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Co-authored-by: Rustem Galiullin <rustemgal@gmail.com> Co-authored-by: Rustem Galiullin <rustem.galiullin@bayanat.ai> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
36 lines
2.5 KiB
Markdown
36 lines
2.5 KiB
Markdown
## Models
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Welcome to the Ultralytics Models directory! Here you will find a wide variety of pre-configured model configuration files (`*.yaml`s) that can be used to create custom YOLO models. The models in this directory have been expertly crafted and fine-tuned by the Ultralytics team to provide the best performance for a wide range of object detection and image segmentation tasks.
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These model configurations cover a wide range of scenarios, from simple object detection to more complex tasks like instance segmentation and object tracking. They are also designed to run efficiently on a variety of hardware platforms, from CPUs to GPUs. Whether you are a seasoned machine learning practitioner or just getting started with YOLO, this 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 selected a model, you can use the provided `*.yaml` file to train and deploy your custom YOLO model with ease. See full details at the Ultralytics [Docs](https://docs.ultralytics.com/models), and if you need help or have any questions, feel free 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/usage/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("model.yaml") # build a YOLOv8n model from scratch
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# YOLO("model.pt") use pre-trained model if available
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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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## Pre-trained Model Architectures
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Ultralytics supports many model architectures. Visit https://docs.ultralytics.com/models to view detailed information and usage. Any of these models can be used by loading their configs or pretrained checkpoints if available.
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## Contributing New Models
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If you've developed a new model architecture or have improvements for existing models that you'd like to contribute to the Ultralytics community, please submit your contribution in a new Pull Request. For more details, visit our [Contributing Guide](https://docs.ultralytics.com/help/contributing).
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