Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- qualcomm/RF-DETR
+pipeline_tag: object-detection
+tags:
+- surveillance
+- Threat_detection
+---
+
+# Transformers for Object detection
+
+### Inference Instructions
+
+```python
+import numpy as np
+import supervision as sv
+import torch
+import requests
+from PIL import Image
+import os
+
+from rfdetr import RFDETRNano
+
+THREAT_CLASSES = {
+    1: "Gun",
+    2: "Explosive", 
+    3: "Grenade",
+    4: "Knife"
+}
+
+image = Image.open("Path_to_image")
+
+# pre-trained weights
+weights_url = "https://huggingface.co/Subh775/Threat-Detection-Rf-Detr-v2/resolve/main/checkpoint_best_total.pth"
+weights_filename = "checkpoint_best_total.pth"
+
+# Download weights if not already present
+if not os.path.exists(weights_filename):
+    print(f"Downloading weights from {weights_url}")
+    response = requests.get(weights_url, stream=True)
+    response.raise_for_status()
+    with open(weights_filename, 'wb') as f:
+        for chunk in response.iter_content(chunk_size=8192):
+            f.write(chunk)
+    print("Download complete.")
+
+model = RFDETRNano(resolution=640, pretrain_weights=weights_filename)
+model.optimize_for_inference()
+
+detections = model.predict(image, threshold=0.5)
+
+color = sv.ColorPalette.from_hex([
+    "#1E90FF", "#32CD32", "#FF0000", "#FF8C00"
+])
+
+text_scale = sv.calculate_optimal_text_scale(resolution_wh=image.size)
+thickness = sv.calculate_optimal_line_thickness(resolution_wh=image.size)
+
+bbox_annotator = sv.BoxAnnotator(color=color, thickness=thickness)
+label_annotator = sv.LabelAnnotator(
+    color=color,
+    text_color=sv.Color.BLACK,
+    text_scale=text_scale,
+    smart_position=True
+)
+
+labels = []
+for class_id, confidence in zip(detections.class_id, detections.confidence):
+    class_name = THREAT_CLASSES.get(class_id, f"unknown_class_{class_id}")
+    labels.append(f"{class_name} {confidence:.2f}")
+
+annotated_image = image.copy()
+annotated_image = bbox_annotator.annotate(annotated_image, detections)
+annotated_image = label_annotator.annotate(annotated_image, detections, labels)
+annotated_image.thumbnail((800, 800))
+annotated_image
+```