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---
comments: true
description: Learn how to run inference using the Ultralytics HUB Inference API. Includes examples in Python and cURL for quick integration.
keywords: Ultralytics, HUB, Inference API, Python, cURL, REST API, YOLO, image processing, machine learning, AI integration
---
# Ultralytics HUB Inference API
After you [train a model](./models.md#train-model), you can use the [Shared Inference API](#shared-inference-api) for free. If you are a [Pro](./pro.md) user, you can access the [Dedicated Inference API](#dedicated-inference-api). The [Ultralytics HUB](https://ultralytics.com/hub) Inference API allows you to run inference through our REST API without the need to install and set up the Ultralytics YOLO environment locally.
<p align="center">
<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/OpWpBI35A5Y"
title="YouTube video player" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Ultralytics HUB Inference API Walkthrough
</p>
## Dedicated Inference API
In response to high demand and widespread interest, we are thrilled to unveil the [Ultralytics HUB](https://ultralytics.com/hub) Dedicated Inference API, offering single-click deployment in a dedicated environment for our [Pro](./pro.md) users!
!!! note "Note"
We are excited to offer this feature FREE during our public beta as part of the [Pro Plan](./pro.md), with paid tiers possible in the future.
- **Global Coverage:** Deployed across 38 regions worldwide, ensuring low-latency access from any location. [See the full list of Google Cloud regions](https://cloud.google.com/about/locations).
- **Google Cloud Run-Backed:** Backed by Google Cloud Run, providing infinitely scalable and highly reliable infrastructure.
- **High Speed:** Sub-100ms latency is possible for YOLOv8n inference at 640 resolution from nearby regions based on Ultralytics testing.
- **Enhanced Security:** Provides robust security features to protect your data and ensure compliance with industry standards. [Learn more about Google Cloud security](https://cloud.google.com/security).
To use the [Ultralytics HUB](https://ultralytics.com/hub) Dedicated Inference API, click on the **Start Endpoint** button. Next, use the unique endpoint URL as described in the guides below.
!!! tip "Tip"
Choose the region with the lowest latency for the best performance as described in the [documentation](https://docs.ultralytics.com/reference/hub/google/__init__).
To shut down the dedicated endpoint, click on the **Stop Endpoint** button.
## Shared Inference API
To use the [Ultralytics HUB](https://ultralytics.com/hub) Shared Inference API, follow the guides below.
Free users have the following usage limits:
- 100 calls / hour
- 1000 calls / month
[Pro](./pro.md) users have the following usage limits:
- 1000 calls / hour
- 10000 calls / month
## Python
To access the [Ultralytics HUB](https://ultralytics.com/hub) Inference API using Python, use the following code:
```python
import requests
# API URL, use actual MODEL_ID
url = "https://api.ultralytics.com/v1/predict/MODEL_ID"
# Headers, use actual API_KEY
headers = {"x-api-key": "API_KEY"}
# Inference arguments (optional)
data = {"imgsz": 640, "conf": 0.25, "iou": 0.45}
# Load image and send request
with open("path/to/image.jpg", "rb") as image_file:
files = {"file": image_file}
response = requests.post(url, headers=headers, files=files, data=data)
print(response.json())
```
!!! note "Note"
Replace `MODEL_ID` with the desired model ID, `API_KEY` with your actual API key, and `path/to/image.jpg` with the path to the image you want to run inference on.
If you are using our [Dedicated Inference API](#dedicated-inference-api), replace the `url` as well.
## cURL
To access the [Ultralytics HUB](https://ultralytics.com/hub) Inference API using cURL, use the following code:
```bash
curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
-H "x-api-key: API_KEY" \
-F "file=@/path/to/image.jpg" \
-F "imgsz=640" \
-F "conf=0.25" \
-F "iou=0.45"
```
!!! note "Note"
Replace `MODEL_ID` with the desired model ID, `API_KEY` with your actual API key, and `path/to/image.jpg` with the path to the image you want to run inference on.
If you are using our [Dedicated Inference API](#dedicated-inference-api), replace the `url` as well.
