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comments	description	keywords
true	Discover how to use YOLOv8 predict mode for various tasks. Learn about different inference sources like images, videos, and data formats.	Ultralytics, YOLOv8, predict mode, inference sources, prediction tasks, streaming mode, image processing, video processing, machine learning, AI

Model Prediction with Ultralytics YOLO

Ultralytics YOLO ecosystem and integrations

Introduction

In the world of machine learning and computer vision, the process of making sense out of visual data is called 'inference' or 'prediction'. Ultralytics YOLOv8 offers a powerful feature known as predict mode that is tailored for high-performance, real-time inference on a wide range of data sources.

Watch: How to Extract the Outputs from Ultralytics YOLOv8 Model for Custom Projects.

Real-world Applications

Manufacturing	Sports	Safety

Vehicle Spare Parts Detection	Football Player Detection	People Fall Detection

Why Use Ultralytics YOLO for Inference?

Here's why you should consider YOLOv8's predict mode for your various inference needs:

Versatility: Capable of making inferences on images, videos, and even live streams.
Performance: Engineered for real-time, high-speed processing without sacrificing accuracy.
Ease of Use: Intuitive Python and CLI interfaces for rapid deployment and testing.
Highly Customizable: Various settings and parameters to tune the model's inference behavior according to your specific requirements.

Key Features of Predict Mode

YOLOv8's predict mode is designed to be robust and versatile, featuring:

Multiple Data Source Compatibility: Whether your data is in the form of individual images, a collection of images, video files, or real-time video streams, predict mode has you covered.
Streaming Mode: Use the streaming feature to generate a memory-efficient generator of Results objects. Enable this by setting stream=True in the predictor's call method.
Batch Processing: The ability to process multiple images or video frames in a single batch, further speeding up inference time.
Integration Friendly: Easily integrate with existing data pipelines and other software components, thanks to its flexible API.

Ultralytics YOLO models return either a Python list of Results objects, or a memory-efficient Python generator of Results objects when stream=True is passed to the model during inference:

!!! Example "Predict"

=== "Return a list with `stream=False`"

    ```python
    from ultralytics import YOLO

    # Load a 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

    # Process results list
    for result in results:
        boxes = result.boxes  # Boxes object for bbox outputs
        masks = result.masks  # Masks object for segmentation masks outputs
        keypoints = result.keypoints  # Keypoints object for pose outputs
        probs = result.probs  # Probs object for classification outputs
    ```

=== "Return a generator with `stream=True`"

    ```python
    from ultralytics import YOLO

    # Load a 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

    # Process results generator
    for result in results:
        boxes = result.boxes  # Boxes object for bbox outputs
        masks = result.masks  # Masks object for segmentation masks outputs
        keypoints = result.keypoints  # Keypoints object for pose outputs
        probs = result.probs  # Probs object for classification outputs
    ```

Inference Sources

YOLOv8 can process different types of input sources for inference, as shown in the table below. The sources include static images, video streams, and various data formats. The table also indicates whether each source can be used in streaming mode with the argument stream=True ✅. Streaming mode is beneficial for processing videos or live streams as it creates a generator of results instead of loading all frames into memory.

!!! Tip "Tip"

Use `stream=True` for processing long videos or large datasets to efficiently manage memory. When `stream=False`, the results for all frames or data points are stored in memory, which can quickly add up and cause out-of-memory errors for large inputs. In contrast, `stream=True` utilizes a generator, which only keeps the results of the current frame or data point in memory, significantly reducing memory consumption and preventing out-of-memory issues.

