@ -30,13 +30,30 @@ For an in-depth look at the Segment Anything Model and the SA-1B dataset, please
The Segment Anything Model can be employed for a multitude of downstream tasks that go beyond its training data. This includes edge detection, object proposal generation, instance segmentation, and preliminary text-to-mask prediction. With prompt engineering, SAM can swiftly adapt to new tasks and data distributions in a zero-shot manner, establishing it as a versatile and potent tool for all your image segmentation needs.
```python
from ultralytics import SAM
model = SAM('sam_b.pt')
model.info() # display model information
model.predict('path/to/image.jpg') # predict
```
!!! example "SAM prediction example"
Device is determined automatically. If a GPU is available then it will be used, otherwise inference will run on CPU.
This comparison shows the order-of-magnitude differences in the model sizes and speeds. Whereas SAM presents unique capabilities for automatic segmenting, it is not a direct competitor to YOLOv8 segment models, which are smaller, faster and more efficient since they are dedicated to more targeted use cases.
To reproduce this test:
```python
from ultralytics import SAM, YOLO
# Profile SAM-b
model = SAM('sam_b.pt')
model.info()
model('ultralytics/assets')
# Profile YOLOv8n-seg
model = YOLO('yolov8n-seg.pt')
model.info()
model('ultralytics/assets')
```
## Auto-Annotation: A Quick Path to Segmentation Datasets
Auto-annotation is a key feature of SAM, allowing users to generate a [segmentation dataset](https://docs.ultralytics.com/datasets/segment) using a pre-trained detection model. This feature enables rapid and accurate annotation of a large number of images, bypassing the need for time-consuming manual labeling.
Glenn Jocher
1 year ago
committed by
GitHub