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import gradio as gr
import spaces
from PIL import Image
from ultralytics import YOLO
# Load Models
models = {
"yolov10n": YOLO("jameslahm/yolov10n"),
"yolov10s": YOLO("jameslahm/yolov10s"),
"yolov10m": YOLO("jameslahm/yolov10m"),
"yolov10b": YOLO("jameslahm/yolov10b"),
"yolov10l": YOLO("jameslahm/yolov10l"),
"yolov10x": YOLO("jameslahm/yolov10x"),
}
@spaces.GPU(duration=30)
def yolov10_inference(image, model_id, image_size, conf_threshold, iou_threshold):
model = models[model_id]
results = model.predict(
source=image,
imgsz=image_size,
conf=conf_threshold,
iou=iou_threshold,
)
annotated_image = results[0].plot()
return Image.fromarray(annotated_image[..., ::-1])
def app():
with gr.Blocks():
with gr.Row():
with gr.Column():
image = gr.Image(type="pil", label="Image")
model_id = gr.Dropdown(
label="Model",
choices=[
"yolov10n",
"yolov10s",
"yolov10m",
"yolov10b",
"yolov10l",
"yolov10x",
],
value="yolov10m",
)
image_size = gr.Slider(
label="Image Size",
minimum=320,
maximum=1280,
step=32,
value=640,
)
conf_threshold = gr.Slider(
label="Confidence Threshold",
minimum=0.0,
maximum=1.0,
step=0.05,
value=0.25,
)
iou_threshold = gr.Slider(
label="IoU Threshold",
minimum=0.0,
maximum=1.0,
step=0.05,
value=0.45,
)
yolov10_infer = gr.Button(value="Detect Objects")
with gr.Column():
output_image = gr.Image(type="pil", label="Annotated Image")
gr.Examples(
examples=[
["dog.jpeg", "yolov10m", 640, 0.25, 0.45],
["huggingface.jpg", "yolov10m", 640, 0.25, 0.45],
["zidane.jpg", "yolov10m", 640, 0.25, 0.45],
],
fn=yolov10_inference,
inputs=[image, model_id, image_size, conf_threshold, iou_threshold],
outputs=[output_image],
cache_examples='lazy',
)
yolov10_infer.click(
fn=yolov10_inference,
inputs=[image, model_id, image_size, conf_threshold, iou_threshold],
outputs=[output_image],
)
gradio_app = gr.Blocks()
with gradio_app:
gr.HTML(
"""
<h1 style='text-align: center'>
YOLOv10: Real-Time End-to-End Object Detection
</h1>
""")
gr.HTML(
"""
<h3 style='text-align: center'>
Follow me for more!
<a href='https://twitter.com/kadirnar_ai' target='_blank'>Twitter</a> | <a href='https://github.com/kadirnar' target='_blank'>Github</a> | <a href='https://www.linkedin.com/in/kadir-nar/' target='_blank'>Linkedin</a> | <a href='https://www.huggingface.co/kadirnar/' target='_blank'>HuggingFace</a>
</h3>
""")
with gr.Row():
with gr.Column():
image = gr.Image(type="pil", label="Image")
model_id = gr.Dropdown(
label="Model",
choices=[
"yolov10n",
"yolov10s",
"yolov10m",
"yolov10b",
"yolov10l",
"yolov10x",
],
value="yolov10m",
)
image_size = gr.Slider(
label="Image Size",
minimum=320,
maximum=1280,
step=32,
value=640,
)
conf_threshold = gr.Slider(
label="Confidence Threshold",
minimum=0.0,
maximum=1.0,
step=0.05,
value=0.25,
)
iou_threshold = gr.Slider(
label="IoU Threshold",
minimum=0.0,
maximum=1.0,
step=0.05,
value=0.45,
)
yolov10_infer = gr.Button(value="Detect Objects")
with gr.Column():
output_image = gr.Image(type="pil", label="Annotated Image")
gr.Examples(
examples=[
["dog.jpeg", "yolov10m", 640, 0.25, 0.45],
["huggingface.jpg", "yolov10m", 640, 0.25, 0.45],
["zidane.jpg", "yolov10m", 640, 0.25, 0.45],
],
fn=yolov10_inference,
inputs=[image, model_id, image_size, conf_threshold, iou_threshold],
outputs=[output_image],
cache_examples='lazy',
)
yolov10_infer.click(
fn=yolov10_inference,
inputs=[image, model_id, image_size, conf_threshold, iou_threshold],
outputs=[output_image],
)
if __name__ == "__main__":
gradio_app.launch() |