Comments (2)
π Hello @etfgvsetaasassqaf, 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.
If this is a π Bug Report, please provide a minimum reproducible example to help us debug it.
If this is a custom training β Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results.
Requirements
Python>=3.8.0 with all requirements.txt installed including PyTorch>=1.8. To get started:
git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # install
Environments
YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
- Notebooks with free GPU:
- Google Cloud Deep Learning VM. See GCP Quickstart Guide
- Amazon Deep Learning AMI. See AWS Quickstart Guide
- Docker Image. See Docker Quickstart Guide
Status
If 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.
Introducing YOLOv8 π
We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - YOLOv8 π!
Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.
Check out our YOLOv8 Docs for details and get started with:
pip install ultralytics
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@ghost hello,
Thank you for your comment. It appears that your post is not related to the YOLOv5 project or any issues you might be experiencing with it. This repository is dedicated to the development and support of the YOLOv5 object detection model, and we encourage discussions and questions that are relevant to this topic.
If you have any questions or issues related to YOLOv5, please feel free to share them here. If you are experiencing a bug, kindly provide a minimum reproducible code example as outlined in our documentation. Additionally, ensure that you are using the latest versions of torch
and the YOLOv5 repository to see if the issue persists.
Thank you for understanding, and we look forward to assisting you with any YOLOv5-related inquiries you may have.
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Related Issues (20)
- yolov5 Youtube playback error HOT 5
- A Problem Concerning the Custom Dataset for Object Detection Using YOLOv5 HOT 7
- The necessity of improving the visual images of the detect results HOT 10
- θΏθ‘ python detect.py --source ./data/images/ --weights weights/yolov5s.pt ζ₯ι HOT 3
- what's going on yolo v5? HOT 4
- Model distillation for yolov5 HOT 20
- 'list' object has no attribute 'shape' HOT 3
- Train in colab and detect in my computer HOT 3
- YOLOv5 receptive range size HOT 8
- Yolov5s versus Yolov5s-cls HOT 4
- Edition!!**FREE Xbox Gift Card Codes [Updated] 50+ New Redeem Code 2024βHow to get Xbox Gift Cards FOR FREE HOT 1
- How can I test/evaluate my custom model and custom dataset when my model is loaded via torch.hub? HOT 5
- β―Today's!!~ Candy Crush Saga Hack - Get Free Gold In Candy Crush Saga 2024 iOS/Android HOT 1
- how to convert pt to onnx to trt HOT 7
- I observe that the validation phase is much slower than the training phase on large validation sets and multi-GPU machines HOT 6
- neck HOT 4
- How to increase FPS camera capture inside the Raspberry Pi 4B 8GB with best.onnx model HOT 13
- Mosaic HOT 5
- how to get mIoU and mPA in yolov5_seg? HOT 5
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