detect.py startup time

[lucas-mior]

Hey, is it possible to make detect.py have a shorter startup time?
I need to run it on a per image basis, that's why I consider 5 seconds startup time a bit too much (not detection per se, which runs very fast).
Thanks.

[github-actions]

👋 Hello @lucas-mior, 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]

@lucas-mior 👋 Hello! Thanks for asking about handling inference results. 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!

[burakocamis]

@glenn-jocher in order to run per image basis offline (downloading source files beforehand on local pc), how should "model" be defined below?

model = torch.hub.load('ultralytics/yolov5', 'custom', 'path/to/best.pt')

Thanks in advance,

[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.

Access additional YOLOv5 🚀 resources:

• Wiki – https://docs.ultralytics.com/yolov5
• Tutorials – https://docs.ultralytics.com/yolov5
• Docs – https://docs.ultralytics.com

Access additional Ultralytics ⚡ resources:

• Ultralytics HUB – https://ultralytics.com/hub
• Vision API – https://ultralytics.com/yolov5
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• Join Our Team – https://ultralytics.com/work
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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 ⭐!

[Tripura-Rajavarapu]

I have been facing the same issue. Can we reduce the start up time of detect.py as the solution is being used for one image at a time and 5 sec is pretty huge for every run.

[glenn-jocher]

@Tripura-Rajavarapu thank you for bringing up this issue. We understand that the startup time of detect.py can be quite long for running it on a per-image basis. Reducing the startup time is definitely a valid concern.

The Ultralytics YOLOv5 repository aims to provide efficient and optimized object detection models. While we continuously work on improving the performance of the codebase, including the startup time, please keep in mind that the startup time may vary depending on the hardware and other factors.

We appreciate your patience and understanding. If you have any further questions or concerns, please feel free to ask.