How to use Ultralytics: what you can do with it

3 use cases

• Detect objects in an image with one command

  Run a pretrained YOLO26n model on any image from the terminal.

  • REPO ultralytics/ultralytics
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  • Gemini CLI
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  Using ultralytics/ultralytics, install `pip install ultralytics`, run `yolo predict model=yolo26n.pt source='https://ultralytics.com/images/bus.jpg'` and then on <my image folder>, and summarise which objects were found in each image.

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• Train and validate a YOLO model on your dataset

  Fine-tune YOLO26n on a dataset config, then check its metrics on the validation set.

  • REPO ultralytics/ultralytics
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  • Gemini CLI
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  • Intermediate
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  With ultralytics/ultralytics, write a Python script that loads YOLO("yolo26n.pt"), trains on <my dataset yaml> for 100 epochs at imgsz 640 like the README example, runs model.val(), and prints the key metrics.

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• Export a YOLO model to ONNX for deployment

  Turn a trained YOLO model into an ONNX file you can run outside PyTorch.

  • REPO ultralytics/ultralytics
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  • Gemini CLI
  • Data and analytics
  • Intermediate
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  Using ultralytics/ultralytics, load <my trained weights>, export them with model.export(format="onnx"), and show me how to run the ONNX file on one image.

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Quick start with Ultralytics

1. Check you have what it needs: Python 3.8 or newer with pip (PyTorch is installed with the package requirements); A GPU is optional for prediction and recommended for training; Images or video to run on, or a dataset config for training.
2. Open ultralytics/ultralytics on GitHub and read the README and license.
3. Install with pip install ultralytics
4. Run yolo predict model=yolo26n.pt source='https://ultralytics.com/images/bus.jpg'
5. Replace the source with your own image path or URL
6. Add arguments like imgsz=640 to tune the run

These steps come from our top use case, Detect objects in an image with one command, which also has a copy-paste agent prompt.

Using the Deduped MCP, get the setup steps for ultralytics/ultralytics

claude mcp add --transport http deduped https://dedupedai.com/mcp

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About this repo

• Who it is forDevelopers and makers who need object detection or segmentation running quickly.
• Why it is hot63k stars, talked about in 2 AI sources we track
• LanguagePython
• Sourcegithub.com/ultralytics/ultralytics · Back to Hot repos

Requirements

• Python 3.8 or newer with pip (PyTorch is installed with the package requirements)
• A GPU is optional for prediction and recommended for training
• Images or video to run on, or a dataset config for training

Related repos

• Trended earlier REPO roboflow/supervision Supervision Supervision is Roboflow's model-agnostic Python toolkit for computer vision. It wraps the output of classification, detection and segmentation models from Ultralytics, Transformers, MMDetection, Inference or RF-DETR into one Detections object, then gives you annotators to draw results, zone counting and tracking tools, and dataset utilities to load, split, merge and convert YOLO, COCO and Pascal VOC datasets.