Comments (3)
π Hello @karmakaragradwip02, 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
from yolov5.
Hi there,
Thank you for reaching out and providing detailed information about the issue you're encountering. It looks like the error is related to dependency conflicts in the packages installed via pip in your Google Colab environment.
To resolve this, you can try the following steps:
-
Upgrade pip: Ensure you have the latest version of pip.
!pip install --upgrade pip
-
Install specific versions of the conflicting packages: You can manually install the required versions of the conflicting packages to resolve the dependency issues.
!pip install requests==2.31.0 pillow==10.0.0
-
Reinstall YOLOv5 dependencies: After resolving the conflicts, reinstall the YOLOv5 dependencies.
!pip install -qr requirements.txt
Here is the complete code snippet you can run in your Colab notebook:
!git clone https://github.com/ultralytics/yolov5.git
%cd yolov5
!pip install --upgrade pip
!pip install requests==2.31.0 pillow==10.0.0
!pip install -qr requirements.txt
import torch
from IPython.display import Image, clear_output
print('Setup complete. Using torch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))
This should help resolve the dependency conflicts you're experiencing. If the issue persists, please ensure you are using the latest version of YOLOv5 and its dependencies.
Feel free to reach out if you have any further questions or run into any other issues. Happy coding! π
from yolov5.
π 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 π and Vision AI β
from yolov5.
Related Issues (20)
- What prevents me from using the AMP functionοΌ HOT 1
- Background annotation HOT 6
- Hi @7rkMnpl, HOT 2
- Multiple GPU Hyperparameter evolution HOT 5
- Marking YOLOv5 Detection Text Outputs with TP or FP HOT 4
- Multiple threads using yolov5 model concurrent inference failed HOT 4
- Detect head structure differs HOT 4
- runs\train\exp10 is not a directory HOT 12
- Similar mAP when splitting data into train, val and test HOT 4
- Syntax and understanding questions about reading tensorflow lite results HOT 1
- A Error which blast my mind.... HOT 1
- Yolov5 Int8 export in PyTorch HOT 10
- Video inference with YOLOv5 model in python HOT 3
- How to disable or add new scales of prediction? HOT 5
- Installation on Windows 7 32 bits HOT 3
- Artificial Neural Network - interpreting model.save output HOT 1
- Origin of warmup_bias_lr? HOT 3
- Silicon Mac GPU Support for training HOT 1
- Split features map of data HOT 1
- incorrect detections for cars after fine-tuning yolov5l HOT 1
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
π Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. πππ
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google β€οΈ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from yolov5.