Model Prediction with Ultralytics YOLO

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.

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:

Predict

Return a list with stream=FalseReturn a generator with stream=True

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

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

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, 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:

Prediction sources

imagescreenshotURLPILOpenCVnumpytorchCSVvideodirectoryglobYouTubeStreams

Run inference on an image file.

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

Run inference on the current screen content as a screenshot.

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

Run inference on an image or video hosted remotely via URL.

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

Run inference on an image opened with Python Imaging Library (PIL).

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

Run inference on an image read with OpenCV.

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

Run inference on an image represented as a numpy array.

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

Run inference on an image represented as a PyTorch tensor.

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

Run inference on a collection of images, URLs, videos and directories listed in a CSV file.

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

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

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

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/**/*.

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

Run inference on all images and videos that match a glob expression with * characters.

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

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.

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

Run inference on remote streaming sources using RTSP, RTMP, 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.

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 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

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)

All supported 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
`show`	`bool`	`False`	show results if possible
`save`	`bool`	`False`	save images with results
`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
`hide_labels`	`bool`	`False`	hide labels
`hide_conf`	`bool`	`False`	hide confidence scores
`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)
`line_width`	`None or int`	`None`	The line width of the bounding boxes. If None, it is scaled to the image size.
`visualize`	`bool`	`False`	visualize model features
`augment`	`bool`	`False`	apply image augmentation to prediction sources
`agnostic_nms`	`bool`	`False`	class-agnostic NMS
`retina_masks`	`bool`	`False`	use high-resolution segmentation masks
`classes`	`None or list`	`None`	filter results by class, i.e. classes=0, or classes=[0,2,3]
`boxes`	`bool`	`True`	Show boxes in segmentation predictions

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:

Results

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.
`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.

Boxes

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.

Masks

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.

Keypoints

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.

Probs

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.

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.

Plotting

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`

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).

Streaming for-loop

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'.

Created 2023-03-12, Updated 2023-09-13
Authors: glenn-jocher (27), Laughing-q (3), sergiuwaxmann (1)

Model Prediction with Ultralytics YOLO

Introduction

Why Use Ultralytics YOLO for Inference?

Key Features of Predict Mode

Inference Sources

Inference Arguments

Image and Video Formats

Images

Videos

Working with Results

Boxes

Masks

Keypoints

Probs

Plotting Results

Streaming Source for-loop

Comments

Streaming Source `for`-loop