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import gradio as gr
from ultralytics import YOLOv10
import supervision as sv
import spaces
from huggingface_hub import hf_hub_download
def download_models(model_id):
hf_hub_download("kadirnar/yolov10", filename=f"{model_id}", local_dir=f"./")
return f"./{model_id}"
MODEL_PATH = 'yolov10n.pt'
model = YOLOv10(MODEL_PATH)
box_annotator = sv.BoxAnnotator()
@spaces.GPU(duration=200)
def yolov10_inference(image, model_id, image_size, conf_threshold, iou_threshold):
model_path = download_models(model_id)
results = model(source=image, imgsz=image_size, iou=iou_threshold, conf=conf_threshold, verbose=False)[0]
detections = sv.Detections.from_ultralytics(results)
labels = [
f"{model.model.names[class_id]} {confidence:.2f}"
for class_id, confidence in zip(detections.class_id, detections.confidence)
]
annotated_image = box_annotator.annotate(image, detections=detections, labels=labels)
return annotated_image
def app():
with gr.Blocks():
with gr.Row():
with gr.Column():
image = gr.Image(type="numpy", label="Image")
model_id = gr.Dropdown(
label="Model",
choices=[
"yolov10n.pt",
"yolov10s.pt",
"yolov10m.pt",
"yolov10b.pt",
"yolov10x.pt",
],
value="yolov10s.pt",
)
image_size = gr.Slider(
label="Image Size",
minimum=320,
maximum=1280,
step=32,
value=640,
)
conf_threshold = gr.Slider(
label="Confidence Threshold",
minimum=0.1,
maximum=1.0,
step=0.1,
value=0.25,
)
iou_threshold = gr.Slider(
label="IoU Threshold",
minimum=0.1,
maximum=1.0,
step=0.1,
value=0.45,
)
yolov10_infer = gr.Button(value="Detect Objects")
with gr.Column():
output_image = gr.Image(type="numpy", label="Annotated Image")
yolov10_infer.click(
fn=yolov10_inference,
inputs=[
image,
model_id,
image_size,
conf_threshold,
iou_threshold,
],
outputs=[output_image],
)
gr.Examples(
examples=[
[
"huggingface.jpg",
"yolov10m.pt",
640,
0.25,
0.45,
],
[
"zidane.jpg",
"yolov10b.pt",
640,
0.25,
0.45,
],
],
fn=yolov10_inference,
inputs=[
image,
model_id,
image_size,
conf_threshold,
iou_threshold,
],
outputs=[output_image],
cache_examples=True,
)
gradio_app = gr.Blocks()
with gradio_app:
gr.Markdown(
"""
# YOLOv10: State-of-the-Art Object Detection
"""
)
gr.Markdown(
"""
Detect objects in images using the YOLOv10 model. Select a pre-trained model, adjust the inference settings, and upload an image to see the detected objects.
"""
)
with gr.Row():
gr.Markdown(
"""
Follow me for more projects and updates:
- [Twitter](https://twitter.com/kadirnar_ai)
- [GitHub](https://github.com/kadirnar)
- [LinkedIn](https://www.linkedin.com/in/kadir-nar/)
- [HuggingFace](https://www.huggingface.co/kadirnar/)
"""
)
app()
gradio_app.launch(debug=True) |