Ultralytics Explorer#

@pooya-github the Ultralytics Explorer feature has been removed as of ultralytics>=8.3.12. To access similar or enhanced functionality, we recommend using Ultralytics Platform, our platform for dataset exploration and management. Let me know if you have further questions!

@mkutu hello!

Setting up YOLO26 with a custom dataset can be straightforward! You'd primarily need a data YAML file to describe your dataset structure and then use it in your training command. Here’s a basic guide to get you started:

1. Prepare your data: Organize your dataset into two folders (images and labels), with corresponding files named identically.

2. Create a YAML file: This file should specify the paths to your training and validation data, the number of classes, and class names. Here's an example:

   # custom_data.yaml
   train: path/to/train/images
   val: path/to/val/images
   nc: number_of_classes
   names: ['class1', 'class2', 'class3']

3. Start Training: Use the following command to train your model:

   yolo train data=custom_data.yaml model=yolo26n.yaml

For exploring and managing your datasets efficiently, you might consider using the Ultralytics Explorer (removed in ultralytics>=8.3.12; use ultralytics==8.3.11 or Ultralytics Platform). This tool offers features like semantic search, SQL queries, and even AI-based querying to derive insights from your data.

Hope this helps! If you have any more questions, feel free to ask.

Can you be more specific?

Hey there! 🌟

It sounds like you've got a pretty wide range of experience with various YOLO versions and even RT-DETR! For running AI using YOLO26 specifically, the process is smoothly integrated into the Ultralytics framework, much like the versions you've mentioned.

To get started, make sure you have Ultralytics YOLO26 installed. If not, you can do so via pip:

pip install ultralytics

And then, to run your AI model with YOLO26, you can simply use:

from ultralytics import YOLO

# Load the model
model = YOLO('yolo26n.pt')  # Here 'yolo26n.pt' is an example model. Replace it with your model path.

# Run inference
results = model('path_to_your_image_or_video')

Or, if you prefer using the command line:

yolo detect predict model=yolo26n.pt source=path_to_your_image_or_video

Remember to replace 'yolo26n.pt' and 'path_to_your_image_or_video' with the actual paths to your YOLO26 model and the image or video you want to run inference on. 🚀

Since you've used various versions before, the process should feel quite familiar. If there's more specific functionality from the Ultralytics Explorer that you're interested in integrating with YOLO26, such as semantic search or SQL queries, the installation command provided in the documentation will be your first step:

pip install ultralytics[explorer]

After that, you can launch the Ultralytics Explorer with:

yolo explorer

And dive into explore mode (Explorer requires ultralytics==8.3.11, as it was removed in ultralytics>=8.3.12)! For any further details or if you've got a unique use case in mind, feel free to share more specifics. Happy to help further. Happy coding! 🎉

Thanks a lot my brother

Note: Ultralytics Explorer was removed in ultralytics>=8.3.12; to use it, install ultralytics==8.3.11. Similar dataset exploration features are available in Ultralytics Platform.

@BABAFAREEDDUDEKULA hey there! 😄

Thanks for reaching out. If you're interested in diving into the Ultralytics Explorer to explore CV datasets with semantic search, SQL queries, vector similarity search, and more, it sounds like you're on the right track!

To get started, ensure you've got the Explorer installed via:

pip install ultralytics[explorer]

Once installed, you can jump straight into using the GUI by typing:

yolo explorer

This launches the GUI in your browser, where you can experiment with creating embeddings, conducting image searches, and even using SQL or natural language queries!

If you encounter any issues or have specific questions as you use the Ultralytics Explorer, feel free to drop them here. Happy exploring! 🌟

Note: Ultralytics Explorer was removed in ultralytics>=8.3.12; to use it, install ultralytics==8.3.11. Similar dataset exploration features are available in Ultralytics Platform.

To set up the Ultralytics Explorer to work on an AWS instance while you're connected via SSH from your local machine, you can use port forwarding to access the Explorer's web interface. Here's a concise step-by-step guide:

1. SSH into AWS with Port Forwarding: Use the -L option to forward a local port to a port on the AWS instance. Replace local_port with a port on your local machine (e.g., 8080), and aws_port with the port the Explorer runs on (usually 5000).

   ssh -i /path/to/your/key.pem -L local_port:localhost:aws_port user @AloshkaD

2. Run Explorer on AWS: Once connected, start the Explorer on the AWS instance:

   yolo explorer

3. Access Explorer Locally: Open a web browser on your local machine and navigate to localhost:local_port to access the Explorer interface.

Remember to replace /path/to/your/key.pem with the path to your AWS key, user with your AWS username, and aws_instance_ip with your AWS instance's IP address. Also, ensure that the AWS security group allows inbound traffic on the aws_port.

Worked with a minor tweak to reference my config file. Thank you!

@AloshkaD you're welcome! I'm glad to hear that you were able to get it working with your config file. If you have any more questions or need further assistance in the future, don't hesitate to reach out. Happy exploring with Ultralytics Explorer! 🚀😊