initial commit

README.md

@@ -1,8 +1,8 @@
 ---
-title: RF DETR
-emoji: 🏃
-colorFrom: blue
-colorTo: yellow
+title: RF-DETR
+emoji: 🔥
+colorFrom: yellow
+colorTo: pink
 sdk: gradio
 sdk_version: 5.22.0
 app_file: app.py

app.py

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+import gradio as gr
+import spaces
+import supervision as sv
+from rfdetr import RFDETRBase
+from rfdetr.util.coco_classes import COCO_CLASSES
+
+MARKDOWN = """
+# RF-DETR 🔥
+
+<div style="display: flex; align-items: center; gap: 8px;">
+  <a href="https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-finetune-rf-detr-on-detection-dataset.ipynb">
+    <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="colab" />
+  </a>
+  <a href="https://blog.roboflow.com/rf-detr">
+    <img src="https://raw.githubusercontent.com/roboflow-ai/notebooks/main/assets/badges/roboflow-blogpost.svg" alt="roboflow" />
+  </a>
+  <a href="https://github.com/roboflow/rf-detr">
+    <img src="https://badges.aleen42.com/src/github.svg" alt="roboflow" />
+  </a>
+</div>
+
+RF-DETR is a real-time, transformer-based object detection model architecture developed 
+by [Roboflow](https://roboflow.com/) and released under the Apache 2.0 license.
+"""
+
+COLOR = sv.ColorPalette.from_hex([
+    "#ffff00", "#ff9b00", "#ff8080", "#ff66b2", "#ff66ff", "#b266ff",
+    "#9999ff", "#3399ff", "#66ffff", "#33ff99", "#66ff66", "#99ff00"
+])
+
+MODEL = RFDETRBase()
+
+
+@spaces.GPU()
+def inference(image, confidence):
+    detections = MODEL.predict(image, threshold=confidence)
+
+    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 = [
+        f"{COCO_CLASSES[class_id]} {confidence:.2f}"
+        for class_id, confidence
+        in zip(detections.class_id, detections.confidence)
+    ]
+
+    annotated_image = image.copy()
+    annotated_image = bbox_annotator.annotate(annotated_image, detections)
+    annotated_image = label_annotator.annotate(annotated_image, detections, labels)
+    return annotated_image
+
+with gr.Blocks() as demo:
+    gr.Markdown(MARKDOWN)
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(
+                label="Input Image",
+                image_mode='RGB',
+                type='pil',
+                height=600
+            )
+            confidence_slider = gr.Slider(
+                label="Confidence",
+                minimum=0.0,
+                maximum=1.0,
+                step=0.05,
+                value=0.5,
+            )
+            submit_button = gr.Button("Submit")
+        with gr.Column():
+            output_image = gr.Image(
+                label="Input Image",
+                image_mode='RGB',
+                type='pil',
+                height=600
+            )
+
+        submit_button.click(
+            inference,
+            inputs=[input_image, confidence_slider],
+            outputs=output_image
+        )
+
+demo.launch(debug=False, show_error=True)

requirements.txt

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+gradio
+spaces
+rfdetr