Resnet18 Model With Sequential Layer For Computing Accuracy On Image Classification Dataset
This residual network has been a broad domain of research in deep learning. Many complex architectures are based upon residual networks. Residual networks are efficient due to skip connections. This paper highlights the addition of a sequential layer to the traditional RESNET 18 model for computing the accuracy of an Image classification dataset. The classification datasets such as Intel Scene dataset, CIFAR10 dataset, etc. These datasets consist of images belonging to various classes. In classification, we assign the pictures to their respective categories. The addition of a sequential layer gives the accuracy in the range of 0 to 1, which helps find the accuracy of prediction for the test set.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationData AugmentationData VisualizationDetecting Image ManipulationImage Augmentationimage-classificationImage ClassificationImage CroppingImage DenoisingImage GenerationImage ManipulationImage Manipulation DetectionImage MattingImage Morphingimage smoothingImage StitchingImage-VariationROI-based image generationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A Hybrid Quantum-Classical Approach based on the Hadamard Transform for the Convolutional Layer
In this paper, we propose a novel Hadamard Transform (HT)-based neural network layer for hybrid quantum-classical computing. It implements the regular convolutional layers in the Hadamard transform domain. The idea is ba…
HTMA-Net: Towards Multiplication-Avoiding Neural Networks via Hadamard Transform and In-Memory Computing
Reducing the cost of multiplications is critical for efficient deep neural network deployment, especially in energy-constrained edge devices. In this work, we introduce HTMA-Net, a novel framework that integrates the Had…
ShaResNet: reducing residual network parameter number by sharing weights
Deep Residual Networks have reached the state of the art in many image processing tasks such image classification. However, the cost for a gain in accuracy in terms of depth and memory is prohibitive as it requires a hig…
image-classificationImage ClassificationSequentially Aggregated Convolutional Networks
Modern deep networks generally implement a certain form of shortcut connections to alleviate optimization difficulties. However, we observe that such network topology alters the nature of deep networks. In many ways, the…
General Classificationimage-classificationImage ClassificationResNet-like Architecture with Low Hardware Requirements
One of the most computationally intensive parts in modern recognition systems is an inference of deep neural networks that are used for image classification, segmentation, enhancement, and recognition. The growing popula…
Edge-computingGeneral Classificationimage-classificationImage Classification