Comments (5)
👋 Hello @218w1d7706, thank you for your interest in Ultralytics YOLOv8 🚀! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.
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.
Join the vibrant Ultralytics Discord 🎧 community for real-time conversations and collaborations. This platform offers a perfect space to inquire, showcase your work, and connect with fellow Ultralytics users.
Install
Pip install the ultralytics
package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.
pip install ultralytics
Environments
YOLOv8 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 Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.
from ultralytics.
Hello! For quantizing a custom-trained YOLOv8 model, you can use the export functionality with INT8 quantization. Here's a simple example on how to export your model to TensorRT format with INT8 precision:
yolo export model=path/to/your/custom_model.pt format=engine int8=True
Make sure to perform this on the same device you plan to deploy the model, as INT8 calibration is device-specific. For more detailed guidance, you can refer to the TensorRT integration documentation provided by Ultralytics.
If you encounter any specific issues during this process, feel free to share them here for more targeted assistance! 🚀
from ultralytics.
I'm trying to reduce the loss, but try it
https://github.com/the0807/YOLOv8-ONNX-TensorRT
from ultralytics.
Hello! To reduce the loss during training, ensure your dataset is well-prepped and consider tweaking hyperparameters like learning rate or batch size. Also, using a pre-trained model can provide a good starting point. For specific adjustments in loss, reviewing the training logs to understand where the model might be underperforming can be helpful. If you're looking into using TensorRT for optimization, ensure your model is properly calibrated, especially when using INT8 precision. Good luck! 🚀
from ultralytics.
👋 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 ultralytics.
Related Issues (20)
- The task of recognizing a tree at different times. HOT 1
- Read the relevant data without the issue of images. HOT 10
- How to use the RTDETR model to train OBB tasks? HOT 9
- AttributeError: 'YOLOv10DetectionModel' object has no attribute 'args', when trying to access to v10DetectLoss. HOT 1
- About WARNING ⚠️ HOT 2
- Is that possible to move YOLOModel initialize predicting to GPU? HOT 2
- can I finetune YOLOV8-pose with a dataset without keypoints HOT 1
- How to use my own yolov8 model in the sagemaker? HOT 1
- Integrating a GAN into YOLOv8 Architecture HOT 3
- About DDP training HOT 16
- Yolov8 training crashing after installing CUDA and cudnn HOT 3
- Exporting yolov10n.pt int8 does not appear to fully quantize, so when compiling for edgetpu, very few operations are mapped to TPU. HOT 2
- using tracker slows down the process time HOT 2
- Training YOLO on thermal Images (Gray scale). HOT 6
- Why does yolov8 create multiple runs directories HOT 3
- Validation Losses are immediately increasing (detection) HOT 1
- Integrating YOLOv8n model for Real-Time Webcam Detection and Image Upload/Camera Capture in a Flutter Web Application HOT 5
- RuntimeError: shape '[1, 67, -1]' is invalid for input of size 38400 HOT 5
- Do SAM 2 negative point prompts work? HOT 3
- model.export to TFLite with int8 doesn't yield a fully int8 quantized model HOT 7
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 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. 📊📈🎉
-
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 ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from ultralytics.