Built a neural network model on the training set of ~6.5k images of 102 flower species using 3 Torchvision pre-trained models: VGG16, VGG19 and DenseNet121, achieved accuracy of 89% on the test set of ~800 records
The project is broken down into multiple steps:
- Load and preprocess the image dataset
- Train the image classifier on your dataset
- Use the trained classifier to predict image content
- Image Classifier Application Project using Pytorch.ipynb : The project file in Jupyter Notebook where most steps are conducted and validated
- train.py and predict.py: 2 main python functions to run the command line application.
- Helper functions:
- helper.py : Host functions to process images, view images, save and load checkpoint of the model
- TrainTestPredictFunc.py : Host functions to train, validate and predict images