Abstract: This paper presents a controlled comparative evaluation of a custom convolutional neural network and several pre-trained deep learning architectures for image classification. The objective ...

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 ...

Over the last few years, with the fast progress of remote sensing technology, the availability of high-resolution satellite images has greatly increased 1. This has led to a growing research interest ...

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 ...

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 ...

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 ...

Want to see how TensorFlow and PyTorch compare for a real-world task? Let’s implement a CIFAR-10 image classification model in both frameworks! Here’s a side-by-side comparison to help you transition ...