https://github.com/romankornev/dogs-vs-cats-redux

99.7% accuracy solution for Dogs vs Cats Redux Kaggle competition
https://github.com/romankornev/dogs-vs-cats-redux

bottleneck bottleneck-features cat deep-learning dog ensemble ipython-notebook jupyter-notebook kaggle machine-learning notebook prediction xception

Last synced: about 1 month ago
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99.7% accuracy solution for Dogs vs Cats Redux Kaggle competition

• Host: GitHub
• URL: https://github.com/romankornev/dogs-vs-cats-redux
• Owner: RomanKornev
• Created: 2017-10-30T00:14:59.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2017-11-02T02:09:03.000Z (about 7 years ago)
• Last Synced: 2024-10-11T15:25:08.701Z (about 1 month ago)
• Topics: bottleneck, bottleneck-features, cat, deep-learning, dog, ensemble, ipython-notebook, jupyter-notebook, kaggle, machine-learning, notebook, prediction, xception
• Language: Jupyter Notebook
• Size: 18.5 MB
• Stars: 11
• Watchers: 2
• Forks: 7
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Dogs vs Cats

Predict whether a given image is of a cat or a dog with 99.7% accuracy. 

Kaggle: https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition

Partially based on [Fast.ai](http://course.fast.ai/) course.

# Intro

- [ ] TODO: The goals

- [ ] TODO: Though process and methods in creating the project

- [ ] TODO: Some bullet points with interesting observations

- [ ] TODO: Any interesting charts or diagrams

## Model info

Final prediction is made from an ensemble of Xception, ResNet50, Inception-ResNetV2.

Training is done using bottlenecks on a data augmented [x6] training set (138000 299x299x3 pictures).

Precomputed bottleneck features are feeded into 2 hidden dense layers [x2048] and 1 output softmax layer.

### Size

Training set size = 2.5GB. Model size = 1GB per model (saved precomputed weights).

### Speed

Time to precompute bottlenecks: 2hr per model on a GTX 770.

Time to train: 5sec/epoch with 10000 batch size.

### Error rates

Ensemble reaches 99.7% accuracy on the validation set (only 6/2000 incorrect).

0.03893 leaderboard score **(15th place from 1,314 teams)**.

# Requirements

* keras==2.0.9

* tensorflow==1.2.0

* pandas

* seaborn

* sklearn

# Data

## Download the data

    pip install kaggle-cli

    kg config -g -u  -p  -c dogs-vs-cats-redux-kernels-edition

    kg download

    7z x *.zip

I split the data into 23000 train and 2000 validation sets.

## File structure

```

dogs-vs-cats-redux

├── data ─── dogscats

│             ├── train

│             │    ├── cats

│             │    └── dogs

│             ├── valid

│             │    ├── cats

│             │    └── dogs

│             ├── test

│             │    └── unknown

│             ├── models

│             └── results

├── submissions

│   └── submission1.csv

├── dogs_cats_redux_ensemble.ipynb

└── README.md

```

# Usage

`dogs_cats_redux_ensemble.ipynb` for final model

`xception_example.py` - example of Xception prediction

`resnet50_example.py` - example of ResNet50 prediction

`inception_resnet_v2_example.py` - example of Inception-ResNet-V2 prediction