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Speed detection #10510
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👋 Hello @OnurAlp808, 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. If this is a 🐛 Bug Report, please provide screenshots and minimum viable code to reproduce your issue, otherwise we can not help you. If this is a custom training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online W&B logging if available. For business inquiries or professional support requests please visit https://ultralytics.com or email [email protected]. RequirementsPython>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started: git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # install EnvironmentsYOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
StatusIf this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training, validation, inference, export and benchmarks on MacOS, Windows, and Ubuntu every 24 hours and on every commit. |
👋 Hello! Thanks for asking about inference speed issues. PyTorch Hub speeds will vary by hardware, software, model, inference settings, etc. Our default example in Colab with a V100 looks like this: YOLOv5 🚀 can be run on CPU (i.e. detect.py inferencepython detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images/ YOLOv5 PyTorch Hub inferenceimport torch
# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
# Images
dir = 'https://ultralytics.com/images/'
imgs = [dir + f for f in ('zidane.jpg', 'bus.jpg')] # batch of images
# Inference
results = model(imgs)
results.print() # or .show(), .save()
# Speed: 631.5ms pre-process, 19.2ms inference, 1.6ms NMS per image at shape (2, 3, 640, 640) Increase SpeedsIf you would like to increase your inference speed some options are:
Good luck 🍀 and let us know if you have any other questions! |
Thanks for the response. However, I guess I couldnt explain it very well though. I am using yolo to detect a specific object(ball). Moreover, I need to measure its velocity and need to check whether it's in or out for a certain area (i.e. football and a goal line). Other than that, how can I reduce the detectable objects that yolo is recognising ?. |
@Draugsleip 👋 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 Simple Inference ExampleThis example loads a pretrained YOLOv5s model from PyTorch Hub as 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! |
Is there any application for speed detection by changing the detect.py file or in any other way ?. Also, how to use yolov5 to check if an object is in/out for certain area.
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