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Improved Docs models Usage examples (#4214)

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Glenn Jocher 2023-08-07 20:57:35 +02:00 • committed by GitHub
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docs/models/sam.md
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@ -72,6 +72,7 @@ The Segment Anything Model can be employed for a multitude of downstream tasks t
# Run inference
model('path/to/image.jpg')
```
=== "CLI"
```bash
@ -99,6 +100,7 @@ The Segment Anything Model can be employed for a multitude of downstream tasks t
predictor.set_image(cv2.imread("ultralytics/assets/zidane.jpg")) # set with np.ndarray
results = predictor(bboxes=[439, 437, 524, 709])
results = predictor(points=[900, 370], labels=[1])
# Reset image
predictor.reset_image()
```
@ -114,9 +116,8 @@ The Segment Anything Model can be employed for a multitude of downstream tasks t
overrides = dict(conf=0.25, task='segment', mode='predict', imgsz=1024, model="mobile_sam.pt")
predictor = SAMPredictor(overrides=overrides)
# segment with additional args
# Segment with additional args
results = predictor(source="ultralytics/assets/zidane.jpg", crop_n_layers=1, points_stride=64)
```
- More additional args for `Segment everything` see [`Predictor/generate` Reference](../reference/models/sam/predict.md).
@ -140,11 +141,11 @@ The Segment Anything Model can be employed for a multitude of downstream tasks t
Here we compare Meta's smallest SAM model, SAM-b, with Ultralytics smallest segmentation model, [YOLOv8n-seg](../tasks/segment.md):
| Model | Size | Parameters | Speed (CPU) |
|------------------------------------------------|----------------------------|------------------------|-------------------------|
| Meta's SAM-b | 358 MB | 94.7 M | 51096 ms/im |
| [MobileSAM](mobile-sam.md) | 40.7 MB | 10.1 M | 46122 ms/im |
| [FastSAM-s](fast-sam.md) with YOLOv8 backbone | 23.7 MB | 11.8 M | 115 ms/im |
| Model | Size | Parameters | Speed (CPU) |
|------------------------------------------------|----------------------------|------------------------|----------------------------|
| Meta's SAM-b | 358 MB | 94.7 M | 51096 ms/im |
| [MobileSAM](mobile-sam.md) | 40.7 MB | 10.1 M | 46122 ms/im |
| [FastSAM-s](fast-sam.md) with YOLOv8 backbone | 23.7 MB | 11.8 M | 115 ms/im |
| Ultralytics [YOLOv8n-seg](../tasks/segment.md) | **6.7 MB** (53.4x smaller) | **3.4 M** (27.9x less) | **59 ms/im** (866x faster) |
This comparison shows the order-of-magnitude differences in the model sizes and speeds between models. 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.
@ -205,16 +206,20 @@ Auto-annotation with pre-trained models can dramatically cut down the time and e
If you find SAM useful in your research or development work, please consider citing our paper:
```bibtex
@misc{kirillov2023segment,
title={Segment Anything},
author={Alexander Kirillov and Eric Mintun and Nikhila Ravi and Hanzi Mao and Chloe Rolland and Laura Gustafson and Tete Xiao and Spencer Whitehead and Alexander C. Berg and Wan-Yen Lo and Piotr Dollár and Ross Girshick},
year={2023},
eprint={2304.02643},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
!!! note ""
=== "BibTeX"
```bibtex
@misc{kirillov2023segment,
title={Segment Anything},
author={Alexander Kirillov and Eric Mintun and Nikhila Ravi and Hanzi Mao and Chloe Rolland and Laura Gustafson and Tete Xiao and Spencer Whitehead and Alexander C. Berg and Wan-Yen Lo and Piotr Dollár and Ross Girshick},
year={2023},
eprint={2304.02643},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
We would like to express our gratitude to Meta AI for creating and maintaining this valuable resource for the computer vision community.