A Web API for automatic background removal using Deep Learning. App is made using Flask and deployed on Heroku.


Model Details:

CNN Architecture - U-Net with Residual connections
Parameters - 2.2M
Trained on - 153,947 Images
validated on - 2693 Images
batch_size = 32
img_size = (256,256)
Trained for - 4 epochs 
Training time - 80min/epoch on GPUs by Google Colab.

Datasets used for training:

The model is trained using modified version of U-NET ( Architecture first presented by Olaf Ronneberger, Philipp Fischer, Thomas Brox in 2015.
I have added Residual skip connections in U-NET Model which makes it more robust.

I can't put model architecture here because of its huuge size. view here.


Training loss - .112
Validation loss - .134

Training accuracy - .941
Validation accuracy - .935
Training meanIOU - .43
Validation meanIOU - .43