is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. Y
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Documentation and Github
Installation of Yolov8
Requirement
Install necessary packages such as Python, Numpy, PyTorch, CUDA and more
Python >=3.8
PyTorch>=1.8
opencv-python>=4.6.0
matplotlib>=3.3.0
1)Install Yolov8 via pip package
First, create a new environment for YOLOv8 in Anaconda Prompt.
e.g. $myENV$ = yolov8
You can also make an exact copy of the existing environment by creating a clone
If you already have an environment named py39, clone it as yolov8
conda create --name yolov8 --clone py39
Activate the environment and Install YOLOv8 with pip to get stable packages.
The result will be saved in the project folder \runs\segment\predict\
Using YOLOv8 with Python : Example Codes
In the project folder, create a new python code file
Project Folder: \source\repos\DLIP\yolov8\
Activate yolov8 environment in Anaconda Prompt
A list of useful commands for YOLOv8
from ultralytics import YOLO
# Create a new YOLO model from scratch
model = YOLO('yolov8n.yaml')
# Load a pretrained YOLO model (recommended for training)
model = YOLO('yolov8n.pt')
# Train the model using the 'coco8.yaml' dataset for 3 epochs
results = model.train(data='coco8.yaml', epochs=3)
# Evaluate the model's performance on the validation set
results = model.val()
# Perform object detection on an image using the model
results = model('https://ultralytics.com/images/bus.jpg')
Example: Detection Inference
Download the dataset file and save in the project folder
You can download the COCO pretrained models such as YOLOv8n and more.
Inference one image
Create a new python source file in the project folder
Yolo8-Inference-Ex1.py
#########################################################
# YOLO v8 Tutorial : Prediction Ex1
#
# Load Pretrained Model and Display the Annoted Results
#
#########################################################
from ultralytics import YOLO
import cv2
from matplotlib import pyplot as plt
# Load a pretrained YOLO model (recommended for training)
model = YOLO('yolov8n.pt')
# Inference Source - a single source
src = cv2.imread("bus.jpg")
# Perform object detection on an image using the model
result = model.predict(source=src, save=True, save_txt=True) # save predictions as labels
# View result
for r in result:
# print the Boxes object containing the detection bounding boxes
print(r.boxes)
# Show results to screen (not recommended)
print("result.show()")
r.show()
# Plot results image
print("result.plot()")
dst = r.plot() # return BGR-order numpy array
cv2.imshow("result plot",dst)
# Plot the original image (NParray)
print("result.orig_img")
cv2.imshow("result orig",r.orig_img)
# Save results to disk
r.save(filename='result.jpg')
cv2.waitKey(0)
##########################################################################################
Inference of multiple images
Create a new python source file in the project folder
Yolo8-Inference-Ex2.py
For multiple input source images, you can copy bus.jpg as bus2.jpg.
#########################################################
# YOLO v8 Tutorial : Prediction Ex2
#
# Load Pretrained Model and Display the Annoted Results (multiple images)
#
#########################################################
from ultralytics import YOLO
import cv2
from matplotlib import pyplot as plt
# Load a pretrained YOLO model (recommended for training)
model = YOLO('yolov8n.pt')
# Inference Source - multiple images
# Perform object detection on images using the model
results = model(['bus.jpg', 'bus2.jpg']) # return a list of Results objects
# View results
for i, r in enumerate(results):
# Plot results image
dst = r.plot() # return BGR-order numpy array
cv2.imshow("r.plot",dst)
# Save results to disk
r.save(filename=f'results{i}.jpg')
cv2.waitKey(0)
Inference on Webcam stream
Create a new python source file in the project folder
Yolo8-Inference-Webcam-Ex3.py
#########################################################
# YOLO v8 Tutorial : Prediction Ex3
#
# Stream Video Prediction
#
# 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'.
#########################################################
import cv2 as cv
from ultralytics import YOLO
# Load the YOLOv8 model
model = YOLO('yolov8n.pt')
# Open the video camera no.0
cap = cv.VideoCapture(0)
# If not success, exit the program
if not cap.isOpened():
print('Cannot open camera')
# 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
cv.imshow("YOLOv8 Inference", annotated_frame)
# Break the loop if 'q' is pressed
if cv.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()
cv.destroyAllWindows()
Example: Train
See the for full documentation on training, validation, prediction and deployment.
Also, you can visit the github repository:
Ultralytics provides various installation methods including pip, conda, and Docker. Install YOLOv8 via the ultralytics pip package for the latest stable release or by cloning the for the most up-to-date version.