Source	Argument	Type	Notes
image	`'image.jpg'`	`str` or `Path`	Single image file.
URL	`'https://ultralytics.com/images/bus.jpg'`	`str`	URL to an image.
screenshot	`'screen'`	`str`	Capture a screenshot.
PIL	`Image.open('im.jpg')`	`PIL.Image`	HWC format with RGB channels.
OpenCV	`cv2.imread('im.jpg')`	`np.ndarray`	HWC format with BGR channels `uint8 (0-255)`.
numpy	`np.zeros((640,1280,3))`	`np.ndarray`	HWC format with BGR channels `uint8 (0-255)`.
torch	`torch.zeros(16,3,320,640)`	`torch.Tensor`	BCHW format with RGB channels `float32 (0.0-1.0)`.
CSV	`'sources.csv'`	`str` or `Path`	CSV file containing paths to images, videos, or directories.
video ✅	`'video.mp4'`	`str` or `Path`	Video file in formats like MP4, AVI, etc.
directory ✅	`'path/'`	`str` or `Path`	Path to a directory containing images or videos.
glob ✅	`'path/*.jpg'`	`str`	Glob pattern to match multiple files. Use the `*` character as a wildcard.
YouTube ✅	`'https://youtu.be/LNwODJXcvt4'`	`str`	URL to a YouTube video.
stream ✅	`'rtsp://example.com/media.mp4'`	`str`	URL for streaming protocols such as RTSP, RTMP, TCP, or an IP address.
multi-stream ✅	`'list.streams'`	`str` or `Path`	`*.streams` text file with one stream URL per row, i.e. 8 streams will run at batch-size 8.

Below are code examples for using each source type:

!!! Example "Prediction sources"

=== "image"

    Run inference on an image file.
    ```python
    from ultralytics import YOLO

    # Load a pretrained YOLOv8n model
    model = YOLO('yolov8n.pt')

    # Define path to the image file
    source = 'path/to/image.jpg'

    # Run inference on the source
    results = model(source)  # list of Results objects
    ```

=== "screenshot"

    Run inference on the current screen content as a screenshot.
    ```python
    from ultralytics import YOLO

    # Load a pretrained YOLOv8n model
    model = YOLO('yolov8n.pt')

    # Define current screenshot as source
    source = 'screen'

    # Run inference on the source
    results = model(source)  # list of Results objects
    ```

=== "URL"

    Run inference on an image or video hosted remotely via URL.
    ```python
    from ultralytics import YOLO

    # Load a pretrained YOLOv8n model
    model = YOLO('yolov8n.pt')

    # Define remote image or video URL
    source = 'https://ultralytics.com/images/bus.jpg'

    # Run inference on the source
    results = model(source)  # list of Results objects
    ```

=== "PIL"

    Run inference on an image opened with Python Imaging Library (PIL).
    ```python
    from PIL import Image
    from ultralytics import YOLO

    # Load a pretrained YOLOv8n model
    model = YOLO('yolov8n.pt')

    # Open an image using PIL
    source = Image.open('path/to/image.jpg')

    # Run inference on the source
    results = model(source)  # list of Results objects
    ```

=== "OpenCV"

    Run inference on an image read with OpenCV.
    ```python
    import cv2
    from ultralytics import YOLO

    # Load a pretrained YOLOv8n model
    model = YOLO('yolov8n.pt')

    # Read an image using OpenCV
    source = cv2.imread('path/to/image.jpg')

    # Run inference on the source
    results = model(source)  # list of Results objects
    ```

=== "numpy"

    Run inference on an image represented as a numpy array.
    ```python
    import numpy as np
    from ultralytics import YOLO

    # Load a pretrained YOLOv8n model
    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')

    # Run inference on the source
    results = model(source)  # list of Results objects
    ```

=== "torch"

    Run inference on an image represented as a PyTorch tensor.
    ```python
    import torch
    from ultralytics import YOLO

    # Load a pretrained YOLOv8n model
    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)

    # Run inference on the source
    results = model(source)  # list of Results objects
    ```

=== "CSV"

    Run inference on a collection of images, URLs, videos and directories listed in a CSV file.
    ```python
    import torch
    from ultralytics import YOLO

    # Load a pretrained YOLOv8n model
    model = YOLO('yolov8n.pt')

    # Define a path to a CSV file with images, URLs, videos and directories
    source = 'path/to/file.csv'

    # Run inference on the source
    results = model(source)  # list of Results objects
    ```

=== "video"

