Flower Species Classifier using TensorFlow.

In this repo, I used TensorFlow to build VGG16 Neural Network and train it from scratch using the 102 Category Flower Dataset, a dataset consisting of 102 flower categories.

• categories_names.json: a json file conaining the flowers/categories names.
• prepocessing.py: the code used to preprocess the images.
• run_training.py: the code used to launch the training.
• test.py: the code used to test the model once it is trained.
• train.py: the code used to train the model.
• utils.py: a python file containing utils functions.
• vgg_16.py: the code used to build the VGG16 model.
• requirements.txt: a text file containing the needed packages to run the project.

1. Prepare the environment:
NB: Use python 3+ only.
Before anything, please install the requirements by running: pip3 install -r requirements.txt.

2. Prepare the data:
Download the 102 Category Flower Dataset available via this link.
Extract all the files into a flower_data/ directory.
The extracted data into flower_data/ should be organized as follows:
flower_data/ should contain three folders named train/, test/ and valid/.
Optional: you can convert all the dataset into npy file by uncommenting lines 33 and 34 of preprocessing.py.

3. Train the VGG16 model: (from scratch)
To train the model, run python3 run_training.py.
The trained model will be saved to a directory named model/.

4. Test the model:
To test your trained model, run python3 run_testing.py. Don't forget to change the image's and the model's paths.