Update README.md

README.md

@@ -11,6 +11,52 @@ tags:
 # 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)
+
+```