@ -53,27 +53,62 @@ I have read the CLA Document and I sign the CLA
When adding new functions or classes, please include a [Google-style docstring](https://google.github.io/styleguide/pyguide.html) to provide clear and concise documentation for other developers. This will help ensure that your contributions are easy to understand and maintain.
Example function that demonstrates Google-style docstrings.
Args:
arg1 (int): The first argument.
arg2 (int): The second argument.
Returns:
(bool): True if successful, False otherwise.
Examples:
>>> result = example_function(1, 2) # returns False
"""
if arg1 == arg2:
return True
return False
```
!!! Example "Example Docstrings"
=== "Google-style"
This example shows both Google-style docstrings. Note that both input and output `types` must always be enclosed by parentheses, i.e. `(bool)`.
```python
def example_function(arg1, arg2=4):
"""
Example function that demonstrates Google-style docstrings.
Args:
arg1 (int): The first argument.
arg2 (int): The second argument. Default value is 4.
Returns:
(bool): True if successful, False otherwise.
Examples:
>>> result = example_function(1, 2) # returns False
"""
if arg1 == arg2:
return True
return False
```
=== "Google-style with type hints"
This example shows both Google-style docstrings and argument and return type hints, though both are not required, one can be used without the other.
```python
def example_function(arg1: int, arg2: int = 4) -> bool:
"""
Example function that demonstrates Google-style docstrings.
Args:
arg1: The first argument.
arg2: The second argument. Default value is 4.
Returns:
True if successful, False otherwise.
Examples:
>>> result = example_function(1, 2) # returns False
"""
if arg1 == arg2:
return True
return False
```
=== "Single-line"
Smaller or simpler functions can utilize a single-line docstring. Note the docstring must use 3 double-quotes, and be a complete sentence starting with a capital letter and ending with a period.
```python
def example_small_function(arg1: int, arg2: int = 4) -> bool:
"""Example function that demonstrates a single-line docstring."""
description: Access object detection capabilities of YOLOv8 via our RESTful API. Learn how to use the YOLO Inference API with Python or CLI for swift object detection.
description: Access object detection capabilities of YOLOv8 via our RESTful API. Learn how to use the YOLO Inference API with Python or cURL for swift object detection.
In this example, replace `API_KEY` with your actual API key, `MODEL_ID` with the desired model ID, and `path/to/image.jpg` with the path to the image you want to analyze.
## Example Usage with CLI
## Example Usage with cURL
You can use the YOLO Inference API with the command-line interface (CLI) by utilizing the `curl` command. Replace `API_KEY` with your actual API key, `MODEL_ID` with the desired model ID, and `image.jpg` with the path to the image you want to analyze:
You can use the YOLO Inference API with client URL (cURL) by utilizing the `curl` command. Replace `API_KEY` with your actual API key, `MODEL_ID` with the desired model ID, and `image.jpg` with the path to the image you want to analyze:
```bash
curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
@ -103,11 +103,11 @@ The JSON list contains information about the detected objects, their coordinates
### Detect Model Format
YOLO detection models, such as `yolov8n.pt`, can return JSON responses from local inference, CLI inference, and Python inference. All of these methods produce the same JSON response format.
YOLO detection models, such as `yolov8n.pt`, can return JSON responses from local inference, cURL inference, and Python inference. All of these methods produce the same JSON response format.
!!! Example "Detect Model JSON Response"
=== "Local"
=== "`ultralytics`"
```python
from ultralytics import YOLO
@ -122,7 +122,7 @@ YOLO detection models, such as `yolov8n.pt`, can return JSON responses from loca
print(results[0].tojson())
```
=== "CLI"
=== "cURL"
```bash
curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
@ -201,11 +201,11 @@ YOLO detection models, such as `yolov8n.pt`, can return JSON responses from loca
### Segment Model Format
YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses from local inference, CLI inference, and Python inference. All of these methods produce the same JSON response format.
YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses from local inference, cURL inference, and Python inference. All of these methods produce the same JSON response format.
!!! Example "Segment Model JSON Response"
=== "Local"
=== "`ultralytics`"
```python
from ultralytics import YOLO
@ -220,7 +220,7 @@ YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses fr
print(results[0].tojson())
```
=== "CLI"
=== "cURL"
```bash
curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
@ -342,11 +342,11 @@ YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses fr
### Pose Model Format
YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from local inference, CLI inference, and Python inference. All of these methods produce the same JSON response format.
YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from local inference, cURL inference, and Python inference. All of these methods produce the same JSON response format.
!!! Example "Pose Model JSON Response"
=== "Local"
=== "`ultralytics`"
```python
from ultralytics import YOLO
@ -361,7 +361,7 @@ YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from loca
print(results[0].tojson())
```
=== "CLI"
=== "cURL"
```bash
curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
@ -18,6 +18,19 @@ The output of an oriented object detector is a set of rotated bounding boxes tha
YOLOv8 OBB models use the `-obb` suffix, i.e. `yolov8n-obb.pt` and are pretrained on [DOTAv1](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/DOTAv1.yaml).
Glenn Jocher
1 year ago
committed by
GitHub