## Arguments
See the table below for a full list of available inference arguments.
| Argument | Default | Type | Description |
| -------- | ------- | ------- | -------------------------------------------------------------------- |
| `file` | | `file` | Image or video file to be used for inference. |
| `imgsz` | `640` | `int` | Size of the input image, valid range is `32` - `1280` pixels. |
| `conf` | `0.25` | `float` | Confidence threshold for predictions, valid range `0.01` - `1.0`. |
| `iou` | `0.45` | `float` | Intersection over Union (IoU) threshold, valid range `0.0` - `0.95`. |
## Response
The [Ultralytics HUB](https://ultralytics.com/hub) Inference API returns a JSON response.
### Classification
!!! Example "Classification Model"
=== "`ultralytics`"
```python
from ultralytics import YOLO
# Load model
model = YOLO("yolov8n-cls.pt")
# Run inference
results = model("image.jpg")
# Print image.jpg results in JSON format
print(results[0].tojson())
```
=== "cURL"
```bash
curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
-H "x-api-key: API_KEY" \
-F "file=@/path/to/image.jpg" \
-F "imgsz=640" \
-F "conf=0.25" \
-F "iou=0.45"
```
=== "Python"
```python
import requests
# API URL, use actual MODEL_ID
url = "https://api.ultralytics.com/v1/predict/MODEL_ID"
# Headers, use actual API_KEY
headers = {"x-api-key": "API_KEY"}
# Inference arguments (optional)
data = {"imgsz": 640, "conf": 0.25, "iou": 0.45}
# Load image and send request
with open("path/to/image.jpg", "rb") as image_file:
files = {"file": image_file}
response = requests.post(url, headers=headers, files=files, data=data)
print(response.json())
```
=== "Response"
```json
{
"images": [
{
"results": [
{
"class": 0,
"name": "person",
"confidence": 0.92
}
],
"shape": [
750,
600
],
"speed": {
"inference": 200.8,
"postprocess": 0.8,
"preprocess": 2.8
}
}
],
"metadata": ...
}
```
### Detection
!!! Example "Detection Model"
=== "`ultralytics`"
```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())
```
=== "cURL"
```bash
curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
-H "x-api-key: API_KEY" \
-F "file=@/path/to/image.jpg" \
-F "imgsz=640" \
-F "conf=0.25" \
-F "iou=0.45"
```
=== "Python"
```python
import requests
# API URL, use actual MODEL_ID
url = "https://api.ultralytics.com/v1/predict/MODEL_ID"
# Headers, use actual API_KEY
headers = {"x-api-key": "API_KEY"}
# Inference arguments (optional)
data = {"imgsz": 640, "conf": 0.25, "iou": 0.45}
# Load image and send request
with open("path/to/image.jpg", "rb") as image_file:
files = {"file": image_file}
response = requests.post(url, headers=headers, files=files, data=data)
print(response.json())
```
=== "Response"
```json
{
"images": [
{
"results": [
{
"class": 0,
"name": "person",
"confidence": 0.92,
"box": {
"x1": 118,
"x2": 416,
"y1": 112,
"y2": 660
}
}
],
"shape": [
750,
600
],
"speed": {
"inference": 200.8,
"postprocess": 0.8,
"preprocess": 2.8
}
}
],
"metadata": ...
