as images was taken from drones in high altitudes) and indistinguishable parts belonging to different classes.
• There are 13 classes, which are: background, avalanche, building_undamaged, building_damaged, cracks/fissure/subsidence, debris/mud//rock flow, fire/flare, flood/water/river/sea, ice_jam_flow, lava_flow, person, pyroclastic_flow, road/railway/bridge, vehicle

EDA:

Some of the images + their masks + classes distribution:

The picture above also shows the classes proportions summed up in all of the images. We can conclude that background class (black color in the masks), is the dominant class in almost all of the images.

• EDA codes are at notebooks/eda.ipynb.

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Model

Architecture:

• I chosen CNN-based U-Net as my main model's architecture for the task. Because it's very suitable for almost all of the segmentation tasks and it's already proven its strengths.
• I used the already implemented u-net models in pytorch from Segmentation Models package.

Training:

• Training codes are at train.py and notebooks/train.ipynb.
• Different loss functions (focal, ce, dice, ce+dice) can be used using the argument --loss_fn.
• Other training settings: CyclicLR schedular, WANDB for logging, Adam as optimizer.

Evaulation and Result:

• In the end using my model, I was able to achieve IOU of something about 0.3.
• Some of the predictions:

from left to right: Picture, GT mask, predicted mask.

• This shows that the model performs very well on some of the classes. But there's still room for improvement.

• In the end, unfortunately, because of lack of resources provided by IPM (GPU for exprimentation and training), I chosen to let this project go 😠.