Project Template: Real-Time Object Detection Using Pytorch With Pre-trained YOLOv8

Project Description:

This template aims to explore the capabilities of the pre-trained YOLOv8 model in real-time object detection scenarios. Participants will implement and optimize the YOLOv8 model to detect and classify various objects in real-time video streams, utilizing the model's pre-trained weights to achieve high accuracy and speed.

Tools and Technologies:

Primary Framework: PyTorch
Model: YOLOv8
Additional Libraries: OpenCV, Ultralytics YOLO library, etc.

Setup and Installation:

Install PyTorch, OpenCV, and other necessary libraries.
Download a pre-trained YOLOv8 model or use the provided yolov8n.pt file.

Dataset (Optional):

Use publicly available datasets to training models.
Data preparation is crucial for achieving specific accurate detection.

Example of Model Implementation:

Load the pre-trained YOLOv8 model using the Ultralytics YOLO library.
Utilize the model to perform real-time object detection on live video streams.

#Example File:
import cv2
from ultralytics import YOLO

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

# Open the video file
video_path = "http://admin:admin@192.168.137.188:8081/live.flv"
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
        for result in model(frame, stream=True):
            annotated_frame = result.plot()
            cv2.imshow("YOLOv8 Inference", annotated_frame)
            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()

Model Evaluation and Optimization:

Evaluate the model performance in real-time object detection scenarios.
Optimize the model for better accuracy and speed, if necessary.

Deployment:

Deploy the model for real-time object detection on a web server or a local machine.
Create a user-friendly interface for interacting with the model.

Performance Analysis:

Analyze the performance of the model in different real-time object detection scenarios.

Documentation:

Provide comprehensive documentation covering the setup, implementation, evaluation, and deployment steps.
Include troubleshooting steps and references for further reading.

Template Author:

Name: Jiaying Wang Email: wangj63@rpi.edu

References:

https://github.com/ultralytics/ultralytics

https://docs.ultralytics.com/modes/predict/#inference-sources

hack-rpi/HackRPI-PyTorch-Template