    Run inference on a video file. By using `stream=True`, you can create a generator of Results objects to reduce memory usage.
    ```python
    from ultralytics import YOLO

    # Load a pretrained YOLOv8n model
    model = YOLO('yolov8n.pt')

    # Define path to video file
    source = 'path/to/video.mp4'

    # Run inference on the source
    results = model(source, stream=True)  # generator of Results objects
    ```

=== "directory"

    Run inference on all images and videos in a directory. To also capture images and videos in subdirectories use a glob pattern, i.e. `path/to/dir/**/*`.
    ```python
    from ultralytics import YOLO

    # Load a pretrained YOLOv8n model
    model = YOLO('yolov8n.pt')

    # Define path to directory containing images and videos for inference
    source = 'path/to/dir'

    # Run inference on the source
    results = model(source, stream=True)  # generator of Results objects
    ```

=== "glob"

    Run inference on all images and videos that match a glob expression with `*` characters.
    ```python
    from ultralytics import YOLO

    # Load a pretrained YOLOv8n model
    model = YOLO('yolov8n.pt')

    # Define a glob search for all JPG files in a directory
    source = 'path/to/dir/*.jpg'

    # OR define a recursive glob search for all JPG files including subdirectories
    source = 'path/to/dir/**/*.jpg'

    # Run inference on the source
    results = model(source, stream=True)  # generator of Results objects
    ```

=== "YouTube"

    Run inference on a YouTube video. By using `stream=True`, you can create a generator of Results objects to reduce memory usage for long videos.
    ```python
    from ultralytics import YOLO

    # Load a pretrained YOLOv8n model
    model = YOLO('yolov8n.pt')

    # Define source as YouTube video URL
    source = 'https://youtu.be/LNwODJXcvt4'

    # Run inference on the source
    results = model(source, stream=True)  # generator of Results objects
    ```

=== "Streams"

    Run inference on remote streaming sources using RTSP, RTMP, TCP and IP address protocols. If multiple streams are provided in a `*.streams` text file then batched inference will run, i.e. 8 streams will run at batch-size 8, otherwise single streams will run at batch-size 1.
    ```python
    from ultralytics import YOLO

    # Load a pretrained YOLOv8n model
    model = YOLO('yolov8n.pt')

    # Single stream with batch-size 1 inference
    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

    # Run inference on the source
    results = model(source, stream=True)  # generator of Results objects
    ```

Inference Arguments

model.predict() accepts multiple arguments that can be passed at inference time to override defaults:

!!! Example

```python
from ultralytics import YOLO

# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')

# Run inference on 'bus.jpg' with arguments
model.predict('bus.jpg', save=True, imgsz=320, conf=0.5)
```

Inference arguments:

Name	Type	Default	Description
`source`	`str`	`'ultralytics/assets'`	source directory for images or videos
`conf`	`float`	`0.25`	object confidence threshold for detection
`iou`	`float`	`0.7`	intersection over union (IoU) threshold for NMS
`imgsz`	`int or tuple`	`640`	image size as scalar or (h, w) list, i.e. (640, 480)
`half`	`bool`	`False`	use half precision (FP16)
`device`	`None or str`	`None`	device to run on, i.e. cuda device=0/1/2/3 or device=cpu
`max_det`	`int`	`300`	maximum number of detections per image
`vid_stride`	`bool`	`False`	video frame-rate stride
`stream_buffer`	`bool`	`False`	buffer all streaming frames (True) or return the most recent frame (False)
`visualize`	`bool`	`False`	visualize model features
`augment`	`bool`	`False`	apply image augmentation to prediction sources
`agnostic_nms`	`bool`	`False`	class-agnostic NMS
`classes`	`list[int]`	`None`	filter results by class, i.e. classes=0, or classes=[0,2,3]
`retina_masks`	`bool`	`False`	use high-resolution segmentation masks
`embed`	`list[int]`	`None`	return feature vectors/embeddings from given layers

