https://github.com/kayoyin/greyclassifier

CS_IOC5008 Visual Recognition using Deep Learning: Image Classification for grey natural scene images with <4000 annotated data
https://github.com/kayoyin/greyclassifier

Last synced: 8 days ago
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CS_IOC5008 Visual Recognition using Deep Learning: Image Classification for grey natural scene images with <4000 annotated data

• Host: GitHub
• URL: https://github.com/kayoyin/greyclassifier
• Owner: kayoyin
• Created: 2019-10-17T13:37:16.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2019-10-17T13:41:06.000Z (about 5 years ago)
• Last Synced: 2023-08-23T15:14:42.620Z (about 1 year ago)
• Language: Python
• Homepage: https://towardsdatascience.com/latest-winning-techniques-for-kaggle-image-classification-with-limited-data-5259e7736327
• Size: 50.4 MB
• Stars: 40
• Watchers: 5
• Forks: 10
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# GreyClassifier

This repository gathers the code for greyscale natural image classification from the [in-class Kaggle challenge](https://www.kaggle.com/c/cs-ioc5008-hw1).

## Getting started

First, create a new virtual environment

```

virtualenv venv -p python3

source venv/bin/activate

```

You might need to make sure your python3 link is ready by typing

```bash

which python3

```

Then install the development requirements

```bash

pip install -r requirements.txt

```

Install pretrained weights 

```bash

sh install_tools.sh

```

## Training the base classifiers

Training configuration can be specified in `src/configs.py`. 

To train a model for a specific subclass, simply uncomment the desired `SUBCLASS` in this file and change `LOGGER` to `rooms`,

`nature` or `urban`.

If you would like to train on single-channel images, you can set `GREY = True`.

Then, run:

```

python -m src.run

```

This will train the CNN model on the training and validation sets, then generate and save the concatenated outputs of the snapshot models in `xgbdata`.

## Training the XGB meta-learners

Make sure that `LOGGER` in `src/configs.py` is set to the same one you used to train your base classifier, and that `TRAIN = True`

Run:

```

python -m src.ensemble

```

This will train and save the XGBoost model weights.

## Ensemble prediction

First, set `TRAIN = False` in `src/configs.py`. 

Run:

```

python -m src.ensemble

```

This will save the testing predictions under `xgb.csv`.

## Future work:

- Add argument parsing so that the user does not have to edit the configuration file for each different run, and parameters can be passed as arguments instead