Comments (6)
ð Hello @mamdouhhz, thank you for your interest in YOLOv5 ð! Please visit our âïļ 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 ð Bug Report, please provide a minimum reproducible example to help us debug it.
If this is a custom training â 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 ð
We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - YOLOv8 ð!
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.
Hello there! ð
Yes, you're onto something with memory limitations. The --imgsz
parameter in YOLOv5 specifies the image size to resize your images to during training. When you set a very large --imgsz
, such as 2880, it significantly increases GPU memory requirements. Your training likely doesn't start with --imgsz 2880
due to insufficient GPU memory to handle such large images.
A workaround is to use a smaller --imgsz
that fits within your GPU's memory limits. You can experiment starting with lower sizes and gradually increasing until you find the maximum size that works for your setup. Additionally, reducing the batch size can help accommodate larger image sizes, as it also reduces memory consumption.
Feel free to consult the docs for more insights on managing resource usage during training.
Let us know if you have any more questions. Happy training!
from yolov5.
Related Issues (20)
- Example "detect.py" get somesthing wrong HOT 3
- Extremely low precision but high mAP HOT 2
- Can yolov5 use as a part of commercial project , if so do we need to open-source the code or the whole project ? HOT 8
- ValueError: not enough values to unpack (expected 3, got 0) YOLOv5_obb HOT 5
- æåčŪįŧéåšĶ HOT 1
- RuntimeError: The size of tensor a (24) must match the size of tensor b (20) at non-singleton dimension 2 HOT 5
- How to show count in screen using yolov5 HOT 6
- How to change annotations indices in memory without changing the dataset locally? HOT 3
- How to add a button inside the video stream of yolov5. HOT 1
- Extract feature vector from the bounding box predicted together with the coordinates and class output vector HOT 5
- augmentation in validation HOT 1
- About detect.py HOT 9
- How to close window in yolov5 detection HOT 1
- Training YoloV5n on a custom dataset, best.pt is bigger than yolov5n official size HOT 4
- Data Augmentation HOT 1
- about eval.py HOT 1
- Need advice for training a YOLOv5-obb model HOT 2
- Code doubts about the model in the detection process HOT 2
- predicting from 2D array HOT 2
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
ð 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. ððð
-
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 âĪïļ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from yolov5.