Semantic Segmentation using UNET

About the Dataset

This dataset contains a large number of car images (as .jpg files). Each car has exactly 16 images, each one taken at different angles.

For the training set, you are provided a .gif file that contains the manually cutout mask for each image. Link to download the dataset: Here

The UNET CNN architecture may be divided into the Encoder, Bottleneck and Decoder blocks, followed by a final segmentation output layer.

Encoder: There are 4 Encoder blocks, each consisting of a convolutional block followed by a Spatial Max Pooling layer.
Bottleneck: The Bottleneck consists of a single convolutional block.
Decoder: There are 4 Decoder blocks, each consisting of a deconvolution operation, followed by a convolutional block, along with skip connections.

Note: The convolutional block consists of 2 conv2d operations each followed by a BatchNorm2d, finally followed by a ReLU activation.

Image preprocessing included augmentations like HorizontalFlip, VerticalFlip, Rotate.
Dataloader object was created for both training and test data
Training process was carried out for 10 epochs, using the Adam Optimizer with a Learning Rate 1e-4.
Validation was carried out using Dice Loss and Intersection over Union Loss.

To use the repo and run inferences, please follow the guidelines below

Cloning the Repository:

  $ git clone https://github.com/anushka17agarwal/Segmentation_UNET

Entering the directory:
```
  $ cd unet/
```
Setting up the Python Environment with dependencies:
```
  $ pip install -r requirements.txt
```
Running the file for inference:
```
  $ python3 train.py
```