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## Models
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Welcome to the Ultralytics Models directory! Here you will find a wide variety of pre-configured model configuration
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files (`*.yaml`s) that can be used to create custom YOLO models. The models in this directory have been expertly crafted
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and fine-tuned by the Ultralytics team to provide the best performance for a wide range of object detection and image
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segmentation tasks.
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These model configurations cover a wide range of scenarios, from simple object detection to more complex tasks like
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instance segmentation and object tracking. They are also designed to run efficiently on a variety of hardware platforms,
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from CPUs to GPUs. Whether you are a seasoned machine learning practitioner or just getting started with YOLO, this
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directory provides a great starting point for your custom model development needs.
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To get started, simply browse through the models in this directory and find one that best suits your needs. Once you've
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selected a model, you can use the provided `*.yaml` file to train and deploy your custom YOLO model with ease. See full
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details at the Ultralytics [Docs](https://docs.ultralytics.com), and if you need help or have any questions, feel free
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to reach out to the Ultralytics team for support. So, don't wait, start creating your custom YOLO model now!
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### Usage
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Model `*.yaml` files may be used directly in the Command Line Interface (CLI) with a `yolo` command:
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```bash
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yolo task=detect mode=train model=yolov8n.yaml data=coco128.yaml epochs=100
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```
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They may also be used directly in a Python environment, and accepts the same
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[arguments](https://docs.ultralytics.com/cfg/) as in the CLI example above:
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```python
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from ultralytics import YOLO
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model = YOLO("yolov8n.yaml") # build a YOLOv8n model from scratch
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model.info() # display model information
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model.train(data="coco128.yaml", epochs=100) # train the model
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```
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