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Reformat Markdown code blocks (#12795)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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docs/en/modes/predict.md
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@ -58,10 +58,10 @@ Ultralytics YOLO models return either a Python list of `Results` objects, or a m
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n.pt') # pretrained YOLOv8n model
model = YOLO("yolov8n.pt") # pretrained YOLOv8n model
# Run batched inference on a list of images
results = model(['im1.jpg', 'im2.jpg']) # return a list of Results objects
results = model(["im1.jpg", "im2.jpg"]) # return a list of Results objects
# Process results list
for result in results:
@ -71,7 +71,7 @@ Ultralytics YOLO models return either a Python list of `Results` objects, or a m
probs = result.probs # Probs object for classification outputs
obb = result.obb # Oriented boxes object for OBB outputs
result.show() # display to screen
result.save(filename='result.jpg') # save to disk
result.save(filename="result.jpg") # save to disk
```
=== "Return a generator with `stream=True`"
@ -80,10 +80,10 @@ Ultralytics YOLO models return either a Python list of `Results` objects, or a m
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n.pt') # pretrained YOLOv8n model
model = YOLO("yolov8n.pt") # pretrained YOLOv8n model
# Run batched inference on a list of images
results = model(['im1.jpg', 'im2.jpg'], stream=True) # return a generator of Results objects
results = model(["im1.jpg", "im2.jpg"], stream=True) # return a generator of Results objects
# Process results generator
for result in results:
@ -93,7 +93,7 @@ Ultralytics YOLO models return either a Python list of `Results` objects, or a m
probs = result.probs # Probs object for classification outputs
obb = result.obb # Oriented boxes object for OBB outputs
result.show() # display to screen
result.save(filename='result.jpg') # save to disk
result.save(filename="result.jpg") # save to disk
```
## Inference Sources
@ -132,10 +132,10 @@ Below are code examples for using each source type:
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
model = YOLO("yolov8n.pt")
# Define path to the image file
source = 'path/to/image.jpg'
source = "path/to/image.jpg"
# Run inference on the source
results = model(source) # list of Results objects
@ -148,10 +148,10 @@ Below are code examples for using each source type:
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
model = YOLO("yolov8n.pt")
# Define current screenshot as source
source = 'screen'
source = "screen"
# Run inference on the source
results = model(source) # list of Results objects
@ -164,10 +164,10 @@ Below are code examples for using each source type:
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
model = YOLO("yolov8n.pt")
# Define remote image or video URL
source = 'https://ultralytics.com/images/bus.jpg'
source = "https://ultralytics.com/images/bus.jpg"
# Run inference on the source
results = model(source) # list of Results objects
@ -181,10 +181,10 @@ Below are code examples for using each source type:
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
model = YOLO("yolov8n.pt")
# Open an image using PIL
source = Image.open('path/to/image.jpg')
source = Image.open("path/to/image.jpg")
# Run inference on the source
results = model(source) # list of Results objects
@ -198,10 +198,10 @@ Below are code examples for using each source type:
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
model = YOLO("yolov8n.pt")
# Read an image using OpenCV
source = cv2.imread('path/to/image.jpg')
source = cv2.imread("path/to/image.jpg")
# Run inference on the source
results = model(source) # list of Results objects
@ -215,10 +215,10 @@ Below are code examples for using each source type:
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
model = YOLO("yolov8n.pt")
# Create a random numpy array of HWC shape (640, 640, 3) with values in range [0, 255] and type uint8
source = np.random.randint(low=0, high=255, size=(640, 640, 3), dtype='uint8')
source = np.random.randint(low=0, high=255, size=(640, 640, 3), dtype="uint8")
# Run inference on the source
results = model(source) # list of Results objects
@ -232,7 +232,7 @@ Below are code examples for using each source type:
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
model = YOLO("yolov8n.pt")
# Create a random torch tensor of BCHW shape (1, 3, 640, 640) with values in range [0, 1] and type float32
source = torch.rand(1, 3, 640, 640, dtype=torch.float32)
@ -249,10 +249,10 @@ Below are code examples for using each source type:
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
model = YOLO("yolov8n.pt")
# Define a path to a CSV file with images, URLs, videos and directories
source = 'path/to/file.csv'
source = "path/to/file.csv"
# Run inference on the source
results = model(source) # list of Results objects
@ -265,10 +265,10 @@ Below are code examples for using each source type:
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
model = YOLO("yolov8n.pt")
# Define path to video file
source = 'path/to/video.mp4'
source = "path/to/video.mp4"
# Run inference on the source
results = model(source, stream=True) # generator of Results objects
@ -281,10 +281,10 @@ Below are code examples for using each source type:
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
model = YOLO("yolov8n.pt")
# Define path to directory containing images and videos for inference
source = 'path/to/dir'
source = "path/to/dir"
# Run inference on the source
results = model(source, stream=True) # generator of Results objects
