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Cleanup (#168)

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@ -68,7 +68,22 @@ pip install ultralytics
<details open>
<summary>Usage</summary>
YOLOv8 may be used in a python environment:
YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command:
```bash
yolo task=detect mode=predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg"
```
`yolo` can be used for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See a full list of available `yolo` [arguments](https://docs.ultralytics.com/config/) in the YOLOv8 [Docs](https://docs.ultralytics.com).
```bash
yolo task=detect mode=train model=yolov8n.pt args...
classify predict yolov8n-cls.yaml args...
segment val yolov8n-seg.yaml args...
export yolov8n.pt format=onnx args...
```
YOLOv8 may also be used directly in a Python environment, and accepts the same [arguments](https://docs.ultralytics.com/config/) as in the CLI example above:
```python
from ultralytics import YOLO
@ -81,18 +96,16 @@ model.predict(source="https://ultralytics.com/images/bus.jpg") # predict on an
model.export(format="onnx") # export the model to ONNX format
```
Or with CLI `yolo` commands:
```bash
yolo task=detect mode=train model=yolov8n.pt args...
classify predict yolov8n-cls.yaml args...
segment val yolov8n-seg.yaml args...
export yolov8n.pt format=onnx args...
```
[Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/yolo/v8/models) download automatically from the latest
Ultralytics [release](https://github.com/ultralytics/ultralytics/releases).
### Known Issues / TODOs
- [ ] TensorFlow exports
- [ ] GPU exports
- [ ] DDP resume
- [ ] [arxiv.org](https://arxiv.org) paper
</details>
## <div align="center">Checkpoints</div>