Update README.md

README.md

@@ -18,10 +18,15 @@
  5. [train.py](https://github.com/AndreiMoraru123/Super-Resolution/blob/main/train.py) runs the whole training pipeline with top-down logic found in the file. Everything is managed by  the `Trainer` from [trainer.py](https://github.com/AndreiMoraru123/Super-Resolution/blob/main/trainer.py).
  6. [resolve.py](https://github.com/AndreiMoraru123/Super-Resolution/blob/main/resolve.py) generates the super resolution images from a given high resolution image (the low resolution version to be solved is generated by down-sampling the given image) and evaluates the models using with `scikit-image`'s `peak_signal_noise_ratio` and `structural_similarity` using the `Evaluator` from [evaluator.py](https://github.com/AndreiMoraru123/Super-Resolution/blob/main/evaluator.py).
 
-The code itself is heavily commented and you can get a feel for how super-resolution models work by looking at the [tests](https://github.com/AndreiMoraru123/Neural-Machine-Translation/tree/main/test).
+The code itself is heavily commented and you can get a feel for how super-resolution models work by running the [tests](https://github.com/AndreiMoraru123/Neural-Machine-Translation/tree/main/test).
 
 ## Overfitting on one image
 
+- **Top-Left**: Bicupic Up-sampling
+- **Top-Right**: Super Resolution ResNet
+- **Bottom-Left**: Super Resolution GAN
+- **Bottom-Right**: Original High Resolution
+
 ![bird_resolved](https://github.com/AndreiMoraru123/Super-Resolution/assets/81184255/1429d7c7-96be-4737-be21-253ac5a09ed2)
 
 ```