multi stage detection

[pubpy2015]

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Question

Hi,

I have a vehicle detection model and vehicle color detection model.
First stage, detect vehicle on image
Second stage: detect vehicle color on vehicle image, which cropped from original image.
It works fine but if on original image has many vehicle, the detection time of second state will increase according to number of vehicle on original image.
I am trying using thread for second stage, using 1 thread per vehicle, it can run but detection time of each thread bigger many time.
Please advice in this case.

Additional

No response

[github-actions]

👋 Hello @pubpy2015, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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[glenn-jocher]

@pubpy2015 👋 hi, thanks for letting us know about this possible problem with YOLOv5 🚀. We've created a few short guidelines below to help users provide what we need in order to start investigating a possible problem.

How to create a Minimal, Reproducible Example

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If you believe your problem meets all the above criteria, please close this issue and raise a new one using the 🐛 Bug Report template with a minimum reproducible example to help us better understand and diagnose your problem.

Thank you! 😃

[pubpy2015]

Thanks for reply!

This is not problem, just is a question about multi stage detection or how to using multi thread with yolov5 ?

[glenn-jocher]

@pubpy2015 I think you should be able to load multiple models for threaded inference with PyTorch Hub. YOLOv5 🚀 PyTorch Hub models allow for simple model loading and inference in a pure python environment without using detect.py.

Simple Inference Example

This example loads a pretrained YOLOv5s model from PyTorch Hub as model and passes an image for inference. 'yolov5s' is the YOLOv5 'small' model. For details on all available models please see the README. Custom models can also be loaded, including custom trained PyTorch models and their exported variants, i.e. ONNX, TensorRT, TensorFlow, OpenVINO YOLOv5 models.

import torch

# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # yolov5n - yolov5x6 official model
#                                            'custom', 'path/to/best.pt')  # custom model

# Images
im = 'https://ultralytics.com/images/zidane.jpg'  # or file, Path, URL, PIL, OpenCV, numpy, list

# Inference
results = model(im)

# Results
results.print()  # or .show(), .save(), .crop(), .pandas(), etc.
results.xyxy[0]  # im predictions (tensor)

results.pandas().xyxy[0]  # im predictions (pandas)
#      xmin    ymin    xmax   ymax  confidence  class    name
# 0  749.50   43.50  1148.0  704.5    0.874023      0  person
# 2  114.75  195.75  1095.0  708.0    0.624512      0  person
# 3  986.00  304.00  1028.0  420.0    0.286865     27     tie

results.pandas().xyxy[0].value_counts('name')  # class counts (pandas)
# person    2
# tie       1

See YOLOv5 PyTorch Hub Tutorial for details.

Good luck 🍀 and let us know if you have any other questions!

[github-actions]

👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.

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Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLOv5 🚀 and Vision AI ⭐!

[devendraswamy]

@pubpy2015 can you please share the dataset details here , that will help me to fastest the process. thanking you in advance.

[glenn-jocher]

Hello @devendraswamy! We would love to help you with your dataset and training process, but we need more information. Could you please provide more details about your dataset, such as the format and contents of your images and labels, and the size of the dataset? Once we have a clearer picture, we can suggest some ways to streamline your training process. Thank you!

[israelmanzi]

Hey @pubpy2015 I have a similar situation as you've heard before! Have you figured out a way to approach this?

Thanks :)