Visualization arguments:

Name	Type	Default	Description
`show`	`bool`	`False`	show predicted images and videos if environment allows
`save`	`bool`	`False`	save predicted images and videos
`save_frames`	`bool`	`False`	save predicted individual video frames
`save_txt`	`bool`	`False`	save results as `.txt` file
`save_conf`	`bool`	`False`	save results with confidence scores
`save_crop`	`bool`	`False`	save cropped images with results
`show_labels`	`bool`	`True`	show prediction labels, i.e. 'person'
`show_conf`	`bool`	`True`	show prediction confidence, i.e. '0.99'
`show_boxes`	`bool`	`True`	show prediction boxes
`line_width`	`None or int`	`None`	line width of the bounding boxes. Scaled to image size if None.

Image and Video Formats

YOLOv8 supports various image and video formats, as specified in data/utils.py. See the tables below for the valid suffixes and example predict commands.

Images

The below table contains valid Ultralytics image formats.

Image Suffixes	Example Predict Command	Reference
`.bmp`	`yolo predict source=image.bmp`	Microsoft BMP File Format
`.dng`	`yolo predict source=image.dng`	Adobe DNG
`.jpeg`	`yolo predict source=image.jpeg`	JPEG
`.jpg`	`yolo predict source=image.jpg`	JPEG
`.mpo`	`yolo predict source=image.mpo`	Multi Picture Object
`.png`	`yolo predict source=image.png`	Portable Network Graphics
`.tif`	`yolo predict source=image.tif`	Tag Image File Format
`.tiff`	`yolo predict source=image.tiff`	Tag Image File Format
`.webp`	`yolo predict source=image.webp`	WebP
`.pfm`	`yolo predict source=image.pfm`	Portable FloatMap

Videos

The below table contains valid Ultralytics video formats.

Video Suffixes	Example Predict Command	Reference
`.asf`	`yolo predict source=video.asf`	Advanced Systems Format
`.avi`	`yolo predict source=video.avi`	Audio Video Interleave
`.gif`	`yolo predict source=video.gif`	Graphics Interchange Format
`.m4v`	`yolo predict source=video.m4v`	MPEG-4 Part 14
`.mkv`	`yolo predict source=video.mkv`	Matroska
`.mov`	`yolo predict source=video.mov`	QuickTime File Format
`.mp4`	`yolo predict source=video.mp4`	MPEG-4 Part 14 - Wikipedia
`.mpeg`	`yolo predict source=video.mpeg`	MPEG-1 Part 2
`.mpg`	`yolo predict source=video.mpg`	MPEG-1 Part 2
`.ts`	`yolo predict source=video.ts`	MPEG Transport Stream
`.wmv`	`yolo predict source=video.wmv`	Windows Media Video
`.webm`	`yolo predict source=video.webm`	WebM Project

Working with Results

All Ultralytics predict() calls will return a list of Results objects:

