Are you familiar with the term Neural Architecture Search? It is a technique used to design better backbones for object detection using artificial intelligence. One such algorithm that is used for this purpose is called DetNAS. In this article, we will discuss the key features of DetNAS and how it helps in designing better backbones for object detection.
What is DetNAS?
DetNAS is a neural architecture search algorithm that is used to improve the backbones of object detection algorithms. This algorithm is based on a technique called one-shot supernet, which contains all the possible networks in the search space. The supernet is trained using the detector training schedule: ImageNet pre-training and detection fine-tuning. After training the supernet, the architecture search is performed on the trained supernet using the detection task as guidance. DetNAS uses evolutionary search rather than RL-based methods or gradient-based methods.
How does DetNAS work?
DetNAS works by generating a supernet which is an ensemble of neural networks that share common weights. The neural networks that make up the supernet represent all possible architectures within a defined search space. Once the supernet is generated, it is trained using the typical detector training schedule. This process involves pre-training the neural network on the ImageNet dataset and fine-tuning it for detection. Once the supernet is trained, it is used to search for the best architecture for object detection.
Evolutionary search techniques are used to select the best architecture from the supernet. The search algorithm starts by randomly selecting a neural network architecture and evaluating its detection performance. Based on the performance, the algorithm either discards the architecture or adds it to a pool of architectures. The algorithm then performs mutations or crossovers on the surviving architectures to generate new architectures. This process is continued until a satisfactory architecture is achieved.
The benefits of DetNAS
DetNAS has several benefits when it comes to designing better backbones for object detection. Firstly, DetNAS generates the best architecture for object detection more efficiently than other methods. It is faster because it trains only one supernet representing all the possible architectures. This makes the search process more efficient as compared to other techniques that require the training of multiple neural networks.
Secondly, DetNAS improves the overall detection performance by generating architectures that are specifically designed for the detection task. Unlike manually designed architectures, the neural network architectures generated by DetNAS are optimized for detection tasks. This ensures that they provide optimal performance on the detection task and require less manual tuning.
Thirdly, DetNAS is more flexible because it enables the user to specify constraints for different aspects of the neural network architecture, such as the number of channels, the size of the kernel, the level of depth, and the layer multiplier. This flexibility ensures that the neural network architecture generated by DetNAS aligns with the user's specific requirements.
Conclusion
DetNAS is an evolutionary search-based algorithm used in the designing of better backbones for object detection. This algorithm generates a supernet containing all possible neural network architectures within a defined search space, trains them using the detector training schedule, and searches for the best architecture for object detection. DetNAS delivers various benefits to the user, including efficiency in generating the best architecture, optimized detection performance, and flexibility in specifying user requirements. By leveraging the benefits of DetNAS, researchers and developers can improve the backbone of object detection algorithms, leading to better and more accurate results.
