https://github.com/ducha-aiki/google-retrieval-challenge-2019-fastai-starter

fast.ai starter kit for Google Landmark Retrieval 2019 challenge
https://github.com/ducha-aiki/google-retrieval-challenge-2019-fastai-starter

cnn fastai kaggle pytorch retrieval

Last synced: about 2 months ago
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fast.ai starter kit for Google Landmark Retrieval 2019 challenge

• Host: GitHub
• URL: https://github.com/ducha-aiki/google-retrieval-challenge-2019-fastai-starter
• Owner: ducha-aiki
• Created: 2019-04-15T13:58:18.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-04-19T11:22:08.000Z (over 5 years ago)
• Last Synced: 2024-07-03T04:04:06.880Z (3 months ago)
• Topics: cnn, fastai, kaggle, pytorch, retrieval
• Language: Jupyter Notebook
• Size: 2.74 MB
• Stars: 61
• Watchers: 4
• Forks: 19
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-fastai - Google Retrieval Challenge 2019
• awesome-fastai - Google Retrieval Challenge 2019

README

        
# Google Landmark Retrieval 2019 Competition fast.ai Starter Pack

The code here is all you need to do the first submission to the [Google Landmark Retrieval 2019 Competition](https://www.kaggle.com/c/landmark-retrieval-2019). It is based on [FastAi library](https://github.com/fastai/fastai) release 1.0.47 and borrows helpser code from great [cnnimageretrieval-pytorch](https://github.com/filipradenovic/cnnimageretrieval-pytorch) library. The latter gives much better results than code in the repo, but not ready-to-make submission and takes 3 days to converge compared to 45 min here. 

## Making first submission

1. Install the [fastai library](https://github.com/fastai/fastai), specifically version 1.0.47. 

2. Install the [faiss library](https://github.com/facebookresearch/faiss). 

conda install faiss-gpu cudatoolkit=9.0 -c pytorch-y

3. Clone this repository. 

4. Start the download process for the data. It would take a lot, so in mean time you can run the code.

5. Because the code here does not depend on competition data for training, only for submission. 

## Notebooks

1. [download-and-create-microtrain](https://github.com/ducha-aiki/google-retrieval-challenge-2019-fastai-starter/blob/master/download-and-create-microtrain.ipynb) - download all the aux data for training and validation

2. [validation-no-training](https://github.com/ducha-aiki/google-retrieval-challenge-2019-fastai-starter/blob/master/validation-no-training.ipynb) - playing with pretrained networks and setting up validation procedure

3. [training-validate-bad](https://github.com/ducha-aiki/google-retrieval-challenge-2019-fastai-starter/blob/master/training-validate-bad.ipynb) - training DenseNet121 on created micro-train in 45 min and playing with post-processing. It works as described, but just because of pure luck: lots of different "subclusters" == labels are depicting the same landmark. So, do not use it for training of all 19k subclusters

3. [training-validate-good-full](https://github.com/ducha-aiki/google-retrieval-challenge-2019-fastai-starter/blob/master/training-validate-good-full.ipynb) - Instead, use "clusters" as a labels, it gives much better results.

4. [submission-trained](https://github.com/ducha-aiki/google-retrieval-challenge-2019-fastai-starter/blob/master/submission-trained.ipynb) - creating a first submission. Warning, this could take a lot (~4-12 hours) because of the dataset size