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ultralytics 8.0.93 HUB docs and JSON2YOLO converter (#2431)

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# 🚧 Page Under Construction ⚒
This page is currently under construction!️ 👷Please check back later for updates. 😃🔜

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# Ultralytics HUB App
<a href="https://bit.ly/ultralytics_hub" target="_blank">
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
<br>
<div align="center">
<a href="https://github.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
<a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
<a href="https://twitter.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
<a href="https://youtube.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
<a href="https://www.tiktok.com/@ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
<a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="" /></a>
<br>
<br>
<a href="https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app" style="text-decoration:none;">
<img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/google-play.svg" width="15%" alt="" /></a>&nbsp;
<a href="https://apps.apple.com/xk/app/ultralytics/id1583935240" style="text-decoration:none;">
<img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/app-store.svg" width="15%" alt="" /></a>
</div>
Welcome to the Ultralytics HUB app, which is designed to demonstrate the power and capabilities of the YOLOv5 and YOLOv8
models. This app is available for download on
the [Apple App Store](https://apps.apple.com/xk/app/ultralytics/id1583935240) and
the [Google Play Store](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app).
**To install the app, simply scan the QR code provided above**. At the moment, the app features YOLOv5 models, with
YOLOv8 models set to be available soon.
With the YOLOv5 model, you can easily detect and classify objects in images and videos with high accuracy and speed. The
model has been trained on a vast dataset and can recognize a wide range of objects, including pedestrians, traffic
signs, and cars.
Using this app, you can try out YOLOv5 on your images and videos, and observe how the model works in real-time.
Additionally, you can learn more about YOLOv5's functionality and how it can be integrated into real-world applications.
We are confident that you will enjoy using YOLOv5 and be amazed at its capabilities. Thank you for choosing Ultralytics
for your AI solutions.

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# 🚧 Page Under Construction ⚒
This page is currently under construction!️ 👷Please check back later for updates. 😃🔜

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# HUB Datasets
## Upload a Dataset
Ultralytics HUB datasets are just like YOLOv5 and YOLOv8 🚀 datasets, they use the same structure and the same label formats to keep
everything simple.
When you upload a dataset to Ultralytics HUB, make sure to **place your dataset YAML inside the dataset root directory**
as in the example shown below, and then zip for upload to https://hub.ultralytics.com/. Your **dataset YAML, directory
and zip** should all share the same name. For example, if your dataset is called 'coco6' as in our
example [ultralytics/hub/coco6.zip](https://github.com/ultralytics/hub/blob/master/coco6.zip), then you should have a
coco6.yaml inside your coco6/ directory, which should zip to create coco6.zip for upload:
```bash
zip -r coco6.zip coco6
```
The example [coco6.zip](https://github.com/ultralytics/hub/blob/master/coco6.zip) dataset in this repository can be
downloaded and unzipped to see exactly how to structure your custom dataset.
<p align="center">
<img width="80%" src="https://user-images.githubusercontent.com/26833433/201424843-20fa081b-ad4b-4d6c-a095-e810775908d8.png" title="COCO6" />
</p>
The dataset YAML is the same standard YOLOv5 and YOLOv8 YAML format. See
the [YOLOv5 and YOLOv8 Train Custom Data tutorial](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/) for full details.
```yaml
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: # dataset root dir (leave empty for HUB)
train: images/train # train images (relative to 'path') 8 images
val: images/val # val images (relative to 'path') 8 images
test: # test images (optional)
# Classes
names:
0: person
1: bicycle
2: car
3: motorcycle
...
```
After zipping your dataset, sign in to [Ultralytics HUB](https://bit.ly/ultralytics_hub) and click the Datasets tab.
Click 'Upload Dataset' to upload, scan and visualize your new dataset before training new YOLOv5 or YOLOv8 models on it!
<img width="100%" alt="HUB Dataset Upload" src="https://user-images.githubusercontent.com/26833433/216763338-9a8812c8-a4e5-4362-8102-40dad7818396.png">

