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 ...

Hyperspectral image (HSI) classification faces challenges in diverse scenarios due to spectral-spatial complexity and class imbalance. Existing methods lack generalizability. This paper presents a ...

In the AI field, new models are being constantly released and every other week, a new AI image model comes out on top. So in this article, we have compiled a list of the best AI image generators which ...

Welcome to the Zero to Mastery Learn PyTorch for Deep Learning course, the second best place to learn PyTorch on the internet (the first being the PyTorch documentation). 00 - PyTorch Fundamentals ...

Rock image classification plays a crucial role in geological exploration, mineral resource development, and environmental monitoring. However, rock images often exhibit high intra-class similarity and ...

Much like how Tesla’s autopilot identifies objects on the road, our model uses transfer learning to teach the computer how to see, understand, and classify, pushing the boundaries of image recognition ...

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 ...

In This project I will show you how to implement a basic Text Classification project in order for you to familiarize yourself with the syntax and begin your journey into on of the most powerful and in ...

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 ...