What will you Learn in the Project?
- Loading the image data with ImageDataGenerator class
- Developing 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 Vgg16?
Pre-requisite
- Woking knowledge of Keras libraryÂ
- Understanding of different convolution operations i.e. Filter, kernels, padding, pooling
Tools Used
- Google Colab for training the model
- Matplotlib library for visualizing the loss & accuracy of the modelÂ
- Keras[tensorflow]
Tasks to be Performed
We will be performing the following tasks as part of this project:
Task-1: Import the dataset and divide it into a train/test/validation split.
Task-2: Develop a fully connected vanilla(base) CNN model
Task-3: Compile the model and do training with Early StoppingÂ
Task-4: Validate the base model on the test set
Task-5: Perform data augmentation on train set Â
Task-6: Train and evaluate the updated model with augmented data
Task-7: Download the vgg16 model and freeze it’s all the weights
Task-8: Create a new model with vgg16
Task-9: Fit and evaluate updated vgg model
Task-10: Save the best model into Keras H5 format
Course Content
+3 enrolled Not Enrolled
Skills you will develop
Develop CNN models in Keras
Plot loss and accuracy using Matplotlib
Transfer learning in Keras
Model training and validation in Keras
Saving the trained model in Keras
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