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[](https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml)
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## Install
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```bash
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pip install ultralytics
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```
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Development
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```
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git clone https://github.com/ultralytics/ultralytics
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cd ultralytics
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pip install -e .
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```
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## Usage
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### 1. CLI
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To simply use the latest Ultralytics YOLO models
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```bash
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yolo task=detect mode=train model=yolov8n.yaml args=...
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classify predict yolov8n-cls.yaml args=...
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segment val yolov8n-seg.yaml args=...
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export yolov8n.pt format=onnx
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```
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### 2. Python SDK
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To use pythonic interface of Ultralytics YOLO model
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```python
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from ultralytics import YOLO
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model = YOLO("yolov8n.yaml") # create a new model from scratch
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model = YOLO(
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"yolov8n.pt"
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) # load a pretrained model (recommended for best training results)
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results = model.train(data="coco128.yaml", epochs=100, imgsz=640)
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results = model.val()
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results = model.predict(source="bus.jpg")
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success = model.export(format="onnx")
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```
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## Models
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| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU<br>(ms) | Speed<br><sup>T4 GPU<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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| ------------------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------- | ---------------------------- | ------------------ | ----------------- |
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| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5n.pt) | 640 | 28.0 | - | - | **1.9** | **4.5** |
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| [YOLOv6n](url) | 640 | 35.9 | - | - | 4.3 | 11.1 |
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| **[YOLOv8n](url)** | 640 | **37.5** | - | - | 3.2 | 8.9 |
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| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s.pt) | 640 | 37.4 | - | - | 7.2 | 16.5 |
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| [YOLOv6s](url) | 640 | 43.5 | - | - | 17.2 | 44.2 |
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| **[YOLOv8s](url)** | 640 | **44.7** | - | - | 11.2 | 28.8 |
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| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5m.pt) | 640 | 45.4 | - | - | 21.2 | 49.0 |
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| [YOLOv6m](url) | 640 | 49.5 | - | - | 34.3 | 82.2 |
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| **[YOLOv8m](url)** | 640 | **50.3** | - | - | 25.9 | 79.3 |
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| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5l.pt) | 640 | 49.0 | - | - | 46.5 | 109.1 |
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| [YOLOv6l](url) | 640 | 52.5 | - | - | 58.5 | 144.0 |
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| [YOLOv7](url) | 640 | 51.2 | - | - | 36.9 | 104.7 |
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| **[YOLOv8l](url)** | 640 | **52.8** | - | - | 43.7 | 165.7 |
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| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x.pt) | 640 | 50.7 | - | - | 86.7 | 205.7 |
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| [YOLOv7-X](url) | 640 | 52.9 | - | - | 71.3 | 189.9 |
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| **[YOLOv8x](url)** | 640 | **53.7** | - | - | 68.2 | 258.5 |
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| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x6.pt) | 1280 | 55.0 | - | - | 140.7 | 839.2 |
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| [YOLOv7-E6E](url) | 1280 | 56.8 | - | - | 151.7 | 843.2 |
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| **[YOLOv8x6](https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5x6.pt)**<br>+TTA | 1280 | -<br>- | -<br>- | -<br>- | 97.4 | 1047.2<br>- |
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If you're looking to modify YOLO for R&D or to build on top of it, refer to [Using Trainer](<>) Guide on our docs.
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