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Satellite image classification with convolutional networks. Finalist in hackathon organized by McKinsey's AI division, Quantum Black.

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hackathon QuantumBlack, McKinsey + Binet IA, École Polytechnique

The problem of the hackathon consisted in classifying satellite images to check whether they had silos in them, to aid a certain start-up know where to place new silos. As a bonus task, the goal was to segment images into the classes "silo" or "not silo".

To create the environment, run conda env create -f environment.yml.

To track training, tensorboard --logdir lightning_logs --bind_all.

The dataset given is in ai_ready/. The test dataset is given in test_to_send/.

All models built are in models/ To choose which model to train, change line from models.model_unet import HackathonModel in run_training.py. To launch training, python run_training.py -v version_name.

The checkpoints for our best models are in model_weights/. The best model found was model_efficient for classification. The only model made for segmentation was model_unet.

To run evaluation on a .csv with the same format as the ones found inside the datasets, run python run_eval -p path_to_csv.

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Satellite image classification with convolutional networks. Finalist in hackathon organized by McKinsey's AI division, Quantum Black.

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