!!! Example "Results"

```python
from ultralytics import YOLO

# Load a pretrained YOLOv8n model
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 objects have the following attributes:

Attribute	Type	Description
`orig_img`	`numpy.ndarray`	The original image as a numpy array.
`orig_shape`	`tuple`	The original image shape in (height, width) format.
`boxes`	`Boxes, optional`	A Boxes object containing the detection bounding boxes.
`masks`	`Masks, optional`	A Masks object containing the detection masks.
`probs`	`Probs, optional`	A Probs object containing probabilities of each class for classification task.
`keypoints`	`Keypoints, optional`	A Keypoints object containing detected keypoints for each object.
`obb`	`OBB, optional`	A OBB object containing the oriented detection bounding boxes.
`speed`	`dict`	A dictionary of preprocess, inference, and postprocess speeds in milliseconds per image.
`names`	`dict`	A dictionary of class names.
`path`	`str`	The path to the image file.

Results objects have the following methods:

Method	Return Type	Description
`__getitem__()`	`Results`	Return a Results object for the specified index.
`__len__()`	`int`	Return the number of detections in the Results object.
`update()`	`None`	Update the boxes, masks, and probs attributes of the Results object.
`cpu()`	`Results`	Return a copy of the Results object with all tensors on CPU memory.
`numpy()`	`Results`	Return a copy of the Results object with all tensors as numpy arrays.
`cuda()`	`Results`	Return a copy of the Results object with all tensors on GPU memory.
`to()`	`Results`	Return a copy of the Results object with tensors on the specified device and dtype.
`new()`	`Results`	Return a new Results object with the same image, path, and names.
`keys()`	`List[str]`	Return a list of non-empty attribute names.
`plot()`	`numpy.ndarray`	Plots the detection results. Returns a numpy array of the annotated image.
`verbose()`	`str`	Return log string for each task.
`save_txt()`	`None`	Save predictions into a txt file.
`save_crop()`	`None`	Save cropped predictions to `save_dir/cls/file_name.jpg`.
`tojson()`	`None`	Convert the object to JSON format.

For more details see the Results class documentation.

Boxes

Boxes object can be used to index, manipulate, and convert bounding boxes to different formats.

!!! Example "Boxes"

```python
from ultralytics import YOLO

# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')

# Run inference on an image
results = model('bus.jpg')  # results list

# View results
for r in results:
    print(r.boxes)  # print the Boxes object containing the detection bounding boxes
```

Here is a table for the Boxes class methods and properties, including their name, type, and description:

Name	Type	Description
`cpu()`	Method	Move the object to CPU memory.
`numpy()`	Method	Convert the object to a numpy array.
`cuda()`	Method	Move the object to CUDA memory.
`to()`	Method	Move the object to the specified device.
`xyxy`	Property (`torch.Tensor`)	Return the boxes in xyxy format.
`conf`	Property (`torch.Tensor`)	Return the confidence values of the boxes.
`cls`	Property (`torch.Tensor`)	Return the class values of the boxes.
`id`	Property (`torch.Tensor`)	Return the track IDs of the boxes (if available).
`xywh`	Property (`torch.Tensor`)	Return the boxes in xywh format.
`xyxyn`	Property (`torch.Tensor`)	Return the boxes in xyxy format normalized by original image size.
`xywhn`	Property (`torch.Tensor`)	Return the boxes in xywh format normalized by original image size.

For more details see the Boxes class documentation.

Masks

Masks object can be used index, manipulate and convert masks to segments.

!!! Example "Masks"

```python
from ultralytics import YOLO

# Load a pretrained YOLOv8n-seg Segment model
model = YOLO('yolov8n-seg.pt')

# Run inference on an image
results = model('bus.jpg')  # results list

# View results
for r in results:
    print(r.masks)  # print the Masks object containing the detected instance masks
```

Here is a table for the Masks class methods and properties, including their name, type, and description:

Name	Type	Description
`cpu()`	Method	Returns the masks tensor on CPU memory.
`numpy()`	Method	Returns the masks tensor as a numpy array.
`cuda()`	Method	Returns the masks tensor on GPU memory.
`to()`	Method	Returns the masks tensor with the specified device and dtype.
`xyn`	Property (`torch.Tensor`)	A list of normalized segments represented as tensors.
`xy`	Property (`torch.Tensor`)	A list of segments in pixel coordinates represented as tensors.

For more details see the Masks class documentation.

Keypoints

Keypoints object can be used index, manipulate and normalize coordinates.

!!! Example "Keypoints"

