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36 lines
1.8 KiB
36 lines
1.8 KiB
## 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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