}
```
### OBB
!!! Example "OBB Model"
=== "`ultralytics`"
```python
from ultralytics import YOLO
# Load model
model = YOLO("yolov8n-obb.pt")
# Run inference
results = model("image.jpg")
# Print image.jpg results in JSON format
print(results[0].tojson())
```
=== "cURL"
```bash
curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
-H "x-api-key: API_KEY" \
-F "file=@/path/to/image.jpg" \
-F "imgsz=640" \
-F "conf=0.25" \
-F "iou=0.45"
```
=== "Python"
```python
import requests
# API URL, use actual MODEL_ID
url = "https://api.ultralytics.com/v1/predict/MODEL_ID"
# Headers, use actual API_KEY
headers = {"x-api-key": "API_KEY"}
# Inference arguments (optional)
data = {"imgsz": 640, "conf": 0.25, "iou": 0.45}
# Load image and send request
with open("path/to/image.jpg", "rb") as image_file:
files = {"file": image_file}
response = requests.post(url, headers=headers, files=files, data=data)
print(response.json())
```
=== "Response"
```json
{
"images": [
{
"results": [
{
"class": 0,
"name": "person",
"confidence": 0.92,
"box": {
"x1": 374.85565,
"x2": 392.31824,
"x3": 412.81805,
"x4": 395.35547,
"y1": 264.40704,
"y2": 267.45728,
"y3": 150.0966,
"y4": 147.04634
}
}
],
"shape": [
750,
600
],
"speed": {
"inference": 200.8,
"postprocess": 0.8,
"preprocess": 2.8
}
}
],
"metadata": ...
}
```
### Segmentation
!!! Example "Segmentation Model"
=== "`ultralytics`"
```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())
```
=== "cURL"
```bash
curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
-H "x-api-key: API_KEY" \
-F "file=@/path/to/image.jpg" \
-F "imgsz=640" \
-F "conf=0.25" \
-F "iou=0.45"
```
=== "Python"
```python
import requests
# API URL, use actual MODEL_ID
url = "https://api.ultralytics.com/v1/predict/MODEL_ID"
# Headers, use actual API_KEY
headers = {"x-api-key": "API_KEY"}
# Inference arguments (optional)
data = {"imgsz": 640, "conf": 0.25, "iou": 0.45}
# Load image and send request
with open("path/to/image.jpg", "rb") as image_file:
files = {"file": image_file}
response = requests.post(url, headers=headers, files=files, data=data)
print(response.json())
```
=== "Response"
```json
{
"images": [
{
"results": [
{
"class": 0,
"name": "person",
"confidence": 0.92,
"box": {
"x1": 118,
"x2": 416,
"y1": 112,
"y2": 660
},
"segments": {
"x": [
266.015625,
266.015625,
258.984375,
...
],
"y": [
110.15625,
113.67188262939453,
120.70311737060547,
...
]
}
}
],
"shape": [
750,
600
],
"speed": {
"inference": 200.8,
"postprocess": 0.8,
"preprocess": 2.8
}
}
],
"metadata": ...
}
```
### Pose
!!! Example "Pose Model"
=== "`ultralytics`"
```python
from ultralytics import YOLO
# Load model
model = YOLO("yolov8n-pose.pt")
# Run inference
results = model("image.jpg")
# Print image.jpg results in JSON format
print(results[0].tojson())
```
=== "cURL"
```bash
curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
-H "x-api-key: API_KEY" \
-F "file=@/path/to/image.jpg" \
-F "imgsz=640" \
-F "conf=0.25" \
-F "iou=0.45"
```
=== "Python"
```python
import requests
# API URL, use actual MODEL_ID
url = "https://api.ultralytics.com/v1/predict/MODEL_ID"
# Headers, use actual API_KEY
headers = {"x-api-key": "API_KEY"}
# Inference arguments (optional)
data = {"imgsz": 640, "conf": 0.25, "iou": 0.45}
# Load image and send request
with open("path/to/image.jpg", "rb") as image_file:
files = {"file": image_file}
response = requests.post(url, headers=headers, files=files, data=data)
print(response.json())
```
=== "Response"
```json
{
"images": [
{
"results": [
{
"class": 0,
"name": "person",
"confidence": 0.92,
"box": {
"x1": 118,
"x2": 416,
"y1": 112,
"y2": 660
},
"keypoints": {
"visible": [
0.9909399747848511,
0.8162999749183655,
0.9872099757194519,
...
],
"x": [
316.3871765136719,
315.9374694824219,
304.878173828125,
...
],
"y": [
156.4207763671875,
148.05775451660156,
144.93240356445312,
...
]
}
}
],
"shape": [
750,
600
],
"speed": {
"inference": 200.8,
"postprocess": 0.8,
"preprocess": 2.8
}
}
],
"metadata": ...
}
```
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