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38 lines
1.9 KiB
38 lines
1.9 KiB
---
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comments: true
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description: Learn about the supported models and architectures, such as YOLOv3, YOLOv5, and YOLOv8, and how to contribute your own model to Ultralytics.
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---
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# Models
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Ultralytics supports many models and architectures with more to come in the future. Want to add your model architecture? [Here's](../help/contributing.md) how you can contribute.
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In this documentation, we provide information on four major models:
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1. [YOLOv3](./yolov3.md): The third iteration of the YOLO model family, known for its efficient real-time object detection capabilities.
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2. [YOLOv5](./yolov5.md): An improved version of the YOLO architecture, offering better performance and speed tradeoffs compared to previous versions.
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3. [YOLOv6](./yolov6.md): Released by [Meituan](https://about.meituan.com/) in 2022 and is in use in many of the company's autonomous delivery robots.
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3. [YOLOv8](./yolov8.md): The latest version of the YOLO family, featuring enhanced capabilities such as instance segmentation, pose/keypoints estimation, and classification.
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4. [Segment Anything Model (SAM)](./sam.md): Meta's Segment Anything Model (SAM).
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5. [Realtime Detection Transformers (RT-DETR)](./rtdetr.md): Baidu's RT-DETR model.
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You can use these models directly in the Command Line Interface (CLI) or in a Python environment. Below are examples of how to use the models with CLI and Python:
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## CLI Example
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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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## Python Example
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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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For more details on each model, their supported tasks, modes, and performance, please visit their respective documentation pages linked above. |