Train and Deploy YOLOv8 Model for Real-time Object Detection

Table of Contents

1. Introduction
2. Custom Update Detection with YOLO V8 Model
3. Training the Custom YOLO V8 Model
   • 3.1 Using Roboflow to Label the Dataset
   • 3.2 Training the Model in Google Colab
4. Exporting and Importing the Trained Model
5. Running Real-time Inference with the Custom Model
   • 5.1 Setting up the GPU Runtime in Google Colab
   • 5.2 Installing the YOLO V8 Model
   • 5.3 Importing the Model and Performing Inference
6. Validating and Evaluating the Model
   • 6.1 Model Summary and Metrics
   • 6.2 Confusion Matrix Analysis
7. Running Predictions on New Images
8. Running Live Inference Using Webcam
   • 8.1 Exporting and Importing the Model for Live Inference
   • 8.2 Performing Live Inference on Objects

Introduction

In this video, we will explore the process of custom update detection using the YOLO V8 model. The YOLO V8 model is a recently released model that enables us to train our own custom object detection models. We will use the Roboflow platform to label our dataset and then train the YOLO V8 model using Google Colab. We will also learn how to export and import the trained model, perform real-time inference, and run predictions on new images. Additionally, we will demonstrate how to use a webcam for live inference on objects. So let's dive in and learn how to train and utilize our own custom YOLO V8 model!

Custom Update Detection with YOLO V8 Model

To perform custom update detection, we will use the YOLO V8 model. YOLO V8 is a state-of-the-art object detection model that can be trained on a custom dataset. In this video, we will focus on detecting different objects in images using the YOLO V8 model. We will specifically detect cups or mugs in our images, but the model can be trained to detect any object of interest.

Training the Custom YOLO V8 Model

3.1 Using Roboflow to Label the Dataset

To train our custom YOLO V8 model, we need a labeled dataset with bounding boxes around the objects we want to detect. We will use the Roboflow platform to label our dataset. Roboflow provides an easy-to-use interface for annotating images and generating the necessary annotations for training an object detection model.

3.2 Training the Model in Google Colab

Once we have our labeled dataset, we will train the YOLO V8 model using Google Colab. Google Colab is a free cloud-based development environment that provides GPU resources, which greatly speeds up the training process. We will install the necessary libraries, import the YOLO V8 model, and train it using our labeled dataset. The training process involves optimizing the model's parameters and adjusting the confidence score threshold to filter out low-confidence predictions.

Exporting and Importing the Trained Model

After training the custom YOLO V8 model, we need to export it and load it into our own Python script. This allows us to use the model for real-time inference in our own projects and applications. We will export the model from Google Colab and specify the path to the exported model in our Python script. By doing this, we can easily run the model and obtain predictions on new images.

Running Real-time Inference with the Custom Model

To perform real-time inference with our custom trained YOLO V8 model, we need to import the model into our Python script and set it to prediction mode. We can then use the model to detect objects in images or even through a webcam. By specifying the source of the images or webcam feed, we can Visualize the predictions made by the model. The YOLO V8 model is fast, allowing us to achieve real-time object detection even on resource-constrained devices.

Validating and Evaluating the Model

To ensure the effectiveness and accuracy of our custom YOLO V8 model, we need to validate and evaluate its performance. We will analyze the model's metrics, such as box loss, class loss, and instance count, for each epoch during training. Additionally, we will create a confusion matrix to assess the model's performance on different class labels.

Running Predictions on New Images

Once we have trained and validated our custom YOLO V8 model, we can use it to make predictions on new images. By providing the path to the model and the location of the test images, we can run the model in prediction mode and obtain the predicted bounding boxes and class labels for objects in the images. This allows us to deploy the model in various applications, such as object recognition tasks.

Running Live Inference Using Webcam

To demonstrate the real-time capabilities of our custom YOLO V8 model, we can perform live inference using a webcam. By importing the model into our Python script and setting it to prediction mode, we can utilize the webcam as the source for detecting objects in real-time. This opens up possibilities for applications such as surveillance systems, interactive experiences, and more.

