Closed zinuok closed 1 year ago
π Hello @zinuok, thank you for your interest in YOLOv5 π! Please visit our βοΈ 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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Python>=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
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@zinuok torch ops do not run on CPU at FP16. This has nothing to do with YOLOv5.
π 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 π can be run on CPU (i.e. --device cpu
, slow) or GPU if available (i.e. --device 0
, faster). You can determine your inference device by viewing the YOLOv5 console output:
python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images/
import 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)
If you would like to increase your inference speed some options are:
--img-size
, i.e. 1280 -> 640 -> 320python detect.py --half
and python val.py --half
Good luck π and let us know if you have any other questions!
π 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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Question
Hello, I'm trying to use YOLOv5n with CPU.
I wanted to speed up model inference even more, so I applied the --half flag. As a result, the following error occurred. Is half-precision not supported in CPU mode?
Additional
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