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ultralytics 8.0.141 create new SettingsManager (#3790)

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This commit is contained in:
Glenn Jocher
2023-07-23 16:03:34 +02:00
committed by GitHub
parent 42afe772d5
commit 20f5efd40a
215 changed files with 917 additions and 749 deletions
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2
docs/hub/app/android.md
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@ -63,4 +63,4 @@ To get started with the Ultralytics Android App, follow these steps:
6. Explore the app's settings to adjust the detection threshold, enable or disable specific object classes, and more.
With the Ultralytics Android App, you now have the power of real-time object detection using YOLO models right at your fingertips. Enjoy exploring the app's features and optimizing its settings to suit your specific use cases.
With the Ultralytics Android App, you now have the power of real-time object detection using YOLO models right at your fingertips. Enjoy exploring the app's features and optimizing its settings to suit your specific use cases.

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docs/hub/app/index.md
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@ -49,4 +49,4 @@ Welcome to the Ultralytics HUB App! We are excited to introduce this powerful mo
- [**iOS**](./ios.md): Learn about YOLO CoreML models accelerated on Apple's Neural Engine for iPhones and iPads.
- [**Android**](./android.md): Explore TFLite acceleration on Android mobile devices.
Get started today by downloading the Ultralytics HUB App on your mobile device and unlock the potential of YOLOv5 and YOLOv8 models on-the-go. Don't forget to check out our comprehensive [HUB Docs](../) for more information on training, deploying, and using your custom models with the Ultralytics HUB platform.
Get started today by downloading the Ultralytics HUB App on your mobile device and unlock the potential of YOLOv5 and YOLOv8 models on-the-go. Don't forget to check out our comprehensive [HUB Docs](../) for more information on training, deploying, and using your custom models with the Ultralytics HUB platform.

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docs/hub/app/ios.md
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@ -53,4 +53,4 @@ To get started with the Ultralytics iOS App, follow these steps:
6. Explore the app's settings to adjust the detection threshold, enable or disable specific object classes, and more.
With the Ultralytics iOS App, you can now leverage the power of YOLO models for real-time object detection on your iPhone or iPad, powered by the Apple Neural Engine and optimized with FP16 or INT8 quantization.
With the Ultralytics iOS App, you can now leverage the power of YOLO models for real-time object detection on your iPhone or iPad, powered by the Apple Neural Engine and optimized with FP16 or INT8 quantization.

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docs/hub/datasets.md
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@ -156,4 +156,4 @@ Navigate to the Dataset page of the dataset you want to delete, open the dataset
If you change your mind, you can restore the dataset from the [Trash](https://hub.ultralytics.com/trash) page.
![Ultralytics HUB screenshot of the Trash page with an arrow pointing to the Restore option of one of the datasets](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_delete_dataset_3.jpg)
![Ultralytics HUB screenshot of the Trash page with an arrow pointing to the Restore option of one of the datasets](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_delete_dataset_3.jpg)

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docs/hub/index.md
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@ -39,4 +39,4 @@ We hope that the resources here will help you get the most out of HUB. Please br
- [**Ultralytics HUB App**](./app/index.md). Learn about the Ultralytics App for iOS and Android, which allows you to run models directly on your mobile device.
* [**iOS**](./app/ios.md). Learn about YOLO CoreML models accelerated on Apple's Neural Engine on iPhones and iPads.
* [**Android**](./app/android.md). Explore TFLite acceleration on mobile devices.
- [**Inference API**](./inference_api.md). Understand how to use the Inference API for running your trained models in the cloud to generate predictions.
- [**Inference API**](./inference_api.md). Understand how to use the Inference API for running your trained models in the cloud to generate predictions.