Article

Custom Update Detection with YOLO V8 Model

Object detection plays a crucial role in computer vision applications. The YOLO V8 model is a powerful tool that allows us to train our own custom object detection models. By utilizing the YOLO V8 architecture, we can detect various objects in images with high accuracy and speed. In this article, we will explore the process of custom update detection using the YOLO V8 model and learn how to train, export, and utilize the model for real-time inference.

The first step in custom update detection is labeling our dataset with bounding boxes around the objects we want to detect. This can be achieved using the Roboflow platform, which provides an intuitive interface for annotating images. Once our dataset is labeled, we can proceed to train the YOLO V8 model.

Training the YOLO V8 model requires a powerful computing environment, and Google Colab provides the perfect solution. With its GPU resources, Google Colab accelerates the training process and allows us to optimize the model's parameters. By adjusting the confidence score threshold, we can filter out low-confidence predictions and improve the accuracy of our model.

After training the custom YOLO V8 model, we need to export it and import it into our Python script for real-time inference. This allows us to utilize the model in our own projects and applications. By specifying the path to the exported model, we can easily run the model and obtain predictions on new images. We can also run the model on a webcam feed, enabling real-time object detection.

To ensure the reliability of our custom model, we need to validate and evaluate its performance. This involves analyzing metrics such as box loss, class loss, and instance count for each epoch during training. Additionally, we can create a confusion matrix to assess the model's performance on different object labels.

With our custom YOLO V8 model validated, we can confidently run predictions on new images. By providing the path to the model and the location of the test images, we can obtain accurate bounding box predictions and class labels. This versatility allows us to deploy the model in various applications and tasks.

To showcase the real-time capabilities of our custom YOLO V8 model, we can perform live inference using a webcam. By importing the model into our Python script and setting it to prediction mode, we can leverage the webcam as a source for real-time object detection. This opens up possibilities for applications such as surveillance systems, interactive experiences, and more.

Overall, the YOLO V8 model provides a powerful and efficient solution for custom update detection. By following the steps outlined in this article, we can train our own custom models, export them, and utilize them for real-time inference. With the ability to detect objects accurately and in real-time, the YOLO V8 model enables a wide range of computer vision applications. So why not give it a try and unleash the potential of your own custom object detection models?

Highlights

• Custom update detection with the YOLO V8 model allows for accurate and real-time object detection.
• The Roboflow platform simplifies the process of labeling datasets for training the YOLO V8 model.
• Google Colab provides the necessary computing resources to train the YOLO V8 model efficiently.
• Exporting and importing the trained model enables its utilization in various Python projects and applications.
• Validation and evaluation of the model's performance ensure its reliability and accuracy.
• Running predictions on new images and performing live inference with a webcam showcase the versatility of the YOLO V8 model.

FAQ

1. How can I label my dataset for training the YOLO V8 model?

   • You can use the Roboflow platform, which offers an intuitive interface for annotating images with bounding boxes.

2. Can the YOLO V8 model be trained to detect objects other than cups or mugs?

   • Yes, the YOLO V8 model can be trained to detect any object of interest by providing a custom dataset with appropriate annotations.

3. Is a GPU required for training the YOLO V8 model?

   • While a GPU can greatly accelerate the training process, it is not mandatory. Training can be performed on CPUs or less powerful GPUs as well.

4. Can the YOLO V8 model be used for real-time inference on resource-constrained devices?

   • Yes, the YOLO V8 model is fast and can achieve real-time object detection even on devices with limited computational resources.

5. How can I evaluate the performance of my custom YOLO V8 model?

   • You can analyze metrics such as box loss, class loss, and instance count for each epoch during training. Additionally, creating a confusion matrix can provide insights into the model's performance on different classes.

6. What applications can benefit from running live inference using a webcam?

   • Live inference with a webcam can be employed in various applications, such as surveillance systems, interactive experiences, and real-time object tracking.

Resources:

• Roboflow
• YOLO V8 Model
• Google Colab