Update README.md

README.md

@@ -45,5 +45,46 @@ If you use the Retinaface model in your research or application, please cite the
 }
 ```
 
+## Example Useage
+
+```python
+import os
+import torch
+import json
+from PIL import Image
+from huggingface_hub import hf_hub_download
+from feat.face_detectors.Retinaface.Retinaface_model import RetinaFace, postprocess_retinaface
+from feat.utils.io import get_resource_path, get_test_data_path
+from feat.utils.image_operations import convert_image_to_tensor, convert_color_vector_to_tensor
+
+device = 'cpu'
+
+# Download Model Weights and Config File
+face_config_file = hf_hub_download(
+    repo_id="py-feat/retinaface",
+    filename="config.json",
+    cache_dir=get_resource_path(),
+)
+with open(face_config_file, "r") as f:
+    face_config = json.load(f)
+    
+face_model_file = hf_hub_download(repo_id='py-feat/retinaface', 
+                                      filename="mobilenet0.25_Final.pth",
+                                      cache_dir=get_resource_path())
+face_checkpoint = torch.load(face_model_file, map_location=device, weights_only=True)
+face_detector = RetinaFace(cfg=face_config, phase="test")                
+face_detector.load_state_dict(face_checkpoint)
+face_detector.eval()
+face_detector.to(device)
+
+# Run Inference
+frame = Image.open(os.path.join(get_test_data_path(), "multi_face.jpg"))
+single_frame = torch.sub(frame, convert_color_vector_to_tensor(np.array([123, 117, 104])))
+predicted_locations, predicted_scores, predicted_landmarks = face_detector.forward(single_frame.to(device))
+face_output = postprocess_retinaface(predicted_locations, predicted_scores, predicted_landmarks, face_config, single_frame, device=device)
+
+
+```
+
 ## Acknowledgements
 We thank the contributors and the open-source community for their valuable support in developing this model. Special thanks to the authors of the original Retinaface paper, the WIDER FACE dataset, and biubug6 for sharing weights and code.