What will you Learn in the Project?
- Loading the image data with ImageDataGenerator class
- Developing the CNN model and compiling and fitting it on the train set
- How to perform Data Augmentation?
- How to do transfer learning with pre-trained models such as ResNet50?
- Woking knowledge of Keras library
- Understanding of different convolution methods and operations i.e. Filter, kernels, padding, pooling
- Google Colab for training the model
- Matplotlib library for visualizing the loss & accuracy of the model
We will be performing the following tasks in this project:
Task-1: Import libraries and load the data with ImageDataGenerator class
Task-2: Build a vanilla[base] CNN Model
Task-3: Compile and fit the base model
Task-4: Plot the results of loss and accuracy of the base model
Task-5: Perform different data augmentation techniques on the train set
Task-6: Build a new model on augmented data and perform model evaluation
Task-7: Transfer Learning with ResNet50
Task-8: Build new ResNet Model and fit it on the dataset
Task-9: Inferencing on the test image with best performing model