---
license: apache-2.0
datasets:
- geekyrakshit/LoL-Dataset
pipeline_tag: image-to-image
tags:
- image-enhancement
- computer-vision
- image-to-image
---
# MIRNet low-light image enhancement
MIRNet-based low-light image enhancer specialized on restoring dark images from events (concerts, parties, clubs...).

## Project source-code and further documentation
Documentation about pre-training, fine-tuning, model architecture, usage and all source code used for building and inference can be found in the [GitHub repository of the project](https://github.com/dblasko/low-light-event-img-enhancer/).
This currently stores the PyTorch model weights and model definition, a HuggingFace pipeline will be implemented in the future. 

## Using the model
To use the model, you need to have the `model` folder, that you can dowload from this repository as well as on [GitHub](https://github.com/dblasko/low-light-event-img-enhancer/), present in your project folder.

Then, the following code can be used to download the model weights from HuggingFace and load them in PyTorch for downstream use of the model:
```python
import torch
import torchvision.transforms as T
from PIL import Image
from huggingface_hub import hf_hub_url, cached_download
from model.MIRNet.model import MIRNet

device = (
    torch.device("cuda")
    if torch.cuda.is_available()
    else torch.device("mps")
    if torch.backends.mps.is_available()
    else torch.device("cpu")
)

# Download the model weights from the Hugging Face Hub
model_url = hf_hub_url(
    repo_id="dblasko/mirnet-low-light-img-enhancement", filename="mirnet_finetuned.pth" # or mirnet_pretrained.pth
)
model_path = cached_download(model_url)

# Load the model
model = MIRNet().to(device)
model.load_state_dict(torch.load(model_path, map_location=device)["model_state_dict"])

# Use the model, for example for inference on an image
model.eval()
with torch.no_grad():
    img = Image.open("image_path.png").convert("RGB")
    img_tensor = T.Compose(
        [
            T.Resize(400),  # Adjust image resizing depending on hardware
            T.ToTensor(),
            T.Normalize([0.0, 0.0, 0.0], [1.0, 1.0, 1.0]),
        ]
    )(img).unsqueeze(0)
    img_tensor = img_tensor.to(device)

    output = model(img_tensor)

```