Update README.md

README.md

@@ -1,10 +1,48 @@
----
-tags:
-- model_hub_mixin
-- pytorch_model_hub_mixin
----
-
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Code: [More Information Needed]
-- Paper: [More Information Needed]
-- Docs: [More Information Needed]
+---
+tags:
+- curb ramp detection
+- accessibility
+license: mit
+datasets:
+- projectsidewalk/rampnet-dataset
+base_model:
+- timm/convnextv2_base.fcmae_ft_in22k_in1k_384
+pipeline_tag: object-detection
+---
+
+This is the curb ramp detection model introduced in *RampNet*.
+
+**Example usage:**
+```py
+import torch
+from transformers import AutoModel
+from PIL import Image
+import numpy as np
+from torchvision import transforms
+from skimage.feature import peak_local_max
+
+IMAGE_PATH = "example.jpg"
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+model = AutoModel.from_pretrained("projectsidewalk/rampnet-model", trust_remote_code=True).to(DEVICE).eval()
+
+preprocess = transforms.Compose([
+    transforms.Resize((2048, 4096), interpolation=transforms.InterpolationMode.BILINEAR),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+img = Image.open(IMAGE_PATH).convert("RGB")
+img_tensor = preprocess(img).unsqueeze(0).to(DEVICE)
+
+with torch.no_grad():
+    heatmap = model(img_tensor).squeeze().cpu().numpy()
+
+peaks = peak_local_max(np.clip(heatmap, 0, 1), min_distance=10, threshold_abs=0.5)
+scale_w = img.width / heatmap.shape[1]
+scale_h = img.height / heatmap.shape[0]
+coordinates = [(int(c * scale_w), int(r * scale_h)) for r, c in peaks]
+
+# Coordinates of detected curb ramps
+print(coordinates)
+```