Simple repository containg exploratory models for the kaggle dogbreeds competition
https://www.kaggle.com/c/dog-breed-identification
All is done in python, using keras on tensorflow backend. As my laptop GPU was gathering dust lately, I decided to run all training on it. It should easily work on newer GPUs on GCP or AWS as well.
- Win10
- python 3.5
- keras, tensorflow, imgaug (for image augmentations)
- nvidia drivers and cuda toolkit is requiered for GPU support
Raw images should be downloaded from kaggle.
Train images are expected to be in data/train folder.
Test images are expected to be in data/test folder.
Submissions will be created into the submissions/ folder.
All notebooks go into the models/ folder.
Notebooks that start with 'bottleneck_extract' preprocess the images using pretrained imagenet models and pickle the extracted features. Notebooks that start with 'bottleneck_NN' are fully connected NNs that do the dogbreeds classification on the bottleneck features, not raw images.