Major Steps & Theory Involved--
- I have Downloaded UCMerced_LandUse dataset from github & split the folders for training & valiidation in ratio 0.8 & 0.2(customizable).
- I have used Alex-net as pretrained model for feature extraction from UCMerced_LandUse dataset. 3 Autoencoder architecture has been prepared according to use case.
- Then Denoising ayutoencoders have been used to introduce noise in dataset so that auto encoders can do training along with noise to make model robust.
- Adam as optimiser and training & validation accuracy as performace metric has been used.
- In 20 epochs accuracy for validation is around 90% which further increase till 95% on increasing number of epochs.