Google Landmark Recognition Challenge

• We use TensorFlow for image recognition. We will be using transfer learning, which means we are starting with a model that has been already trained on another problem. We will then be retraining it on a similar problem. Deep learning from scratch can take days, but transfer learning can be done in short order.
• MoBileNet will be used, to be small and efficient.

• Follow tutorial from codelabs
• Clone the respo from tensorflow
• After done the tutorial, replace the label_image.py from this respo to the cloned respo
• copy the run.ipynb to cloned respo and run

• image_downloader image downloader for both testing and training data, with compression option
• datasets analysis data analysis and visualisation
• label_image.py for image labelling and prediction
• run.ipynb to run the prediction

• train: 336 GB with 1,220,165 images
• test: 34.9 GB with 116,163 images
• May want to consider run on Cloud