The Inception architecture processes feature maps through parallel convolutional branches with varying receptive fields. In PyTorch, an InceptionModule can be constructed by defining four distinct pathways: a standalone 1×1 convolution, a 5×5 convolution preceded by a bottleneck layer, a cascade of...
Problem 1: Oracle Bone Script Image Preprocessing and Feature Extraction The initial challenge involves developing a robust preprocessing pipeline for ancient oracle bone script rubbings. These historical artifacts contain significant degradation including speckle noise, artificial textures, and int...
Preparing the MNIST Dataset The MNIST dataset consists of 28×28 grayscale images of handwritten digits, split into 60,000 training samples and 10,000 test samples. We use torchvision to download and transform the data. import torch from torch.utils.data import DataLoader from torchvision import data...
The task of distinguishing cats from dogs originates from a beginner-level Kaggle competition titled Dogs vs Cats. To gain deeper insights into Convolutional Neural Networks (CNNs), several classic models like LeNet, AlexNet, and ResNet were implemented using PyTorch. This exploration investigates h...