add gradio demo

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wa22 2024-05-29 11:03:33 +08:00
parent 1f6d1fc7e6
commit f837da7c0a
3 changed files with 109 additions and 1 deletions

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@ -21,6 +21,8 @@ Over the past years, YOLOs have emerged as the predominant paradigm in the field
</details>
**UPDATES** 🔥
- 2024/05/29: We identify a bug in existing HuggingFace demos. Please use `gr.Image(type="pil", label="Image")` rather than ``gr.Image(type="numpy", label="Image")`` for prediction.
- 2024/05/29: Add the gradio demo for running the models locally. Thanks to [AK](https://x.com/_akhaliq)!
- 2024/05/27: Thanks to [sujanshresstha](sujanshresstha) for the integration with [DeepSORT](https://github.com/sujanshresstha/YOLOv10_DeepSORT.git)!
- 2024/05/27: We have updated the [checkpoints](https://github.com/THU-MIG/yolov10/releases/tag/v1.1) with other attributes, like class names, for ease of use.
- 2024/05/26: Thanks to [CVHub520](https://github.com/CVHub520) for the integration into [X-AnyLabeling](https://github.com/CVHub520/X-AnyLabeling)!
@ -47,6 +49,11 @@ conda activate yolov10
pip install -r requirements.txt
pip install -e .
```
## Demo
```
python app.py
# Please visit http://127.0.0.1:7860
```
## Validation
[`yolov10n.pt`](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10n.pt) [`yolov10s.pt`](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10s.pt) [`yolov10m.pt`](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10m.pt) [`yolov10b.pt`](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10b.pt) [`yolov10l.pt`](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10l.pt) [`yolov10x.pt`](https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10x.pt)

100
app.py Normal file
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@ -0,0 +1,100 @@
# Ackownledgement: https://huggingface.co/spaces/kadirnar/Yolov10/blob/main/app.py
# Thanks to @kadirnar
import gradio as gr
from ultralytics import YOLOv10
def yolov10_inference(image, model_path, image_size, conf_threshold):
model = YOLOv10(model_path)
model.predict(source=image, imgsz=image_size, conf=conf_threshold, save=True)
return model.predictor.plotted_img[:, :, ::-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.pt",
"yolov10s.pt",
"yolov10m.pt",
"yolov10b.pt",
"yolov10l.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=0.0,
maximum=1.0,
step=0.1,
value=0.25,
)
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,
],
outputs=[output_image],
)
gr.Examples(
examples=[
[
"ultralytics/assets/bus.jpg",
"yolov10s.pt",
640,
0.25,
],
[
"ultralytics/assets/zidane.jpg",
"yolov10s.pt",
640,
0.25,
],
],
fn=yolov10_inference,
inputs=[
image,
model_id,
image_size,
conf_threshold,
],
outputs=[output_image],
cache_examples=True,
)
gradio_app = gr.Blocks()
with gradio_app:
gr.HTML(
"""
<h1 style='text-align: center'>
YOLOv10: Real-Time End-to-End Object Detection
</h1>
""")
with gr.Row():
with gr.Column():
app()
gradio_app.launch(debug=True)

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@ -6,4 +6,5 @@ pycocotools==2.0.7
PyYAML==6.0.1
scipy==1.13.0
onnxsim==0.4.36
onnxruntime-gpu==1.18.0
onnxruntime-gpu==1.18.0
gradio==4.31.5