The repository contains implementation of state of art super-resolution with GANs, the uses case of this project involves high resolution movie generation with low resolution input images. The more training over larger dataset can enable better GANs outputs.
├── LICENSE
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── Set5 <- Benchmark Dataset for inference
│ └── training <- Dataset used for training
│
├── models <- Contains Generator and Discriminator Model
│
├── notebooks <- Contains example notebook scripts to run training and inferencing
│
├── reports <- Generated Results/HR images
│ ├── inference_result<- Output of inferencing
│ └── training_results<- Generated during training
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── src <- Source code for use in this project.
│ │
│ │
│ ├── train <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── custom_dataset.py
│ │ ├── model.py
│ │ ├── infer.py
│ │ └── model_training.py
└─────────────────────────────────────────────────────────────────────────────────────────────────────────
Usage:
!git clone https://github.com/mahendra-gehlot/hawk-eye
set directory to hawk-eye
cd hawk-eye
Requirement Installation:
pip install -r requirements.txt
Inferencing:
infer.py [-h] input_directory input_resolution output_directory
positional arguments:
input_directory Directory for inference
input_resolution Input Resolution
output_directory Directory for output
optional arguments:
-h, --help show this help message and exit
python3 src/train/infer.py 'data/Set5/' 520 'reports/infer_result/'
ICAIML project (DS 226) Aug-Dec 2022
Team members:
- Mahendra Gehlot
- Shashank Kumar
- Vedant Gupta
- Sayan Chaudhuri
- Abhishek Karan