์ฝ˜ํ…์ธ ๋กœ ๊ฑด๋„ˆ๋›ฐ๊ธฐ

Live Inference with Streamlit Application using Ultralytics YOLO11

์†Œ๊ฐœ

Streamlit makes it simple to build and deploy interactive web applications. Combining this with Ultralytics YOLO11 allows for real-time object detection and analysis directly in your browser. YOLO11 high accuracy and speed ensure seamless performance for live video streams, making it ideal for applications in security, retail, and beyond.



Watch: How to Use Streamlit with Ultralytics for Real-Time Computer Vision in Your Browser

์–‘์‹์—… ๋™๋ฌผ ์‚ฌ์œก
Fish Detection using Ultralytics YOLO11 Animals Detection using Ultralytics YOLO11
Fish Detection using Ultralytics YOLO11 Animals Detection using Ultralytics YOLO11

์‹ค์‹œ๊ฐ„ ์ถ”๋ก ์˜ ์žฅ์ 

  • Seamless Real-Time Object Detection: Streamlit combined with YOLO11 enables real-time object detection directly from your webcam feed. This allows for immediate analysis and insights, making it ideal for applications requiring instant feedback.
  • ์‚ฌ์šฉ์ž ์นœํ™”์ ์ธ ๋ฐฐํฌ: Streamlit์˜ ๋Œ€ํ™”ํ˜• ์ธํ„ฐํŽ˜์ด์Šค๋Š” ์ „๋ฌธ์ ์ธ ๊ธฐ์ˆ  ์ง€์‹ ์—†์ด๋„ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์„ ์‰ฝ๊ฒŒ ๋ฐฐํฌํ•˜๊ณ  ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•ด์ค๋‹ˆ๋‹ค. ์‚ฌ์šฉ์ž๋Š” ํด๋ฆญ ํ•œ ๋ฒˆ์œผ๋กœ ์‹ค์‹œ๊ฐ„ ์ถ”๋ก ์„ ์‹œ์ž‘ํ•  ์ˆ˜ ์žˆ์–ด ์ ‘๊ทผ์„ฑ๊ณผ ์‚ฌ์šฉ์„ฑ์ด ํ–ฅ์ƒ๋ฉ๋‹ˆ๋‹ค.
  • Efficient Resource Utilization: YOLO11 optimized algorithm ensure high-speed processing with minimal computational resources. This efficiency allows for smooth and reliable webcam inference even on standard hardware, making advanced computer vision accessible to a wider audience.

๊ฐ„์†Œํ™”๋œ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜ ์ฝ”๋“œ

Ultralytics ์„ค์น˜

์• ํ”Œ๋ฆฌ์ผ€์ด์…˜ ๋นŒ๋“œ๋ฅผ ์‹œ์ž‘ํ•˜๊ธฐ ์ „์— Ultralytics Python ํŒจํ‚ค์ง€๊ฐ€ ์„ค์น˜๋˜์–ด ์žˆ๋Š”์ง€ ํ™•์ธํ•˜์„ธ์š”. ๋‹ค์Œ ๋ช…๋ น์„ ์‚ฌ์šฉํ•˜์—ฌ ์„ค์น˜ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ultralytics

๊ฐ„์†Œํ™”๋œ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜

from ultralytics import solutions

solutions.inference()

### Make sure to run the file using command `streamlit run <file-name.py>`
yolo streamlit-predict

This will launch the Streamlit application in your default web browser. You will see the main title, subtitle, and the sidebar with configuration options. Select your desired YOLO11 model, set the confidence and NMS thresholds, and click the "Start" button to begin the real-time object detection.

Python ์—์„œ ํŠน์ • ๋ชจ๋ธ์„ ์„ ํƒ์ ์œผ๋กœ ์ œ๊ณตํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค:

์‚ฌ์šฉ์ž ์ง€์ • ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•œ ๊ฐ„์†Œํ™”๋œ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜

from ultralytics import solutions

# Pass a model as an argument
solutions.inference(model="path/to/model.pt")

### Make sure to run the file using command `streamlit run <file-name.py>`

๊ฒฐ๋ก 

By following this guide, you have successfully created a real-time object detection application using Streamlit and Ultralytics YOLO11. This application allows you to experience the power of YOLO11 in detecting objects through your webcam, with a user-friendly interface and the ability to stop the video stream at any time.

