Comments (7)
Hello @sky-fly97, thank you for your interest in our work! Please visit our Custom Training Tutorial to get started, and see our Google Colab Notebook, Docker Image, and GCP Quickstart Guide for example environments.
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 model or data training question, please note that Ultralytics does not provide free personal support. As a leader in vision ML and AI, we do offer professional consulting, from simple expert advice up to delivery of fully customized, end-to-end production solutions for our clients, such as:
- Cloud-based AI surveillance systems operating on hundreds of HD video streams in realtime.
- Edge AI integrated into custom iOS and Android apps for realtime 30 FPS video inference.
- Custom data training, hyperparameter evolution, and model exportation to any destination.
For more information please visit https://www.ultralytics.com.
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@sky-fly97 we are continuously researching speed and accuracy improvements. If you have proven ideas that show quantitative improvement we'd be happy to integrate them into our work.
If you have GPU resources you'd like to contribute to the research we can send you docker files to run as well, which will speed up our research and lead to improvements faster.
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@sky-fly97 new models have been released yesterday which are smaller and faster. See the readme table for updated speeds.
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@sky-fly97 new models have been released yesterday which are smaller and faster. See the readme table for updated speeds.
Wow, Thank you for your efforts!
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speed of the smallest model inference on cpu is 3 fps? could you please give me some advice on how to speed up?
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@sljlp I think for any ML model in object detection, CPU performance will always be quite slow. YOLOv5s is faster on CPU than efficientdet D0, but as you see it still does not compare to GPU speeds, or neural engines like Apple's 5 TOPS ANE speed. These are really the only acceptable solutions if you want fast inference today.
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This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
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Related Issues (20)
- The learned data is recognized by YOLOV5's detect.py , but only one of them overlaps and is not recognized at the same time HOT 6
- Training with Larger Image results in 0 P, R, mAP HOT 5
- Ram usage HOT 14
- Ram Usage HOT 2
- Getting a ValueError when training using the high augmentation yaml file HOT 1
- How to convert segment json data labeled by labelme to yolo format? HOT 4
- Raspi5 Yolov5 HOT 2
- SAM or YOLOV5-seg? HOT 3
- YOLOv5 interface - predict problem HOT 2
- Yolov5 Bug in Raspi
- pytorch's nvidia gpu device is not recognized. HOT 3
- Inaccurate bounding boxes when detecting large images HOT 4
- PILLOW version too high causing bugs during model training and validation-PILLOW版本过高导致模型训练验证时发生bug HOT 5
- "The labels from detect.py do not give me the prediction results in the same order as the ground truth .txt file." HOT 2
- Get Scalar Validation Metrics HOT 2
- train yolov5 on kaggle erro HOT 3
- ultralytics>=8.0.232 does not install on Yolov5 HOT 7
- 能不能减少标注的工作量实现自动标注 HOT 9
- Why is background FP so high? HOT 4
- Extracting Features from Specific Layers of the YOLOv5x6 Model HOT 3
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