# run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}.pt' imgsz=160 verbose=0.26
# run: coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}.pt' imgsz=160 verbose=0.318
- name:Benchmark SegmentationModel
- name:Benchmark SegmentationModel
shell:bash
shell:bash
run:coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-seg.pt' imgsz=160 verbose=0.30
run:coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-seg.pt' imgsz=160 verbose=0.286
- name:Benchmark ClassificationModel
- name:Benchmark ClassificationModel
shell:bash
shell:bash
run:coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-cls.pt' imgsz=160 verbose=0.16
run:coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-cls.pt' imgsz=160 verbose=0.166
- name:Benchmark PoseModel
- name:Benchmark PoseModel
shell:bash
shell:bash
run:coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-pose.pt' imgsz=160 verbose=0.17
run:coverage run -a --source=ultralytics -m ultralytics.cfg.__init__ benchmark model='path with spaces/${{ matrix.model }}-pose.pt' imgsz=160 verbose=0.185
Welcome to the Ultralytics Models directory! Here you will find a wide variety of pre-configured model configuration
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.
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.
These model configurations cover a wide range of scenarios, from simple object detection to more complex tasks like
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.
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.
To get started, simply browse through the models in this directory and find one that best suits your needs. Once you've
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!
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!
### Usage
### Usage
@ -37,8 +28,7 @@ model.train(data="coco128.yaml", epochs=100) # train the model
## Pre-trained Model Architectures
## Pre-trained Model Architectures
Ultralytics supports many model architectures. Visit https://docs.ultralytics.com/models to view detailed information
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.
and usage. Any of these models can be used by loading their configs or pretrained checkpoints if available.
By default, trackers will use the configuration in `ultralytics/cfg/trackers`.
By default, trackers will use the configuration in `ultralytics/cfg/trackers`. We also support using a modified tracker config file. Please refer to the tracker config files in `ultralytics/cfg/trackers`.
We also support using a modified tracker config file. Please refer to the tracker config files
in `ultralytics/cfg/trackers`.<br>
## Contributing New Trackers
If you've developed a new tracker architecture or have improvements for existing trackers 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).
Glenn Jocher
2 years ago
committed by
GitHub