๋น„๋””์˜ค ์ŠคํŠธ๋ฆผ ๋…นํ™”, ์ฃผ์„์ด ๋‹ฌ๋ฆฐ ํ”„๋ ˆ์ž„ ์ €์žฅ, ๋‹ค๋ฅธ ์ปดํ“จํ„ฐ ๋น„์ „ ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ์™€์˜ ํ†ตํ•ฉ ๋“ฑ ๋” ๋งŽ์€ ๊ธฐ๋Šฅ์„ ์ถ”๊ฐ€ํ•˜์—ฌ ๊ฐœ์„ ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

์ปค๋ฎค๋‹ˆํ‹ฐ์™€ ์ƒ๊ฐ ๊ณต์œ 

์ปค๋ฎค๋‹ˆํ‹ฐ์— ์ฐธ์—ฌํ•˜์—ฌ ์ž์„ธํžˆ ์•Œ์•„๋ณด๊ณ , ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๊ณ , ํ”„๋กœ์ ํŠธ๋ฅผ ๊ณต์œ ํ•˜์„ธ์š”:

๋„์›€๋ง ๋ฐ ์ง€์›์„ ์ฐพ์„ ์ˆ˜ ์žˆ๋Š” ๊ณณ

  • GitHub ์ด์Šˆ: ์งˆ๋ฌธ, ๋ฒ„๊ทธ ์‹ ๊ณ , ๊ธฐ๋Šฅ ์ œ์•ˆ์„ ํ•˜๋ ค๋ฉด Ultralytics GitHub ๋ฆฌํฌ์ง€ํ† ๋ฆฌ๋ฅผ ๋ฐฉ๋ฌธํ•˜์„ธ์š”.
  • Ultralytics ๋””์Šค์ฝ”๋“œ ์„œ๋ฒ„: Ultralytics Discord ์„œ๋ฒ„์— ๊ฐ€์ž…ํ•˜์—ฌ ๋‹ค๋ฅธ ์‚ฌ์šฉ์ž ๋ฐ ๊ฐœ๋ฐœ์ž์™€ ์†Œํ†ตํ•˜๊ณ , ์ง€์›์„ ๋ฐ›๊ณ , ์ง€์‹์„ ๊ณต์œ ํ•˜๊ณ , ์•„์ด๋””์–ด๋ฅผ ๋ธŒ๋ ˆ์ธ์Šคํ† ๋ฐํ•˜์„ธ์š”.

๊ณต์‹ ๋ฌธ์„œ

  • Ultralytics YOLO11 Documentation: Refer to the official YOLO11 documentation for comprehensive guides and insights on various computer vision tasks and projects.

์ž์ฃผ ๋ฌป๋Š” ์งˆ๋ฌธ

How can I set up a real-time object detection application using Streamlit and Ultralytics YOLO11?

Setting up a real-time object detection application with Streamlit and Ultralytics YOLO11 is straightforward. First, ensure you have the Ultralytics Python package installed using:

pip install ultralytics

๊ทธ๋Ÿฐ ๋‹ค์Œ ๊ธฐ๋ณธ Streamlit ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์„ ๋งŒ๋“ค์–ด ์‹ค์‹œ๊ฐ„ ์ถ”๋ก ์„ ์‹คํ–‰ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค:

๊ฐ„์†Œํ™”๋œ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜

from ultralytics import solutions

solutions.inference()

### Make sure to run the file using command `streamlit run <file-name.py>`
yolo streamlit-predict

์‹ค์ œ ์„ค์ •์— ๋Œ€ํ•œ ์ž์„ธํ•œ ๋‚ด์šฉ์€ ๋ฌธ์„œ์˜ ์ŠคํŠธ๋ฆผ๋ฆฟ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜ ์ฝ”๋“œ ์„น์…˜์„ ์ฐธ์กฐํ•˜์„ธ์š”.

What are the main advantages of using Ultralytics YOLO11 with Streamlit for real-time object detection?

