Achieve Accurate Object Detection with YOLO-NAS

Table of Contents

1. Introduction
2. What is YOLO Nas?
3. YOLO V8 vs YOLO Nas
4. Training YOLO Nas
5. Working with Custom Data Set
6. Image Recognition with YOLO Nas
7. Video Analysis with YOLO Nas
8. Fine-tuning YOLO Nas Model
9. Evaluating the Model
10. Conclusion

Introduction

Welcome to my Channel! In today's video, I will be discussing the new object detection algorithm called YOLO Nas (You Only Look Once Neural Architecture Search). Developed by Desi Ai, this model has surpassed the performance of all previous object detection algorithms such as YOLO V8, YOLO V7, YOLO V6, and YOLO V5. YOLO Nas is trained on popular datasets like Coco dataset, Objects 365, and the Roboflow 100. In this Tutorial, I will show you how to use the pre-trained YOLO Nas model to make predictions and also how to train the model on a custom dataset to detect your own objects.

What is YOLO Nas?

YOLO Nas is an advanced object detection algorithm that utilizes neural architecture search to achieve state-of-the-art performance. Unlike traditional object detection methods that require multiple passes over an image, YOLO Nas only requires a single pass, making it exceptionally fast and efficient. This algorithm is trained on popular datasets like Coco dataset, Objects 365, and the Roboflow 100, enabling it to detect a wide range of objects accurately.

YOLO V8 vs YOLO Nas

When comparing YOLO V8 to YOLO Nas, it becomes evident that YOLO Nas outperforms its predecessor in terms of accuracy and speed. While YOLO V8 is a highly accurate object detection algorithm, YOLO Nas takes it a step further by utilizing neural architecture search to optimize the model's architecture specifically for object detection. This allows YOLO Nas to achieve superior performance while maintaining fast inference times.

Training YOLO Nas

To train the YOLO Nas model on a custom dataset, we recommend using the Super Gradient Package, which is a PyTorch-based training library specifically designed for object detection tasks. It provides essential functionalities for training, testing, and running the YOLO Nas model. Before training, it is crucial to create a separate environment to avoid compatibility issues with different Python projects. Additionally, installing packages like Super Gradient, iou_metrics, and roboflow (if using the Roboflow dataset) is necessary.

Working with Custom Data Set

To work with a custom dataset, ensure it is formatted in YOLO V5, V7, or V8 format. These formats consist of the "train," "test," and "validation" folders, each containing an "images" folder and a "labels" folder. The "images" folder should contain the training images, while the corresponding annotation files should be stored in the "labels" folder. It is essential to provide the correct paths for the data directory, images directory, labels directory, and class names when loading the dataset. Super Gradient supports various data loaders, making it easy to load custom datasets.

Image Recognition with YOLO Nas

Using the pre-trained YOLO Nas model for image recognition is straightforward. After activating the environment and installing the necessary packages, load the model and specify the confidence score for predictions. By providing the URL of an image, predictions can be made using the "predict" method. Adjusting the threshold value allows for different confidence scores. The model effectively detects objects in images using bounding boxes, providing accurate results.

Video Analysis with YOLO Nas

YOLO Nas can also be used for video analysis. Specify the input video path and output video path to apply object detection to a video. The pre-trained YOLO Nas model will process the video frames and create an output video with bounding boxes around the detected objects. Whether working with a locally stored video or live video from a webcam, the YOLO Nas model can accurately identify and track objects in real-time.

Fine-tuning YOLO Nas Model

Fine-tuning the YOLO Nas model is essential to achieve optimal performance with a custom dataset. Utilize the Super Gradient trainer to fine-tune the pre-trained model. Configure parameters such as the maximum number of epochs, optimizer, and loss function to train the model according to specific requirements. By training the model for a sufficient number of epochs and experimenting with different optimization techniques, the final model can achieve high accuracy on the custom dataset.

Evaluating the Model

After training the custom YOLO Nas model, evaluation is necessary to assess its performance. By calling the evaluation function, the model's precision, recall, and F1 scores can be calculated, providing insights into its accuracy. Comparing these metrics against a chosen threshold helps determine the adequacy of the model for object detection tasks. Evaluating the model ensures its performance meets the desired expectations and requirements.

Conclusion

In conclusion, the YOLO Nas object detection algorithm is a powerful tool for accurately detecting objects in images and videos. By leveraging neural architecture search, YOLO Nas achieves state-of-the-art performance while maintaining fast inference times. Whether working with pre-trained models or training on custom datasets, the YOLO Nas algorithm provides excellent accuracy and efficiency. With its ability to recognize a wide range of objects, YOLO Nas can be applied to various real-world applications, including surveillance systems, autonomous vehicles, and more.

Pros:

• Exceptional accuracy and speed
• Ability to work with custom datasets
• Fast and efficient object detection
• Real-time video analysis
• Compatibility with popular deep learning frameworks

Cons:

• Significant computational resources required for training
• Training time can be lengthy for large datasets
• Custom dataset preparation can be complex for beginners

Highlights

• YOLO Nas (You Only Look Once Neural Architecture Search) is a state-of-the-art object detection algorithm that achieves high accuracy and fast inference times.
• YOLO Nas outperforms previous versions such as YOLO V8, offering superior accuracy and efficiency.
• Training YOLO Nas on custom datasets is made easy with the Super Gradient package, which provides essential functionalities for training, testing, and running the model.
• The ability to perform image recognition and video analysis makes YOLO Nas suitable for a wide range of applications.
• Fine-tuning the YOLO Nas model allows for optimal performance on custom datasets, ensuring high accuracy in object detection tasks.

Frequently Asked Questions

Q: What is the advantage of using YOLO Nas over traditional object detection algorithms? A: YOLO Nas offers the advantage of fast inference times by only requiring a single pass over an image or video. This makes it highly efficient for real-time applications and resource-constrained environments.

Q: Can YOLO Nas be trained on custom datasets? A: Yes, YOLO Nas can be trained on custom datasets using the Super Gradient package. By fine-tuning the pre-trained model with the custom dataset, accurate object detection can be achieved for specific objects of interest.

Q: What types of objects can YOLO Nas detect? A: YOLO Nas is capable of detecting a wide range of objects, thanks to its training on popular datasets like Coco, Objects 365, and Roboflow 100. The model can identify objects from various categories, including common everyday objects, animals, and more.

Q: Is YOLO Nas suitable for real-time video analysis? A: Yes, YOLO Nas is well-suited for real-time video analysis. By processing video frames through the pre-trained model, accurate object detection can be performed, allowing for applications such as surveillance systems, autonomous vehicles, and more.

Q: What are the system requirements for training YOLO Nas? A: Training YOLO Nas requires significant computational resources, including a powerful GPU. It is recommended to use a dedicated environment and comply with the requirements stated in the Super Gradient package documentation.

Resources:

• Super Gradient GitHub Repo
• YOLO Nas GitHub Repo
• Coco Dataset
• Objects 365 Dataset
• Roboflow Dataset