This project aims to classify images as Pokémon. When the input image is a Pokémon, the algorithm will respond the Pokémon name. When the input image is a human face or anything else, the algorithm will respond which Pokémon you lookalike.
Inspired by CNN Project: Dog Breed Classifier
- OpenCV
- PyTorch
- CNN
- Transfer Learning
- Image classification
- Colab
- Labeled Faces in the Wild Home. This dataset will be used to evaluate a human face detector.
- Pokemon Generation One. This dataset will be used to train and evaluate a Pokémon detector and a classifier of Pokémon species.
- CIFAR-100. This dataset will be used to train the Pokémon detector, providing examples not representing Pokémon.
# Assuming you have an image named "pika.jpg" in the current directory
docker run -v $PWD:/app bameza/bazema_pokemon:latest --image_path pika.jpgPython >= 3.6
virtualenv -p python3.8 venv && source venv/bin/activate
make install
make linter
bazema_pokemon --image_path pika.jpg- https://pytorch.org/tutorials
- https://towardsdatascience.com/how-to-train-an-image-classifier-in-pytorch-and-use-it-to-perform-basic-inference-on-single-images-99465a1e9bf5?gi=f8b2c41eddbc
- https://curiousily.com/posts/transfer-learning-for-image-classification-using-torchvision-pytorch-and-python/
- https://lionbridge.ai/articles/end-to-end-multiclass-image-classification-using-pytorch-and-transfer-learning/
- https://medium.com/@011ivand3r/pytorch-pokedex-70ad86f42568
- make docker image smaller