Pokemon-Image-Classifier

Its a convNet built upon InceptionV3 and trained on 928 pokemon classes. The model can predict any pokemon image till season 20 (Pokemon Sun and Moon)

Model details:
loss: 0.1279 - accuracy: 0.9743 - validation loss: 0.9940 - validation accuracy: 0.7917

Getting Started

Clone the repository to your local machine
Extract all the files into a directory
Run the Run_model.ipynb

Prerequisites

Dependencies :

• cv2
• Matplotlib
• Tensorflow - gpu
• keras
• pillow
• pickle
• os
• requests
• io

  import cv2
  import matplotlib.pyplot as plt
  from keras.optimizers import Adam
  from keras.models import Model,load_model
  from keras.applications.inception_v3 import InceptionV3
  from keras.layers import Dense,Input,GlobalMaxPooling2D
  from keras.preprocessing.image import ImageDataGenerator
  from keras.callbacks import EarlyStopping,ReduceLROnPlateau
  import matplotlib.pyplot as plt
  import os
  from keras.models import load_model
  from os import path
  from os import listdir
  import numpy as np
  import requests
  from PIL import Image
  from io import BytesIO
  import pickle

Installing

Cv2

$ pip install opencv-python (if you need only main modules)
$ pip install opencv-contrib-python (if you need both main and contrib modules)

Matplotlib

$ pip install matplotlib
If you are on Linux, you might prefer to use your package manager. Matplotlib is packaged for almost every major Linux distribution.
    Debian / Ubuntu: sudo apt-get install python3-matplotlib
    Fedora: sudo dnf install python3-matplotlib
    Red Hat: sudo yum install python3-matplotlib
    Arch: sudo pacman -S python-matplotlib

Tensorflow GPU

$ pip install tensorflow-gpu  # stable
$ pip install tf-nightly-gpu  # preview
You will also need to install CUDA drivers, for more details visit  
https://towardsdatascience.com/installing-tensorflow-with-cuda-cudnn-and-gpu-support-on-windows-10-60693e46e781

Keras

These installation steps assume that you are on a Linux or Mac environment. If you are on Windows, you will need to remove sudo to run the commands below.
$ sudo pip install keras  
$ pip install keras
Alternatively: install Keras from the GitHub source:
$ git clone https://github.com/keras-team/keras.git
Then, cd to the Keras folder and run the install command:
$ cd keras
$ sudo python setup.py install

pillow

$ pip install Pillow
$ easy_install Pillow
$ python setup.py install

Screen Shots :
Screenshot 1

Screenshot 2

Screenshot 3

Screenshot 4

Deployment

Training the model

Detailed instructions on how to train this model are described in the jupyter notebook attached (‘PokeClassifier_Train.ipynb‘).

Using pre-trained weights

If you want to download and use the weights of this trained model follow the link below.

https://drive.google.com/file/d/1Zai3RoV7L7mX1AlUqqYgUHb1VHuQ3N5A/view?usp=sharing

Using the Model

You can directly run the app to predict the pokemon in an image

Instructions :

1. Download the model.h5 from the link provided
   2(a). Download the pokemon_classes pickle file
   2(b). Download the dataset from the link provided (optional) to create the classes_list (skip this if you’re using the pickle file)
2. Either run Run_model.ipynb or Run_model.py
   Note : Change the location of files if required.

Other details

Python files for all the notebooks are also provided, if you need .py scripts for some reason.

Contributing

Please read CODE_OF_CONDUCT.md for details on our code of conduct, and the process for submitting pull requests to us.

Authors

• Abdul Ahad Siddiqui - Initial work - Abdul Ahad Siddiqui

License

This project is licensed under the MIT License - see the LICENSE.md file for details