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---
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
[](https://huggingface.co/spaces/dblasko/mirnet-low-light-img-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 page 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_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_path = hf_hub_download(
repo_id="dblasko/mirnet-low-light-img-enhancement", filename="mirnet_finetuned.pth"
)
# 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)
if img_tensor.shape[2] % 8 != 0:
img_tensor = img_tensor[:, :, : -(img_tensor.shape[2] % 8), :]
if img_tensor.shape[3] % 8 != 0:
img_tensor = img_tensor[:, :, :, : -(img_tensor.shape[3] % 8)]
output = model(img_tensor)
```
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