Using Ultralytics YOLO11 with Streamlit for real-time object detection offers several advantages:

  • Seamless Real-Time Detection: Achieve high-accuracy, real-time object detection directly from webcam feeds.
  • ์‚ฌ์šฉ์ž ์นœํ™”์ ์ธ ์ธํ„ฐํŽ˜์ด์Šค: Streamlit์˜ ์ง๊ด€์ ์ธ ์ธํ„ฐํŽ˜์ด์Šค๋Š” ์ „๋ฌธ์ ์ธ ๊ธฐ์ˆ  ์ง€์‹ ์—†์ด๋„ ์‰ฝ๊ฒŒ ์‚ฌ์šฉํ•˜๊ณ  ๋ฐฐํฌํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.
  • Resource Efficiency: YOLO11's optimized algorithms ensure high-speed processing with minimal computational resources.

์—ฌ๊ธฐ์—์„œ ์ด๋Ÿฌํ•œ ์ด์ ์— ๋Œ€ํ•ด ์ž์„ธํžˆ ์•Œ์•„๋ณด์„ธ์š”.

์›น ๋ธŒ๋ผ์šฐ์ €์— Streamlit ๊ฐ์ฒด ๊ฐ์ง€ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์„ ๋ฐฐํฌํ•˜๋ ค๋ฉด ์–ด๋–ป๊ฒŒ ํ•ด์•ผ ํ•˜๋‚˜์š”?

After coding your Streamlit application integrating Ultralytics YOLO11, you can deploy it by running:

streamlit run <file-name.py>

This command will launch the application in your default web browser, enabling you to select YOLO11 models, set confidence, and NMS thresholds, and start real-time object detection with a simple click. For a detailed guide, refer to the Streamlit Application Code section.

What are some use cases for real-time object detection using Streamlit and Ultralytics YOLO11?

Real-time object detection using Streamlit and Ultralytics YOLO11 can be applied in various sectors:

  • ๋ณด์•ˆ: ๋ฌด๋‹จ ์•ก์„ธ์Šค๋ฅผ ์‹ค์‹œ๊ฐ„์œผ๋กœ ๋ชจ๋‹ˆํ„ฐ๋งํ•ฉ๋‹ˆ๋‹ค.
  • ์†Œ๋งค์—… ๊ณ ๊ฐ ๊ณ„์‚ฐ, ์ง„์—ด๋Œ€ ๊ด€๋ฆฌ ๋“ฑ.
  • ์•ผ์ƒ๋™๋ฌผ๊ณผ ๋†์—…: ๋™๋ฌผ ๋ฐ ์ž‘๋ฌผ ์ƒํƒœ ๋ชจ๋‹ˆํ„ฐ๋ง

๋ณด๋‹ค ์‹ฌ์ธต์ ์ธ ์‚ฌ์šฉ ์‚ฌ๋ก€์™€ ์˜ˆ์‹œ๋Š” Ultralytics ์†”๋ฃจ์…˜์„ ์ฐธ์กฐํ•˜์„ธ์š”.

How does Ultralytics YOLO11 compare to other object detection models like YOLOv5 and RCNNs?

Ultralytics YOLO11 provides several enhancements over prior models like YOLOv5 and RCNNs:

  • ์†๋„ ๋ฐ ์ •ํ™•์„ฑ ํ–ฅ์ƒ: ์‹ค์‹œ๊ฐ„ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์˜ ์„ฑ๋Šฅ์ด ํ–ฅ์ƒ๋˜์—ˆ์Šต๋‹ˆ๋‹ค.
  • ์‚ฌ์šฉ ํŽธ์˜์„ฑ: ๊ฐ„์†Œํ™”๋œ ์ธํ„ฐํŽ˜์ด์Šค์™€ ๋ฐฐํฌ.
  • ๋ฆฌ์†Œ์Šค ํšจ์œจ์„ฑ: ์ตœ์†Œํ•œ์˜ ์—ฐ์‚ฐ ์š”๊ตฌ ์‚ฌํ•ญ์œผ๋กœ ๋” ๋น ๋ฅธ ์†๋„๋ฅผ ์œ„ํ•ด ์ตœ์ ํ™”๋˜์—ˆ์Šต๋‹ˆ๋‹ค.

For a comprehensive comparison, check Ultralytics YOLO11 Documentation and related blog posts discussing model performance.


๐Ÿ“… Created 3 months ago โœ๏ธ Updated 10 days ago

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