@ -297,13 +297,13 @@ Below are code examples for using each source type:
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
model = YOLO("yolov8n.pt")
# Define a glob search for all JPG files in a directory
source = 'path/to/dir/*.jpg'
source = "path/to/dir/*.jpg"
# OR define a recursive glob search for all JPG files including subdirectories
source = 'path/to/dir/**/*.jpg'
source = "path/to/dir/**/*.jpg"
# Run inference on the source
results = model(source, stream=True) # generator of Results objects
@ -316,10 +316,10 @@ Below are code examples for using each source type:
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
model = YOLO("yolov8n.pt")
# Define source as YouTube video URL
source = 'https://youtu.be/LNwODJXcvt4'
source = "https://youtu.be/LNwODJXcvt4"
# Run inference on the source
results = model(source, stream=True) # generator of Results objects
@ -332,13 +332,13 @@ Below are code examples for using each source type:
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
model = YOLO("yolov8n.pt")
# Single stream with batch-size 1 inference
source = 'rtsp://example.com/media.mp4' # RTSP, RTMP, TCP or IP streaming address
source = "rtsp://example.com/media.mp4" # RTSP, RTMP, TCP or IP streaming address
# Multiple streams with batched inference (i.e. batch-size 8 for 8 streams)
source = 'path/to/list.streams' # *.streams text file with one streaming address per row
source = "path/to/list.streams" # *.streams text file with one streaming address per row
# Run inference on the source
results = model(source, stream=True) # generator of Results objects
@ -354,10 +354,10 @@ Below are code examples for using each source type:
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
model = YOLO("yolov8n.pt")
# Run inference on 'bus.jpg' with arguments
model.predict('bus.jpg', save=True, imgsz=320, conf=0.5)
model.predict("bus.jpg", save=True, imgsz=320, conf=0.5)
```
Inference arguments:
@ -445,11 +445,11 @@ All Ultralytics `predict()` calls will return a list of `Results` objects:
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
model = YOLO("yolov8n.pt")
# Run inference on an image
results = model('bus.jpg') # list of 1 Results object
results = model(['bus.jpg', 'zidane.jpg']) # list of 2 Results objects
results = model("bus.jpg") # list of 1 Results object
results = model(["bus.jpg", "zidane.jpg"]) # list of 2 Results objects
```
`Results` objects have the following attributes:
@ -497,10 +497,10 @@ For more details see the [`Results` class documentation](../reference/engine/res
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
model = YOLO("yolov8n.pt")
# Run inference on an image
results = model('bus.jpg') # results list
results = model("bus.jpg") # results list
# View results
for r in results:
@ -535,10 +535,10 @@ For more details see the [`Boxes` class documentation](../reference/engine/resul
from ultralytics import YOLO
# Load a pretrained YOLOv8n-seg Segment model
model = YOLO('yolov8n-seg.pt')
model = YOLO("yolov8n-seg.pt")
# Run inference on an image
results = model('bus.jpg') # results list
results = model("bus.jpg") # results list
# View results
for r in results:
@ -568,10 +568,10 @@ For more details see the [`Masks` class documentation](../reference/engine/resul
from ultralytics import YOLO
# Load a pretrained YOLOv8n-pose Pose model
model = YOLO('yolov8n-pose.pt')
model = YOLO("yolov8n-pose.pt")
# Run inference on an image
results = model('bus.jpg') # results list
results = model("bus.jpg") # results list
# View results
for r in results:
@ -602,10 +602,10 @@ For more details see the [`Keypoints` class documentation](../reference/engine/r
from ultralytics import YOLO
# Load a pretrained YOLOv8n-cls Classify model
model = YOLO('yolov8n-cls.pt')
model = YOLO("yolov8n-cls.pt")
# Run inference on an image
results = model('bus.jpg') # results list
results = model("bus.jpg") # results list
# View results
for r in results:
@ -637,10 +637,10 @@ For more details see the [`Probs` class documentation](../reference/engine/resul
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n-obb.pt')
model = YOLO("yolov8n-obb.pt")
# Run inference on an image
results = model('bus.jpg') # results list
results = model("bus.jpg") # results list
# View results
for r in results:
@ -676,22 +676,22 @@ The `plot()` method in `Results` objects facilitates visualization of prediction
from ultralytics import YOLO
# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')
model = YOLO("yolov8n.pt")
# Run inference on 'bus.jpg'
results = model(['bus.jpg', 'zidane.jpg']) # results list
results = model(["bus.jpg", "zidane.jpg"]) # results list
# Visualize the results
for i, r in enumerate(results):
# Plot results image
im_bgr = r.plot() # BGR-order numpy array
im_rgb = Image.fromarray(im_bgr[..., ::-1]) # RGB-order PIL image
# Show results to screen (in supported environments)
r.show()
# Save results to disk
r.save(filename=f'results{i}.jpg')
r.save(filename=f"results{i}.jpg")
```
### `plot()` Method Parameters
@ -727,9 +727,11 @@ When using YOLO models in a multi-threaded application, it's important to instan
Instantiate a single model inside each thread for thread-safe inference:
```python
from ultralytics import YOLO
from threading import Thread
from ultralytics import YOLO
def thread_safe_predict(image_path):
"""Performs thread-safe prediction on an image using a locally instantiated YOLO model."""
local_model = YOLO("yolov8n.pt")
@ -755,7 +757,7 @@ Here's a Python script using OpenCV (`cv2`) and YOLOv8 to run inference on video
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO('yolov8n.pt')
model = YOLO("yolov8n.pt")
# Open the video file
video_path = "path/to/your/video/file.mp4"