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# Ultralytics HUB
<a href="https://bit.ly/ultralytics_hub" target="_blank">
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
<br>
<br>
<div align="center">
<a href="https://github.com/ultralytics/hub/actions/workflows/ci.yaml">
<img src="https://github.com/ultralytics/hub/actions/workflows/ci.yaml/badge.svg" alt="CI CPU"></a>
<a href="https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
</div>
<br>
👋 Hello from the [Ultralytics](https://ultralytics.com/) Team! We've been working hard these last few months to
launch [Ultralytics HUB](https://bit.ly/ultralytics_hub), a new web tool for training and deploying all your YOLOv5 and YOLOv8 🚀
models from one spot!
## Introduction
HUB is designed to be user-friendly and intuitive, with a drag-and-drop interface that allows users to
easily upload their data and train new models quickly. It offers a range of pre-trained models and
templates to choose from, making it easy for users to get started with training their own models. Once a model is
trained, it can be easily deployed and used for real-time object detection, instance segmentation and classification tasks.
We hope that the resources here will help you get the most out of HUB. Please browse the HUB <a href="https://docs.ultralytics.com/hub">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/hub/issues/new/choose">GitHub</a> for support, and join our <a href="https://discord.gg/n6cFeSPZdD">Discord</a> community for questions and discussions!
- [**Quickstart**](./quickstart.md). Start training and deploying YOLO models with HUB in seconds.
- [**Datasets: Preparing and Uploading**](./datasets.md). Learn how to prepare and upload your datasets to HUB in YOLO format.
- [**Projects: Creating and Managing**](./projects.md). Group your models into projects for improved organization.
- [**Models: Training and Exporting**](./models.md). Train YOLOv5 and YOLOv8 models on your custom datasets and export them to various formats for deployment.
- [**Integrations: Options**](./integrations.md). Explore different integration options for your trained models, such as TensorFlow, ONNX, OpenVINO, CoreML, and PaddlePaddle.
- [**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)
- [**Android**](./app/android.md)
- [**Inference API**](./inference_api.md). Understand how to use the Inference API for running your trained models in the cloud to generate predictions.

