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ultralytics 8.0.196 instance-mean Segment loss (#5285)

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Co-authored-by: Andy <39454881+yermandy@users.noreply.github.com>
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Glenn Jocher 2023-10-09 20:08:39 +02:00 • committed by GitHub
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6
docs/datasets/segment/coco8-seg.md
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@ -8,11 +8,7 @@ keywords: COCO8-Seg dataset, Ultralytics, YOLOv8, instance segmentation, dataset
## Introduction
[Ultralytics](https://ultralytics.com) COCO8-Seg is a small, but versatile instance segmentation dataset composed of the
first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and
debugging segmentation models, or for experimenting with new detection approaches. With 8 images, it is small enough to
be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training
larger datasets.
[Ultralytics](https://ultralytics.com) COCO8-Seg is a small, but versatile instance segmentation dataset composed of the first 8 images of the COCO train 2017 set, 4 for training and 4 for validation. This dataset is ideal for testing and debugging segmentation models, or for experimenting with new detection approaches. With 8 images, it is small enough to be easily manageable, yet diverse enough to test training pipelines for errors and act as a sanity check before training larger datasets.
This dataset is intended for use with Ultralytics [HUB](https://hub.ultralytics.com)
and [YOLOv8](https://github.com/ultralytics/ultralytics).

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docs/datasets/segment/index.md
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@ -107,7 +107,7 @@ You can easily convert labels from the popular COCO dataset format to the YOLO f
```python
from ultralytics.data.converter import convert_coco
convert_coco(labels_dir='path/to/coco/annotations/', use_segments=True)
```
@ -129,7 +129,7 @@ To auto-annotate your dataset using the Ultralytics framework, you can use the `
```python
from ultralytics.data.annotator import auto_annotate
auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model='sam_b.pt')
```