Update README.md

README.md

@@ -15,8 +15,9 @@
  2. download the [COCO dataset](https://cocodataset.org/#home) (I use COCO 2017).
  3. download the [Set5, Set14, BSD100 test datasets](https://github.com/XPixelGroup/BasicSR/blob/master/docs/DatasetPreparation.md#common-image-sr-datasets)
  4. [create_data_lists.py](https://github.com/AndreiMoraru123/Super-Resolution/blob/main/create_data_lists.py) creates the JSON files for training and testing from the COCO image paths.
- 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).
+ 5. [architecture.py](https://github.com/AndreiMoraru123/Super-Resolution/blob/main/architecture.py) defines the training blueprints for both models (ResNet and GAN).
+ 6. [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).
+ 7. [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 running the [tests](https://github.com/AndreiMoraru123/Neural-Machine-Translation/tree/main/test).