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ultralytics 8.0.100 add Mosaic9() augmentation (#2605)

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Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: Tommy in Tongji <36354458+TommyZihao@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: BIGBOSS-FOX <47949596+BIGBOSS-FOX@users.noreply.github.com>
Co-authored-by: xbkaishui <xxkaishui@gmail.com>
This commit is contained in:
Glenn Jocher
2023-05-14 20:43:35 +02:00
committed by GitHub
parent db1c5885d5
commit dce4efce48
23 changed files with 351 additions and 64 deletions
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docs/datasets/segment/index.md
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@ -45,7 +45,7 @@ train: <path-to-training-images>
val: <path-to-validation-images>
nc: <number-of-classes>
names: [<class-1>, <class-2>, ..., <class-n>]
names: [ <class-1>, <class-2>, ..., <class-n> ]
```
@ -72,7 +72,7 @@ train: data/train/
val: data/val/
nc: 2
names: ['person', 'car']
names: [ 'person', 'car' ]
```
## Usage
@ -107,4 +107,30 @@ names: ['person', 'car']
from ultralytics.yolo.data.converter import convert_coco
convert_coco(labels_dir='../coco/annotations/', use_segments=True)
```
```
## Auto-Annotation
Auto-annotation is an essential feature that allows you to generate a segmentation dataset using a pre-trained detection model. It enables you to quickly and accurately annotate a large number of images without the need for manual labeling, saving time and effort.
### Generate Segmentation Dataset Using a Detection Model
To auto-annotate your dataset using the Ultralytics framework, you can use the `auto_annotate` function as shown below:
```python
from ultralytics.yolo.data import auto_annotate
auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model='sam_b.pt')
```
| Argument | Type | Description | Default |
|------------|---------------------|---------------------------------------------------------------------------------------------------------|--------------|
| data | str | Path to a folder containing images to be annotated. | |
| det_model | str, optional | Pre-trained YOLO detection model. Defaults to 'yolov8x.pt'. | 'yolov8x.pt' |
| sam_model | str, optional | Pre-trained SAM segmentation model. Defaults to 'sam_b.pt'. | 'sam_b.pt' |
| device | str, optional | Device to run the models on. Defaults to an empty string (CPU or GPU, if available). | |
| output_dir | str, None, optional | Directory to save the annotated results. Defaults to a 'labels' folder in the same directory as 'data'. | None |
The `auto_annotate` function takes the path to your images, along with optional arguments for specifying the pre-trained detection and [SAM segmentation models](https://docs.ultralytics.com/models/sam), the device to run the models on, and the output directory for saving the annotated results.
By leveraging the power of pre-trained models, auto-annotation can significantly reduce the time and effort required for creating high-quality segmentation datasets. This feature is particularly useful for researchers and developers working with large image collections, as it allows them to focus on model development and evaluation rather than manual annotation.