Ultralytics HUB 推理 API
在您训练模型后,您可以免费使用 Shared Inference API。如果您是 Pro 用户,则可以访问 Dedicated Inference API。Ultralytics HUB Inference API 允许您通过我们的 REST API 运行推理,而无需在本地安装和设置 Ultralytics YOLO 环境。
观看: Ultralytics HUB 推理 API 演练
专用推理 API
为了响应市场的强烈需求和广泛关注,我们非常激动地推出 Ultralytics HUB 专用推理 API,为我们的 Pro 用户提供一键式专用环境部署!
注意
我们很高兴在公开测试期间免费提供此功能,作为 Pro Plan 的一部分,将来可能会有付费层级。
- 全球覆盖: 部署在全球 38 个区域,确保从任何位置进行低延迟访问。查看 Google Cloud 区域的完整列表。
- Google Cloud Run 支持: 由 Google Cloud Run 提供支持,提供无限可扩展且高度可靠的基础设施。
- 高速度: 根据 Ultralytics 测试,对于来自附近区域的 640 分辨率的 YOLOv8n 推理,可能实现低于 100 毫秒的延迟。
- 增强的安全性: 提供强大的安全功能来保护您的数据,并确保符合行业标准。详细了解 Google Cloud 安全性。
要使用 Ultralytics HUB 专用推理 API,请点击 启动端点 按钮。接下来,按照以下指南中的说明使用唯一的端点 URL。
提示
选择延迟最低的区域以获得最佳性能,如文档中所述。
要关闭专用端点,请单击停止端点按钮。
共享推理 API
要使用 Ultralytics HUB 共享推理 API,请按照以下指南操作。
Ultralytics HUB共享推理API具有以下使用限制:
- 每小时100次呼叫
Python
要使用 Python 访问 Ultralytics HUB 推理 API,请使用以下代码:
import requests
# API URL
url = "https://predict.ultralytics.com"
# Headers, use actual API_KEY
headers = {"x-api-key": "API_KEY"}
# Inference arguments (use actual MODEL_ID)
data = {"model": "https://hub.ultralytics.com/models/MODEL_ID", "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())
注意
替换 MODEL_ID
使用所需的模型 ID, API_KEY
以及您实际的 API 密钥,以及 path/to/image.jpg
以及您想要运行推理的图像路径。
如果您正在使用我们的 专用推理 API,替换 url
也一样。
cURL
要使用 cURL 访问 Ultralytics HUB 推理 API,请使用以下代码:
curl -X POST "https://predict.ultralytics.com" \
-H "x-api-key: API_KEY" \
-F "model=https://hub.ultralytics.com/models/MODEL_ID" \
-F "file=@/path/to/image.jpg" \
-F "imgsz=640" \
-F "conf=0.25" \
-F "iou=0.45"
注意
替换 MODEL_ID
使用所需的模型 ID, API_KEY
以及您实际的 API 密钥,以及 path/to/image.jpg
以及您想要运行推理的图像路径。
如果您正在使用我们的 专用推理 API,替换 url
也一样。
参数
请参阅下表,获取可用推理参数的完整列表。
参数 | 默认值 | 类型 | 描述 |
---|---|---|---|
file |
file |
用于推理的图像或视频文件。 | |
imgsz |
640 |
int |
输入图像的尺寸,有效范围是 32 - 1280 像素。 |
conf |
0.25 |
float |
预测的置信度阈值,有效范围 0.01 - 1.0 . |
iou |
0.45 |
float |
交并比 (IoU)阈值,有效范围 0.0 - 0.95 . |
响应
Ultralytics HUB推理API返回JSON响应。
分类
分类模型
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].to_json())
curl -X POST "https://predict.ultralytics.com" \
-H "x-api-key: API_KEY" \
-F "model=https://hub.ultralytics.com/models/MODEL_ID" \
-F "file=@/path/to/image.jpg" \
-F "imgsz=640" \
-F "conf=0.25" \
-F "iou=0.45"
import requests
# API URL
url = "https://predict.ultralytics.com"
# Headers, use actual API_KEY
headers = {"x-api-key": "API_KEY"}
# Inference arguments (use actual MODEL_ID)
data = {"model": "https://hub.ultralytics.com/models/MODEL_ID", "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())
{
"images": [
{
"results": [
{
"class": 0,
"name": "person",
"confidence": 0.92
}
],
"shape": [
750,
600
],
"speed": {
"inference": 200.8,
"postprocess": 0.8,
"preprocess": 2.8
}
}
],
"metadata": ...
}
检测
检测模型
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].to_json())
curl -X POST "https://predict.ultralytics.com" \
-H "x-api-key: API_KEY" \
-F "model=https://hub.ultralytics.com/models/MODEL_ID" \
-F "file=@/path/to/image.jpg" \
-F "imgsz=640" \
-F "conf=0.25" \
-F "iou=0.45"
import requests
# API URL
url = "https://predict.ultralytics.com"
# Headers, use actual API_KEY
headers = {"x-api-key": "API_KEY"}
# Inference arguments (use actual MODEL_ID)
data = {"model": "https://hub.ultralytics.com/models/MODEL_ID", "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())
{
"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
OBB 模型
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 -X POST "https://predict.ultralytics.com" \
-H "x-api-key: API_KEY" \
-F "model=https://hub.ultralytics.com/models/MODEL_ID" \
-F "file=@/path/to/image.jpg" \
-F "imgsz=640" \
-F "conf=0.25" \
-F "iou=0.45"
import requests
# API URL
url = "https://predict.ultralytics.com"
# Headers, use actual API_KEY
headers = {"x-api-key": "API_KEY"}
# Inference arguments (use actual MODEL_ID)
data = {"model": "https://hub.ultralytics.com/models/MODEL_ID", "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())
{
"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": ...
}
分割
分割模型
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 -X POST "https://predict.ultralytics.com" \
-H "x-api-key: API_KEY" \
-F "model=https://hub.ultralytics.com/models/MODEL_ID" \
-F "file=@/path/to/image.jpg" \
-F "imgsz=640" \
-F "conf=0.25" \
-F "iou=0.45"
import requests
# API URL
url = "https://predict.ultralytics.com"
# Headers, use actual API_KEY
headers = {"x-api-key": "API_KEY"}
# Inference arguments (use actual MODEL_ID)
data = {"model": "https://hub.ultralytics.com/models/MODEL_ID", "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())
{
"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": ...
}
姿势估计
姿势估计模型
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 -X POST "https://predict.ultralytics.com" \
-H "x-api-key: API_KEY" \
-F "model=https://hub.ultralytics.com/models/MODEL_ID" \
-F "file=@/path/to/image.jpg" \
-F "imgsz=640" \
-F "conf=0.25" \
-F "iou=0.45"
import requests
# API URL
url = "https://predict.ultralytics.com"
# Headers, use actual API_KEY
headers = {"x-api-key": "API_KEY"}
# Inference arguments (use actual MODEL_ID)
data = {"model": "https://hub.ultralytics.com/models/MODEL_ID", "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())
{
"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": ...
}