Following papers are implemented using PyTorch. ResNeXt-29 8x64d 3.97 (1 run) 3.65 (average of 10 runs) 42h50m* ResNeXt-29 16x64d 3.58 (average of 10 runs) shake-shake-26 2x32d (S-S-I) 3.68 3.55 ...
Abstract: Managing solid waste efficiently has become an increasingly pressing concern as cities grow. Waste production intensifies this study and presents a deep learning-based waste classification ...
Reverse image searching is a quick and easy way to trace the origin of an image, identify objects or landmarks, find higher-resolution alternatives or check if a photo has been altered or used ...
This project provides a concise PyTorch training example using the CIFAR-100 image classification task, designed to help beginners quickly get started. It also offers some flexible adjustment and ...
A new AI model, H-CAST, groups fine details into object-level concepts as attention moves from lower to high layers, outputting a classification tree—such as bird, eagle, bald eagle—rather than ...
Abstract: With an emphasis on convolutional neural networks (CNNs), this research does a thorough analysis of the effectiveness and suitability of the TensorFlow and PyTorch frameworks for image ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results