Single Shot MultiBox Detector - SSD
Single Shot Multibox Detector - Facade Parsing
Semester Project at Swiss Data Science Center @EPFL
This project is a collaboration between the Civil Engineers and the Swiss Data Science Center(SDSC) at EPFL. The motivation behind the project is to help Civil Engineers in detecting the damage imposed on buildings by an earthquake. In this semester project, only a sub-part of the whole project has been tackled. By using deep learning, we automate the detection of important objects in an given image. As a deep learning method we are using the Single Shot MultiBox Detector.
Find more details about the project in the report.pdf.
To install the
ssd_project library and use it within your python environment:
pip install ssd-project==1.0
Examples on how to use
Examples for various functions and sub-tasks of the project can be found in the Notebooks folder.
To train, the
train.py script is ready to use:
usage: train.py [-h] [--epochs EPOCHS] [--split-seed SPLIT_RATIO] [--batch-train BATCH_SIZE] [--lr LR] [--model PRETRAINED_MODEL] [--path_imgs PATH_IMGS] [--path_bboxes PATH_BBOXES] [--path_labels PATH_LABELS] Train an SSD model optional arguments: -h, --help show this help message and exit --epochs EPOCHS --split-seed SPLIT_RATIO --batch-train BATCH_SIZE --lr LR --model PRETRAINED_MODEL --path_imgs PATH_IMGS --path_bboxes PATH_BBOXES --path_labels PATH_LABELS
python3 train.py --epochs=500 python train.py --model saved_models/BEST_model_ssd300.pth.tar
For loading a pretrained a model python train.py --model saved_models/BEST_model_ssd300.pth.tar
When training a new model, the train.py script saves a specific structure, therefore to continue training from the best specific point, just give as input to the saved structure.
For predictions, one should look into
Ground Truth & Aspect Ratios
notebooks/creation_ground_truth.ipynb shows how the ground truth was derived.
notebooks/Aspect_Ratios.ipynb shows how aspect ratios and scales for prior box are derived
notebooks/Data Augmentations Examples.ipynb shows an example of all the transformations applied to one image.
The best model is saved in
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
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