Инференс в реальном времени с приложением Streamlit и Ultralytics YOLO26#

@j3dd4h Hi, thanks for sharing the details. We currently don't support the rknn model, but you can adjust this check in code to make the application work for rknn models. We will add support for this soon.

Ok, got it

@j3dd4h opened the Pull request here: https://github.com/ultralytics/ultralytics/pull/22212

@NNQHuong the error you're encountering suggests a possible issue with your PyTorch installation. Please verify that PyTorch is properly installed and compatible with your Python version by running:

import torch
print(torch.__version__)

If the error persists, ensure you're using the latest version of PyTorch and Ultralytics with:

Regarding your CLI query, you can use your custom-trained YOLO11 model by specifying the model path directly, like so:

For detailed examples, check the documentation here. Let me know if additional assistance is required!

Hi @NNQHuong, thank you for sharing the error log! It appears that the issue might be related to your PyTorch version. You may need to upgrade or downgrade it to ensure everything works correctly.

However, if this still doesn't resolve the issue, please share your code here for further testing.

Thanks, Ultralytics Team!

Thanks for your infomative reply, it worked smoothly! I'm kind of new to this field anyway, about 6 months , I bet you can see it in my words haha, so thanks for your patience too!

The Inference class has no source argument: selecting "webcam" in the sidebar always opens camera index 0 (self.vid_file_name = 0 in ultralytics/solutions/streamlit_inference.py). To use a different camera, make it your system's default camera or change that index in the file:

inf = solutions.Inference(model="yolo26n.pt")
inf.inference()

Run it with streamlit run <file-name.py>.

@Vectorvin 您好!Streamlit 方案现在也支持导出的模型格式:除了 .pt,还可以把 .onnx、.torchscript、.mlpackage、.engine(TensorRT)、OpenVINO 或 RKNN 模型路径传给 solutions.Inference(model="yolo26n.engine")。导出步骤请参考导出文档。需要企业级部署支持请访问ultralytics.com/license。

@sathyanarayanaRP streamlit can receive input directly from a webcam or video file. For cameras that don't support RTSP, you can use USB or other compatible connections. The Streamlit app processes frames in real-time, allowing for seamless integration with various camera types. For more details, please refer to the Streamlit documentation.

@Cheikh-Ahmedand the Streamlit app has no source argument: choosing "webcam" in the sidebar always opens camera index 0 (self.vid_file_name = 0 in ultralytics/solutions/streamlit_inference.py). To use your external webcam, make it the system's default camera, or change that index to 1 (or the index of your webcam) in the file.

Run the app with yolo solutions inference, or with streamlit run <file-name.py> for your own script.

Hey ! Thanks lot for your response, but the code couldn't work with streamlit code. I pasted the following code on my Laptop anconda PowerShell Prompt:

yolo streamlit-predict solutions.inference(source=1)

Still, my Webcam can't run, but my Laptop 's Camera.

Also, I tried the below code in jupyter notebook, but the problem still persists.

from ultralytics import solutions

solutions.inference() solutions.inference(source=1)

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

Any help either from the Respectful Staff or others would be appreciated!!!

@Cheikh-Ahmedand the Streamlit app has no source argument: choosing "webcam" in the sidebar always opens camera index 0 (self.vid_file_name = 0 in ultralytics/solutions/streamlit_inference.py). To use an external webcam, set it as your system's default camera or change that index in the file. Run the app with yolo solutions inference, or streamlit run <file-name.py> for your own script. If issues persist, please check your system's camera settings.

@AdadAlShabab you can use the following code to run the Streamlit application for live inference:

from ultralytics import solutions

inf = solutions.Inference(model="yolo26n.pt")
inf.inference()

Run the file with the command streamlit run <file-name.py>. For more details, please refer to the Streamlit Live Inference Guide.

@Charan2128 i'm glad to hear the initial issue is resolved. Regarding the error with OpenCV when switching tasks (pose, seg, cls), please ensure you have the latest versions of both the Ultralytics package and OpenCV. You can update them using:

pip install --upgrade ultralytics opencv-python

If the issue persists, please provide more details about the error message and the specific function that is causing the problem. This will help us diagnose and address the issue more effectively. For further guidance, you can also refer to our Streamlit Live Inference Guide.

That was a Quick Replay and Quick fix ,Thanks for your kind support on my issues. The problem is fixed now.

@Charan2128 i'm glad to hear the initial issue is resolved. For the new issue with switching tasks and encountering OpenCV errors, please ensure you have the latest versions of both the Ultralytics package and OpenCV installed. You can update them using pip install --upgrade ultralytics opencv-python. If the problem persists, please provide the specific error message and function causing the issue so we can assist you further. For more detailed guidance, you can refer to our Streamlit Live Inference Guide.

@Charan2128 it looks like you're encountering an AttributeError because solutions.inference is not an attribute of the ultralytics.solutions module. Please ensure you are using the latest version of the Ultralytics package by running pip install --upgrade ultralytics. Then launch the Streamlit app directly from the CLI with yolo solutions inference, or from Python with solutions.Inference(model="yolo26n.pt").inference() run via streamlit run <file-name.py>. For more details, you can refer to our Streamlit Live Inference Guide.