Overview

While go through vacation photos, people always ask themself: What is the name of this temple I visited in Thailand? Who created this monument in France? Landmark recognition can help! This technology can predict landmark labels directly from image pixels, to help people better understand and organize their photo collections.

In this project, a model use for landmark recognition will be built based on ImageNet Classification With Deep Convolutional Neural Networks

Requirements

• Python 3.x
• Install the requirements packages pip install -r requirements.txt

Dataset

• The dataset can be downloaded from Google Landmark Recognition Challenge.
• Unzip the downloaded dataset and put under src/data/dataset.csv and follow the notebook src/analysis.ipynb or just use the reduced version of the original dataset.
• Use the script src/utils/download_data.py to download the images. Use the command line way: python src/utils/download_data.py "src/data/train.csv" "src/data/train" or just follow the notebook src/analysis.ipynb

File Descriptions

• src/data/train.csv - the training images set
• src/data/valid.csv - the validation images set
• src/data/test.csv - the test images set
• src/analysis.ipynb - The notebook that I've used to analyze and preprocess dataset
• src/benchmark_model.ipynb - The notebook that I've used to build a benchmark model
• src/vgg16_model.ipynb - The notebook that I've used to build my final solution