UltralyticsYOLOv8 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
See the YOLOv8 Docs for full documentation on training, validation, prediction and deployment.
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 Ultralytics GitHub repository for the most up-to-date version.
Requirement
Install necessary packages such as Python, Numpy, PyTorch, CUDA and more
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')
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()
