-
-
Notifications
You must be signed in to change notification settings - Fork 16.2k
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Segmentation Tutorial #9521
Segmentation Tutorial #9521
Conversation
Pulling ultralytics master
@paulguerrie awesome, looks much better :) |
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
How to run a inference just like object detection? |
@Sarveshsh8 yes, for segmentation you can run inference by loading a model and passing inputs through it. Here's an example using the YOLOv5 model: import torch
from PIL import Image
# Load the model
model = torch.hub.load('ultralytics/yolov5', 'custom', path_or_model='best.pt')
# Load an image
img = Image.open('path/to/image.jpg')
# Perform inference
results = model(img)
# Display results
results.show() For more detailed usage, check out the official Ultralytics YOLOv5 documentation. |
Added a
tutorial.ipynb
tosegment/
to demonstrate training, validation, and prediction for YOLOv5 Segmentation.🛠️ PR Summary
Made with ❤️ by Ultralytics Actions
🌟 Summary
New Jupyter notebook tutorial for YOLOv5 segmentation capabilities.
📊 Key Changes
tutorial.ipynb
) for YOLOv5 segmentation.🎯 Purpose & Impact