```python
from ultralytics import YOLO

# Load a pretrained YOLOv8n-pose Pose model
model = YOLO('yolov8n-pose.pt')

# Run inference on an image
results = model('bus.jpg')  # results list

# View results
for r in results:
    print(r.keypoints)  # print the Keypoints object containing the detected keypoints
```

Here is a table for the Keypoints class methods and properties, including their name, type, and description:

Name	Type	Description
`cpu()`	Method	Returns the keypoints tensor on CPU memory.
`numpy()`	Method	Returns the keypoints tensor as a numpy array.
`cuda()`	Method	Returns the keypoints tensor on GPU memory.
`to()`	Method	Returns the keypoints tensor with the specified device and dtype.
`xyn`	Property (`torch.Tensor`)	A list of normalized keypoints represented as tensors.
`xy`	Property (`torch.Tensor`)	A list of keypoints in pixel coordinates represented as tensors.
`conf`	Property (`torch.Tensor`)	Returns confidence values of keypoints if available, else None.

For more details see the Keypoints class documentation.

Probs

Probs object can be used index, get top1 and top5 indices and scores of classification.

!!! Example "Probs"

```python
from ultralytics import YOLO

# Load a pretrained YOLOv8n-cls Classify model
model = YOLO('yolov8n-cls.pt')

# Run inference on an image
results = model('bus.jpg')  # results list

# View results
for r in results:
    print(r.probs)  # print the Probs object containing the detected class probabilities
```

Here's a table summarizing the methods and properties for the Probs class:

Name	Type	Description
`cpu()`	Method	Returns a copy of the probs tensor on CPU memory.
`numpy()`	Method	Returns a copy of the probs tensor as a numpy array.
`cuda()`	Method	Returns a copy of the probs tensor on GPU memory.
`to()`	Method	Returns a copy of the probs tensor with the specified device and dtype.
`top1`	Property (`int`)	Index of the top 1 class.
`top5`	Property (`list[int]`)	Indices of the top 5 classes.
`top1conf`	Property (`torch.Tensor`)	Confidence of the top 1 class.
`top5conf`	Property (`torch.Tensor`)	Confidences of the top 5 classes.

For more details see the Probs class documentation.

OBB

OBB object can be used to index, manipulate, and convert oriented bounding boxes to different formats.

!!! Example "OBB"

```python
from ultralytics import YOLO

# Load a pretrained YOLOv8n model
model = YOLO('yolov8n-obb.pt')

# Run inference on an image
results = model('bus.jpg')  # results list

# View results
for r in results:
    print(r.obb)  # print the OBB object containing the oriented detection bounding boxes
```

Here is a table for the OBB class methods and properties, including their name, type, and description:

Name	Type	Description
`cpu()`	Method	Move the object to CPU memory.
`numpy()`	Method	Convert the object to a numpy array.
`cuda()`	Method	Move the object to CUDA memory.
`to()`	Method	Move the object to the specified device.
`conf`	Property (`torch.Tensor`)	Return the confidence values of the boxes.
`cls`	Property (`torch.Tensor`)	Return the class values of the boxes.
`id`	Property (`torch.Tensor`)	Return the track IDs of the boxes (if available).
`xyxy`	Property (`torch.Tensor`)	Return the horizontal boxes in xyxy format.
`xywhr`	Property (`torch.Tensor`)	Return the rotated boxes in xywhr format.
`xyxyxyxy`	Property (`torch.Tensor`)	Return the rotated boxes in xyxyxyxy format.
`xyxyxyxyn`	Property (`torch.Tensor`)	Return the rotated boxes in xyxyxyxy format normalized by image size.

For more details see the OBB class documentation.

Plotting Results

You can use the plot() method of a Result objects to visualize predictions. It plots all prediction types (boxes, masks, keypoints, probabilities, etc.) contained in the Results object onto a numpy array that can then be shown or saved.

!!! Example "Plotting"

