Upload 5 files

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+examples/placa_mercosul_1553295403391_v2_900x506.jpg filter=lfs diff=lfs merge=lfs -text
+examples/placas-vermelhas.jpg filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,43 @@
+import gradio as gr
+
+from ultralytics import ASSETS, YOLO
+
+model = YOLO("/content/license_plate_detector.pt")
+
+
+def predict_image(img, conf_threshold, iou_threshold):
+    """Predicts objects in an image using a YOLO11 model with adjustable confidence and IOU thresholds."""
+    results = model.predict(
+        source=img,
+        conf=conf_threshold,
+        iou=iou_threshold,
+        show_labels=True,
+        show_conf=True,
+        imgsz=640,
+    )
+
+    for r in results:
+        im_array = r.plot()
+        im = Image.fromarray(im_array[..., ::-1])
+
+    return im
+
+title='License Plate Detector 🚗'
+description='A license plate detector model fine-tuned from Ultralytics Yolov11'
+
+iface = gr.Interface(
+    fn=predict_image,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),
+        gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold"),
+        gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold"),
+    ],
+    outputs=gr.Image(type="pil", label="Result"),
+    title="Ultralytics Gradio",
+    description="Upload images for inference. The Ultralytics YOLO11n model is used by default.",
+    examples=examples,
+    flagging_mode='never'
+)
+
+if __name__ == "__main__":
+    iface.launch()

license_plate_detector(1).pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0341f5c741a385298512f9616852b59b029a177c2e0a06f22e4a57487fe9c067
+size 19182483