Comments (3)
👋 Hello @zyad630, 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 a minimum reproducible example to help us debug it.
If this is a custom training ❓ 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 🚀
We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - YOLOv8 🚀!
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
from yolov5.
Hey there! 🚀 It looks like your script is unable to locate the models
module which is typically part of the directory structure. One common cause is not being in the correct directory when running your Jupyter notebook or your environment not being set up properly.
Ensure that you're running the Jupyter Notebook in the root directory of your cloned yolov5
repository. Alternatively, you can adjust your sys path at the beginning of your script to include the root directory of YOLOv5:
import sys
sys.path.append('path_to_yolov5')
from models.experimental import attempt_load
# Your other import statements and code follow here
Make sure to replace 'path_to_yolov5'
with the path to your YOLOv5 directory. This adjustment tells Python to include your specified directory in its search for modules.
Hope this helps! Let me know if there's anything else you need. 😊
from yolov5.
👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.
For additional resources and information, please see the links below:
- Docs: https://docs.ultralytics.com
- HUB: https://hub.ultralytics.com
- Community: https://community.ultralytics.com
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 YOLO 🚀 and Vision AI ⭐
from yolov5.
Related Issues (20)
- 关于yolov5在mac设备上使用mps加速出现的各种问题 HOT 2
- RuntimeError: Caught RuntimeError in replica 0 on device 0 HOT 2
- How to modify the network structure of the YOLOv5 classification model HOT 4
- 'RandomSampler' object has no attribute 'set_epoch' HOT 2
- Hyperparameters explanation HOT 4
- Suppress torch.hub.load() Output HOT 8
- How can I save the detections Yolov5 makes when he's working with a camera source? HOT 4
- How to specify yolov5 to train multiple folders? HOT 1
- pulling out model's layer intermediates HOT 2
- Continuous training of a Ultralytics Model HOT 4
- Exporting trained yolov5 model (trained on custom dataset) to 'saved model' format changes the no. of classes and the name of classes to default coco128 values HOT 2
- more details about training procedure HOT 4
- divide the objects into small and large categories based on the size of the bonding boxes HOT 8
- Request for YOLOv5 v6.2 Source Code under GPL-3.0 License HOT 4
- What prevents me from using the AMP function? HOT 4
- What prevents me from using the AMP function? HOT 1
- What prevents me from using the AMP function? HOT 1
- Background annotation HOT 6
- Hi @7rkMnpl, HOT 2
- Multiple GPU Hyperparameter evolution HOT 5
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from yolov5.