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ultralytics 8.0.141 create new SettingsManager (#3790)

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Glenn Jocher 2023-07-23 16:03:34 +02:00 • committed by GitHub
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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
}
]
}
```
```