Dog Breeds Classification

This repo is intended to contain a set of scripts and data for reproducing dog breed classification model training, analysis, and inference.

Prerequisites

1. Install Python 2.7
2. Install all required python dependencies: pip install -r requirements.txt

Download Data

1. cd to this repo root directory
2. Execute setup script: sh ./setup/setup.sh. In its turn the script executes the following other scripts:
   • Creates all required directories: sh ./create_dirs.sh
   • Downloads Google Inception model: sh ./inception_download.sh. The link to the frozen TensorFlow model is taken from here
   • Downloads Stanford Dogs Dataset: sh ./download_stanford_dogs_dataset.sh

Prepare Data

1. Convert downloaded Stanford Dogs Dataset to TensorFlow friendly TFRecords file: python -m src.data_preparation.stanford_ds_to_tfrecords

Train

This section describes how to build dog breed classification dense neural network model on top of the pre-trained by Google deep neural network (namely Inception model).

1. Give a name to the model your are going to train by assigning a name to CURRENT_MODEL_NAME variable in consts.py script
2. Configure number of layers and number of units in each layer by setting HEAD_MODEL_LAYERS variable in consts.py
3. Train the model: python -m src.training.train. It might take 25-35 minutes depending on the depth of your model and number of epochs (which could be configured in the train.py script itself). TensorBoard could be used to observe the training process: tensorboard --logdir=./summary

Freeze Model

1. Freeze the model: python -m src.freezing.freeze. This will bind Inception model with the trained on the previous step "head" model and serialize it as a TensorFlow graph with variables represented as constants. This frozen model will be ready to use for classification tasks.

Analyze

1. Produce CSV file with predicted vs actual breed. This could be used to analyze precision on the training data e.g. plot a confusion matrix (see Confusion.ipynb). Result CSV file goes to metrics/training_confusion.csv: python -m src.analysis.training_perf_analysis

Infer

1. Use the model frozen on the previous step to classify an image either available on the filesystem or downloadable as an HTTP resource:

python -m src.inference.classify uri https://raw.githubusercontent.com/stormy-ua/dog-breeds-classification/master/images/shih-tzu.jpg

python -m src.inference.classify file images/airedale.jpg

In my case the model was training for 25 mins (5000 epochs) and the following sample classification outputs were produced:

(Image is taken from http://dogtime.com/dog-breeds/airedale-terrier)

(Image is taken from http://dogtime.com/dog-breeds/shih-tzu)

These examples also available in the Inference.ipynb Python notebook.

Have a fun!

Running in Docker

The already pre-built model w/ all the scripts and Python notebooks for inference are available in the docker container. Run the container:

docker run -p 8888:8888 -p 6006:6006 kirillpanarin/dog_breed_classification

Navigate a browser to http://localhost:8888/notebooks/Inference.ipynb and you will be all set for doing inference against real-life dog images.

Kagle Dog Breed Classification Competition

This model ranked #3 in the Kaggle's Dog Breed Classification Challenge.