```python
from PIL import Image
from ultralytics import YOLO

# Load a pretrained YOLOv8n model
model = YOLO('yolov8n.pt')

# Run inference on 'bus.jpg'
results = model('bus.jpg')  # results list

# Show the results
for r in results:
    im_array = r.plot()  # plot a BGR numpy array of predictions
    im = Image.fromarray(im_array[..., ::-1])  # RGB PIL image
    im.show()  # show image
    im.save('results.jpg')  # save image
```

The `plot()` method supports the following arguments:

| Argument     | Type            | Description                                                                    | Default       |
|--------------|-----------------|--------------------------------------------------------------------------------|---------------|
| `conf`       | `bool`          | Whether to plot the detection confidence score.                                | `True`        |
| `line_width` | `float`         | The line width of the bounding boxes. If None, it is scaled to the image size. | `None`        |
| `font_size`  | `float`         | The font size of the text. If None, it is scaled to the image size.            | `None`        |
| `font`       | `str`           | The font to use for the text.                                                  | `'Arial.ttf'` |
| `pil`        | `bool`          | Whether to return the image as a PIL Image.                                    | `False`       |
| `img`        | `numpy.ndarray` | Plot to another image. if not, plot to original image.                         | `None`        |
| `im_gpu`     | `torch.Tensor`  | Normalized image in gpu with shape (1, 3, 640, 640), for faster mask plotting. | `None`        |
| `kpt_radius` | `int`           | Radius of the drawn keypoints. Default is 5.                                   | `5`           |
| `kpt_line`   | `bool`          | Whether to draw lines connecting keypoints.                                    | `True`        |
| `labels`     | `bool`          | Whether to plot the label of bounding boxes.                                   | `True`        |
| `boxes`      | `bool`          | Whether to plot the bounding boxes.                                            | `True`        |
| `masks`      | `bool`          | Whether to plot the masks.                                                     | `True`        |
| `probs`      | `bool`          | Whether to plot classification probability                                     | `True`        |

Thread-Safe Inference

Ensuring thread safety during inference is crucial when you are running multiple YOLO models in parallel across different threads. Thread-safe inference guarantees that each thread's predictions are isolated and do not interfere with one another, avoiding race conditions and ensuring consistent and reliable outputs.

When using YOLO models in a multi-threaded application, it's important to instantiate separate model objects for each thread or employ thread-local storage to prevent conflicts:

!!! Example "Thread-Safe Inference"

Instantiate a single model inside each thread for thread-safe inference:
```python
from ultralytics import YOLO
from threading import Thread

def thread_safe_predict(image_path):
    # Instantiate a new model inside the thread
    local_model = YOLO("yolov8n.pt")
    results = local_model.predict(image_path)
    # Process results


# Starting threads that each have their own model instance
Thread(target=thread_safe_predict, args=("image1.jpg",)).start()
Thread(target=thread_safe_predict, args=("image2.jpg",)).start()
```

For an in-depth look at thread-safe inference with YOLO models and step-by-step instructions, please refer to our YOLO Thread-Safe Inference Guide. This guide will provide you with all the necessary information to avoid common pitfalls and ensure that your multi-threaded inference runs smoothly.

Streaming Source `for`-loop

Here's a Python script using OpenCV (cv2) and YOLOv8 to run inference on video frames. This script assumes you have already installed the necessary packages (opencv-python and ultralytics).

!!! Example "Streaming for-loop"

```python
import cv2
from ultralytics import YOLO

# Load the YOLOv8 model
model = YOLO('yolov8n.pt')

# Open the video file
video_path = "path/to/your/video/file.mp4"
cap = cv2.VideoCapture(video_path)

# Loop through the video frames
while cap.isOpened():
    # Read a frame from the video
    success, frame = cap.read()

    if success:
        # Run YOLOv8 inference on the frame
        results = model(frame)

        # Visualize the results on the frame
        annotated_frame = results[0].plot()

        # Display the annotated frame
        cv2.imshow("YOLOv8 Inference", annotated_frame)

        # Break the loop if 'q' is pressed
        if cv2.waitKey(1) & 0xFF == ord("q"):
            break
    else:
        # Break the loop if the end of the video is reached
        break

# Release the video capture object and close the display window
cap.release()
cv2.destroyAllWindows()
```

This script will run predictions on each frame of the video, visualize the results, and display them in a window. The loop can be exited by pressing 'q'.

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