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convolutionalfunction

Convolutional neural networks (CNN) are similar to the multi-layer perceptron network. They are ideal for computer vision applications, although it can be used for non-image applications. In contrast to a multi-layer perceptron, CNNs extract features through a simple convolutional operation.

daformer

[CVPR22] Official Implementation of DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation

deepmapping_pcr

ecg_cnn

An Optimal Deep Learning Framework for Detecting Abnormal Heart Beats Using ECG Signals

ecg_diabetes

In this study, we demonstrate how transfer learning can be applied to the ECG domain for early detection of Hypoglycemic events. Our model was trained using the ImageNet dataset, and features maps were transferred to extract patterns of small ECG excerpts. 

firstneuralnetwork

The modern era is data-driven. Data and information are being collected and stored more than we can process. Dealing with such a copious amount of data requires speed, accuracy, and utmost efficacy. The conventional method of dealing with such an abundance of information is highly inefficient and inaccurate. This issue engendered an approach that is robust, diligent, accurate, and highly reliable. Deep learning plays a crucial role in today’s fast-paced technological era. It is versatile and is being utilized in almost every field. The applications of deep learning have proliferated in the past decade. From image recognition to financial management, it has proven to be an essential tool for modern technology. It has ameliorated the quotidian life of a common man.

hortensiarecognition

mmcv

OpenMMLab Computer Vision Foundation

pratyaksh10.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

proda

Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation (CVPR 2021)

quadformer

rootonnvme

Switch the rootfs to a NVMe SSD on the Jetson Xavier NX and Jetson AGX Xavier

smart-dustbin

The amalgamation of IoT and Deep learning can bring a revolutionary change in technology and be applied to waste management. Consequently, prediction and examination of garbage levels may help municipal authorities incorporate an efficient garbage management system and reduce the overflow of garbage bins.

tpsnet

In this project, we designed a novel ResNet architecture to maximize classification accuracy on the CIFAR-10 dataset while ensuring that the total number of parameters are less than 5M. We implement a random search approach for hyperparameter optimization. Our best performing architecture, TPSNet achieved a test accuracy of 94.84%.