Freaks City

Steps Below we introduce fine‑tuning, a common technique in transfer learning. As illustrated in the following diagram, fine‑tuning consists of four steps. Pre‑train a neural network model (the source model) on a source dataset, e.g., ImageNet. Create a new neural network (the target model). It replicates all model design and parameters from t ...

1. Dataset Acquisition Concrete surface cracks are a primary defect in civil structures. Building inspection is performed to evaluate stiffness and tensile strangth. Crack detection plays a vital role in building health assessment. The dataset contains images of various concrete surfaces with and without cracks. The image data is divided into t ...

Image classification, a fundamental computer vision task, falls under supervised learning. Given an image, the goal is to predict its category. This article demonstrates how too use a ResNet50 network to classify the CIFAR-10 dataset using the MindSpore framework. ResNet Architecture ResNet50, introduced by Kaiming He et al. in 2015, won the IL ...

Detecting manipulated media generated by deeppfake algorithms is a pressing challenge. This article presents an end-to-end pipeline for training a binary image classifier that distinguishes real faces from synthetically generaetd ones using EfficientNet, PyTorch, and the timm library. Task Overview and Data Format The objective is to assign a p ...

01 Cat vs Dog Recognition Introduction: Manually building LeNet for cat vs dog recognition. Reference: https://mtyjkh.blog.csdn.net/article/details/121263237 Code: 01-cat-dog (github.com) Note: Beginners are advised to practice typing all the code, as it serves as a template. Regardless, you should be able to type it fluently (know the steps, t ...