YOLOv8: How to Train for Object Detection on a Custom Dataset

Summary

This video tutorial introduces the latest release of the YOLO V8 algorithm, a significant advancement in real-time object detection. The host demonstrates how to train the model on a custom dataset using roboflow and highlights the new CLI and SDK interfaces for easier deployment and inference. Viewers are guided through the training process, dataset creation with roboflow, and deploying the model for API inference, showcasing the model's performance on images and videos with impressive speed and accuracy.

Takeaways

• 🚀 YOLOv8 has been released, and it's claimed to be the new state-of-the-art for real-time object detection.
• ⚖️ Internal tests show that YOLOv8 fine-tunes much faster than its predecessors, YOLOv5 and YOLOv7, using the Roboflow 100 dataset.
• 📊 The video demonstrates how to train, validate, predict, and deploy a YOLOv8 object detection model on a custom dataset.
• 🛠️ YOLOv8 introduces significant engineering changes, including a shift from using Python scripts to CLI tools and SDKs for better model management.
• 📈 The video highlights that YOLOv8 supports the same data format as YOLOv5, allowing for easy retraining of models using existing datasets.
• 💻 The video provides a walkthrough on creating and annotating a dataset using Roboflow, specifically for football player detection.
• 📦 YOLOv8 is the first iteration to have an official pip package, simplifying the installation process.
• 🔄 The video compares the new CLI and SDK APIs, showing how to perform predictions using both methods.
• 🔍 The video demonstrates training a YOLOv8 model, including monitoring key metrics like box loss and class loss to assess model performance.
• 🌐 The video ends with a guide on deploying the trained YOLOv8 model using Roboflow's API for inference, showcasing its practical application.

Q & A

What is the latest version of YOLO discussed in the video?

The latest version discussed in the video is YOLO V8.

What does the team behind YOLO V8 claim about its performance?

The team behind YOLO V8 claims that it has achieved state-of-the-art performance in object detection in real-time.

What is the Roboflow 100 dataset used for in the video?

The Roboflow 100 dataset is used to measure the performance of YOLO V8 against its predecessors, YOLO V5 and YOLO V7.

What improvements were observed in YOLO V8 compared to its previous versions?

YOLO V8 was observed to fine-tune much faster than its predecessors, YOLO V5 and YOLO V7.

How can one stay updated with the latest videos on computer vision?

To stay updated with the latest videos on computer vision, one can like and subscribe to the channel, ensuring they are notified when new videos are released.

What is the focus of the video regarding the training process?

The video focuses on the process of training the YOLO V8 model on a custom dataset, specifically for object detection.

What changes were introduced in the code base of YOLO V8?

YOLO V8 introduced the biggest engineering jump since migrating from Darknet to PyTorch, including the use of CLIs and SDKs, and the removal of the need to fork the repository for trackers.

Who is the team behind the creation of YOLO V8?

YOLO V8 is created by the Ultralytics team, the company behind YOLO V3 and YOLO V5.

What are the two new ways to interact with the YOLO V8 code base?

The two new ways to interact with the YOLO V8 code base are through the Command Line Interface (CLI) and the Software Development Kit (SDK).

How can one create a custom dataset for training YOLO V8 using Roboflow?

One can create a custom dataset for training YOLO V8 using Roboflow by creating a new project, selecting object detection, naming the project, and then uploading images and annotations. Roboflow assists with the annotation process and allows for dataset generation with applied transformations and augmentations.

What is the significance of the confusion matrix in evaluating the YOLO V8 model's performance?

The confusion matrix is significant in evaluating the YOLO V8 model's performance as it shows how the model handles different classes, indicating correct detections and misclassifications.

How can the trained weights of the YOLO V8 model be used for validation?

The trained weights of the YOLO V8 model can be used for validation by using the CLI with the 'val' mode instead of 'detect' or 'train', which utilizes a separate test dataset to provide true mAP metrics.

What is the inference speed of YOLO V8 on a single frame as demonstrated in the video?

The inference speed of YOLO V8 on a single frame is between 12 to 13 milliseconds, which is approximately 80 FPS.

How can the trained YOLO V8 model be deployed for inference over an API?

The trained YOLO V8 model can be deployed for inference over an API by using a single line of code to upload the model to the server. Once uploaded, the model can be used for inference through the hosted API.