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# 🚧 Page Under Construction ⚒
This page is currently under construction!️ 👷Please check back later for updates. 😃🔜
# YOLO Inference API
The YOLO Inference API allows you to access the YOLOv8 object detection capabilities via a RESTful API. This enables you to run object detection on images without the need to install and set up the YOLOv8 environment locally.
## API URL
The API URL is the address used to access the YOLO Inference API. In this case, the base URL is:
```
https://api.ultralytics.com/v1/predict
```
## Example Usage in Python
To access the YOLO Inference API with the specified model and API key using Python, you can use the following code:
```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())
```
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
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:
```commandline
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" \
-F "confidence=0.25" \
-F "iou=0.45"
```
## Passing Arguments
This command sends a POST request to the YOLO Inference API with the specified `MODEL_ID` in the URL and the `API_KEY` in the request `headers`, along with the image file specified by `@path/to/image.jpg`.
Here's an example of passing the `size`, `confidence`, and `iou` arguments via the API URL using the `requests` library in Python:
```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())
```
In this example, the `data` dictionary contains the query arguments `size`, `confidence`, and `iou`, which tells the API to run inference at image size 640 with confidence and IoU thresholds of 0.25 and 0.45.
This will send the query parameters along with the file in the POST request. See the table below for a full list of available inference arguments.
| Argument | Default | Type | Notes |
|--------------|---------|---------|-----------------------------------------|
| `size` | `640` | `int` | allowable range is `32` - `1280` pixels |
| `confidence` | `0.25` | `float` | allowable range is `0.01` - `1.0` |
| `iou` | `0.45` | `float` | allowable range is `0.0` - `0.95` |
| `url` | `''` | `str` | |
| `normalize` | `False` | `bool` | |
## Return JSON format
The YOLO Inference API returns a JSON list with the detection results. The format of the JSON list will be the same as the one produced locally by the `results[0].tojson()` command.
The JSON list contains information about the detected objects, their coordinates, classes, and confidence scores.
### Detect Model Format
YOLO detection models, such as `yolov8n.pt`, can return JSON responses from local inference, CLI API inference, and Python API inference. All of these methods produce the same JSON response format.
!!! example "Detect Model JSON Response"
=== "Local"
```python
from ultralytics import YOLO
# Load model
model = YOLO('yolov8n.pt')
# Run inference
results = model('image.jpg')
# Print image.jpg results in JSON format
print(results[0].tojson())
```
=== "CLI API"
```commandline
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" \
-F "confidence=0.25" \
-F "iou=0.45"
```
=== "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())
```
=== "JSON Response"
```json
{
"success": True,
"message": "Inference complete.",
"data": [
{
"name": "person",
"class": 0,
"confidence": 0.8359682559967041,
"box": {
"x1": 0.08974208831787109,
"y1": 0.27418340047200523,
"x2": 0.8706787109375,
"y2": 0.9887352837456598
}
},
{
"name": "person",
"class": 0,
"confidence": 0.8189555406570435,
"box": {
"x1": 0.5847355842590332,
"y1": 0.05813225640190972,
"x2": 0.8930277824401855,
"y2": 0.9903111775716146
}
},
{
"name": "tie",
"class": 27,
"confidence": 0.2909725308418274,
"box": {
"x1": 0.3433395862579346,
"y1": 0.6070465511745877,
"x2": 0.40964522361755373,
"y2": 0.9849439832899306
}
}
]
}
```
### Segment Model Format
YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses from local inference, CLI API inference, and Python API inference. All of these methods produce the same JSON response format.
!!! example "Segment Model JSON Response"
=== "Local"
```python
from ultralytics import YOLO
# Load model
model = YOLO('yolov8n-seg.pt')
# Run inference
results = model('image.jpg')
# Print image.jpg results in JSON format
print(results[0].tojson())
```
=== "CLI API"
```commandline
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" \
-F "confidence=0.25" \
-F "iou=0.45"
```
=== "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())
```
=== "JSON Response"
Note `segments` `x` and `y` lengths may vary from one object to another. Larger or more complex objects may have more segment points.
```json
{
"success": True,
"message": "Inference complete.",
"data": [
{
"name": "person",
"class": 0,
"confidence": 0.856913149356842,
"box": {
"x1": 0.1064866065979004,
"y1": 0.2798851860894097,
"x2": 0.8738358497619629,
"y2": 0.9894873725043403
},
"segments": {
"x": [
0.421875,
0.4203124940395355,
0.41718751192092896
...
],
"y": [
0.2888889014720917,
0.2916666567325592,
0.2916666567325592
...
]
}
},
{
"name": "person",
"class": 0,
"confidence": 0.8512625694274902,
"box": {
"x1": 0.5757311820983887,
"y1": 0.053943040635850696,
"x2": 0.8960096359252929,
"y2": 0.985154045952691
},
"segments": {
"x": [
0.7515624761581421,
0.75,
0.7437499761581421
...
],
"y": [
0.0555555559694767,
0.05833333358168602,
0.05833333358168602
...
]
}
},
{
"name": "tie",
"class": 27,
"confidence": 0.6485961675643921,
"box": {
"x1": 0.33911995887756347,
"y1": 0.6057066175672743,
"x2": 0.4081430912017822,
"y2": 0.9916408962673611
},
"segments": {
"x": [
0.37187498807907104,
0.37031251192092896,
0.3687500059604645
...
],
"y": [
0.6111111044883728,
0.6138888597488403,
0.6138888597488403
...
]
}
}
]
}
```
### Pose Model Format
YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from local inference, CLI API inference, and Python API inference. All of these methods produce the same JSON response format.
!!! example "Pose Model JSON Response"
=== "Local"
```python
from ultralytics import YOLO
# Load model
model = YOLO('yolov8n-seg.pt')
# Run inference
results = model('image.jpg')
# Print image.jpg results in JSON format
print(results[0].tojson())
```
=== "CLI API"
```commandline
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" \
-F "confidence=0.25" \
-F "iou=0.45"
```
=== "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())
```
=== "JSON Response"
Note COCO-keypoints pretrained models will have 17 human keypoints. The `visible` part of the keypoints indicates whether a keypoint is visible or obscured. Obscured keypoints may be outside the image or may not be visible, i.e. a person's eyes facing away from the camera.
```json
{
"success": True,
"message": "Inference complete.",
"data": [
{
"name": "person",
"class": 0,
"confidence": 0.8439509868621826,
"box": {
"x1": 0.1125,
"y1": 0.28194444444444444,
"x2": 0.7953125,
"y2": 0.9902777777777778
},
"keypoints": {
"x": [
0.5058594942092896,
0.5103894472122192,
0.4920862317085266
...
],
"y": [
0.48964157700538635,
0.4643048942089081,
0.4465252459049225
...
],
"visible": [
0.8726999163627625,
0.653947651386261,
0.9130823612213135
...
]
}
},
{
"name": "person",
"class": 0,
"confidence": 0.7474289536476135,
"box": {
"x1": 0.58125,
"y1": 0.0625,
"x2": 0.8859375,
"y2": 0.9888888888888889
},
"keypoints": {
"x": [
0.778544008731842,
0.7976160049438477,
0.7530890107154846
...
],
"y": [
0.27595141530036926,
0.2378823608160019,
0.23644638061523438
...
],
"visible": [
0.8900790810585022,
0.789978563785553,
0.8974530100822449
...
]
}
}
]
}
```

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# 🚧 Page Under Construction ⚒
This page is currently under construction!️ 👷Please check back later for updates. 😃🔜

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# HUB Models
## Train a Model
Connect to the Ultralytics HUB notebook and use your model API key to begin training!
<a href="https://colab.research.google.com/github/ultralytics/hub/blob/master/hub.ipynb" target="_blank">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
## Deploy to Real World
Export your model to 13 different formats, including TensorFlow, ONNX, OpenVINO, CoreML, Paddle and many others. Run
models directly on your [iOS](https://apps.apple.com/xk/app/ultralytics/id1583935240) or
[Android](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app) mobile device by downloading
the [Ultralytics App](https://ultralytics.com/app_install)!

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# 🚧 Page Under Construction ⚒
This page is currently under construction!️ 👷Please check back later for updates. 😃🔜

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