50
docs/hub/inference_api.md
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@ -111,7 +111,7 @@ YOLO detection models, such as `yolov8n.pt`, can return JSON responses from loca
=== "Local"
```python
from ultralytics import YOLO
# Load model
model = YOLO('yolov8n.pt')
@ -119,12 +119,12 @@ YOLO detection models, such as `yolov8n.pt`, can return JSON responses from loca
results = model('image.jpg')
# Print image.jpg results in JSON format
print(results[0].tojson())
print(results[0].tojson())
```
=== "CLI API"
```bash
curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
-H "x-api-key: API_KEY" \
-F "image=@/path/to/image.jpg" \
-F "size=640" \
@ -135,21 +135,21 @@ YOLO detection models, such as `yolov8n.pt`, can return JSON responses from loca
=== "Python API"
```python
import requests
# API URL, use actual MODEL_ID
url = f"https://api.ultralytics.com/v1/predict/MODEL_ID"
# Headers, use actual API_KEY
headers = {"x-api-key": "API_KEY"}
# Inference arguments (optional)
data = {"size": 640, "confidence": 0.25, "iou": 0.45}
# Load image and send request
with open("path/to/image.jpg", "rb") as image_file:
files = {"image": image_file}
response = requests.post(url, headers=headers, files=files, data=data)
print(response.json())
```
@ -205,7 +205,7 @@ YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses fr
=== "Local"
```python
from ultralytics import YOLO
# Load model
model = YOLO('yolov8n-seg.pt')
@ -213,12 +213,12 @@ YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses fr
results = model('image.jpg')
# Print image.jpg results in JSON format
print(results[0].tojson())
print(results[0].tojson())
```
=== "CLI API"
```bash
curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
-H "x-api-key: API_KEY" \
-F "image=@/path/to/image.jpg" \
-F "size=640" \
@ -229,21 +229,21 @@ YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses fr
=== "Python API"
```python
import requests
# API URL, use actual MODEL_ID
url = f"https://api.ultralytics.com/v1/predict/MODEL_ID"
# Headers, use actual API_KEY
headers = {"x-api-key": "API_KEY"}
# Inference arguments (optional)
data = {"size": 640, "confidence": 0.25, "iou": 0.45}
# Load image and send request
with open("path/to/image.jpg", "rb") as image_file:
files = {"image": image_file}
response = requests.post(url, headers=headers, files=files, data=data)
print(response.json())
```
@ -342,7 +342,7 @@ YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from loca
=== "Local"
```python
from ultralytics import YOLO
# Load model
model = YOLO('yolov8n-seg.pt')
@ -350,12 +350,12 @@ YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from loca
results = model('image.jpg')
# Print image.jpg results in JSON format
print(results[0].tojson())
print(results[0].tojson())
```
=== "CLI API"
```bash
curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
-H "x-api-key: API_KEY" \
-F "image=@/path/to/image.jpg" \
-F "size=640" \
@ -366,21 +366,21 @@ YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from loca
=== "Python API"
```python
import requests
# API URL, use actual MODEL_ID
url = f"https://api.ultralytics.com/v1/predict/MODEL_ID"
# Headers, use actual API_KEY
headers = {"x-api-key": "API_KEY"}
# Inference arguments (optional)
data = {"size": 640, "confidence": 0.25, "iou": 0.45}
# Load image and send request
with open("path/to/image.jpg", "rb") as image_file:
files = {"image": image_file}
response = requests.post(url, headers=headers, files=files, data=data)
print(response.json())
```
@ -455,4 +455,4 @@ YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from loca
}
]
}
```
```

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docs/hub/models.md
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@ -210,4 +210,4 @@ Navigate to the Model page of the model you want to delete, open the model actio
If you change your mind, you can restore the model from the [Trash](https://hub.ultralytics.com/trash) page.
![Ultralytics HUB screenshot of the Trash page with an arrow pointing to the Restore option of one of the models](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_delete_model_3.jpg)
![Ultralytics HUB screenshot of the Trash page with an arrow pointing to the Restore option of one of the models](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_delete_model_3.jpg)

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docs/hub/projects.md
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@ -166,4 +166,4 @@ Navigate to the Project page of the project where the model you want to mode is
Select the project you want to transfer the model to and click **Save**.
![Ultralytics HUB screenshot of the Transfer Model dialog with an arrow pointing to the dropdown and one to the Save button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_transfer_models_3.jpg)
![Ultralytics HUB screenshot of the Transfer Model dialog with an arrow pointing to the dropdown and one to the Save button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_transfer_models_3.jpg)