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ultralytics 8.0.136 refactor and simplify package (#3748)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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2023-07-16 23:47:45 +08:00
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parent 8ebe94d1e9
commit 620f3eb218
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2
docs/models/fast-sam.md
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@ -42,7 +42,7 @@ To perform object detection on an image, use the `predict` method as shown below
```python
from ultralytics import FastSAM
from ultralytics.yolo.fastsam import FastSAMPrompt
from ultralytics.models.fastsam import FastSAMPrompt
# Define image path and inference device
IMAGE_PATH = 'ultralytics/assets/bus.jpg'

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docs/models/mobile-sam.md
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@ -6,9 +6,9 @@ keywords: MobileSAM, Faster Segment Anything, Segment Anything, Segment Anything
![MobileSAM Logo](https://github.com/ChaoningZhang/MobileSAM/blob/master/assets/logo2.png?raw=true)
# Faster Segment Anything (MobileSAM)
# Mobile Segment Anything (MobileSAM)
The MobileSAM paper is now available on [ResearchGate](https://www.researchgate.net/publication/371851844_Faster_Segment_Anything_Towards_Lightweight_SAM_for_Mobile_Applications) and [arXiv](https://arxiv.org/pdf/2306.14289.pdf). The most recent version will initially appear on ResearchGate due to the delayed content update on arXiv.
The MobileSAM paper is now available on [arXiv](https://arxiv.org/pdf/2306.14289.pdf).
A demonstration of MobileSAM running on a CPU can be accessed at this [demo link](https://huggingface.co/spaces/dhkim2810/MobileSAM). The performance on a Mac i5 CPU takes approximately 3 seconds. On the Hugging Face demo, the interface and lower-performance CPUs contribute to a slower response, but it continues to function effectively.

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docs/models/sam.md
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@ -88,7 +88,7 @@ The Segment Anything Model can be employed for a multitude of downstream tasks t
=== "Prompt inference"
```python
from ultralytics.vit.sam import Predictor as SAMPredictor
from ultralytics.models.sam import Predictor as SAMPredictor
# Create SAMPredictor
overrides = dict(conf=0.25, task='segment', mode='predict', imgsz=1024, model="mobile_sam.pt")
@ -108,7 +108,7 @@ The Segment Anything Model can be employed for a multitude of downstream tasks t
=== "Segment everything"
```python
from ultralytics.vit.sam import Predictor as SAMPredictor
from ultralytics.models.sam import Predictor as SAMPredictor
# Create SAMPredictor
overrides = dict(conf=0.25, task='segment', mode='predict', imgsz=1024, model="mobile_sam.pt")
@ -119,7 +119,7 @@ The Segment Anything Model can be employed for a multitude of downstream tasks t
```
- More additional args for `Segment everything` see [`Predictor/generate` Reference](../reference/vit/sam/predict.md).
- More additional args for `Segment everything` see [`Predictor/generate` Reference](../reference/models/sam/predict.md).
## Available Models and Supported Tasks
@ -184,7 +184,7 @@ Auto-annotation is a key feature of SAM, allowing users to generate a [segmentat
To auto-annotate your dataset with the Ultralytics framework, use the `auto_annotate` function as shown below:
```python
from ultralytics.yolo.data.annotator import auto_annotate
from ultralytics.data.annotator import auto_annotate
auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model='sam_b.pt')
```