image classification

Python Programming Learning Circle

Dec 19, 2024 · Artificial Intelligence

Overview of Microsoft’s Open‑Source Computer Vision Recipes Library

The article introduces Microsoft’s open‑source Computer Vision Recipes library, describing its purpose, target audience, repository links, supported vision scenarios such as image classification, similarity, detection, key‑point, segmentation, action recognition, multi‑object tracking and crowd counting, and provides guidance on using PyTorch, Azure and GPU resources.

AzureOpen-sourcePyTorch

0 likes · 7 min read

Overview of Microsoft’s Open‑Source Computer Vision Recipes Library

Rare Earth Juejin Tech Community

May 17, 2024 · Artificial Intelligence

Building a Low‑Cost AI Image Classification Platform for Edge Devices

This article describes how to create a cheap AI image‑classification system that trains a TensorFlow model on a desktop, converts it to TFLite, and runs it on Android phones and Raspberry Pi devices, detailing data preparation, training, deployment, and hardware considerations.

AIAndroidEdge Computing

0 likes · 9 min read

Building a Low‑Cost AI Image Classification Platform for Edge Devices

Rare Earth Juejin Tech Community

Apr 22, 2024 · Artificial Intelligence

PP-LCNet: A Lightweight CPU-Optimized Convolutional Neural Network

PP-LCNet is a lightweight convolutional neural network designed for Intel CPUs that leverages MKLDNN acceleration, H‑Swish activation, selective SE modules, larger kernels, and expanded fully‑connected layers to achieve higher accuracy without increasing inference latency across image classification, detection, and segmentation tasks.

CPU optimizationMKLDNNdeep learning

0 likes · 25 min read

PP-LCNet: A Lightweight CPU-Optimized Convolutional Neural Network

Rare Earth Juejin Tech Community

Jul 22, 2023 · Artificial Intelligence

Building an Image Classification Model with Transformers and TensorFlow: Theory, Code, and Practice

This article explains how to leverage computer‑vision techniques and deep‑learning frameworks such as Transformers and TensorFlow to build a complete image‑classification pipeline, covering the underlying RGB and CNN principles, model architecture, data preparation, training, and inference with runnable Python code.

CNNPythonTensorFlow

0 likes · 15 min read

Building an Image Classification Model with Transformers and TensorFlow: Theory, Code, and Practice

DataFunSummit

Apr 21, 2023 · Artificial Intelligence

Fine‑Tuning a ViT Image Classification Model on a Small Flower Dataset Using ModelScope

This tutorial walks through the complete process of fine‑tuning a Vision Transformer (ViT) model for 14‑class flower image classification on ModelScope, covering dataset preparation, model loading, training configuration, evaluation, and inference with practical code examples.

Fine-tuningModelScopePython

0 likes · 14 min read

Fine‑Tuning a ViT Image Classification Model on a Small Flower Dataset Using ModelScope

Rare Earth Juejin Tech Community

Oct 18, 2022 · Artificial Intelligence

Practical Implementation of Vision Transformer (ViT) for Image Classification in PyTorch

This article walks readers through building, training, and evaluating a Vision Transformer (ViT) model for a five‑class flower classification task, providing detailed code snippets, model architecture explanations, training script adjustments, and experimental results that highlight the importance of pre‑trained weights.

PyTorchViTVision Transformer

0 likes · 13 min read

Practical Implementation of Vision Transformer (ViT) for Image Classification in PyTorch

HomeTech

Sep 20, 2022 · Artificial Intelligence

Deep Learning for Image Classification: Classic Networks, Attention Mechanisms, and Their Application to Fine‑Grained Classification and Automotive Series Recognition

This article reviews the evolution of deep‑learning image‑classification networks, surveys attention mechanisms for fine‑grained tasks, describes the CVPR 2022 FGVC9 competition solution using RegNetY and random attention cropping, and discusses its deployment in automotive series recognition along with future challenges.

CVPRattention mechanismsautomotive recognition

0 likes · 19 min read

Deep Learning for Image Classification: Classic Networks, Attention Mechanisms, and Their Application to Fine‑Grained Classification and Automotive Series Recognition

Zhuanzhuan Tech

Aug 17, 2022 · Artificial Intelligence

Designing a Scalable Image Classification System for Prohibited Item Detection in a Second‑hand E‑commerce Platform

This article describes how a second‑hand e‑commerce company built a fast, modular image‑classification pipeline using small binary classifiers, efficientNet‑b0, and active‑learning‑driven data annotation to detect prohibited items while keeping inference latency under 200 ms and reducing labeling costs dramatically.

AIModel Architectureactive learning

0 likes · 10 min read

Designing a Scalable Image Classification System for Prohibited Item Detection in a Second‑hand E‑commerce Platform

JD Tech

Jul 18, 2022 · Artificial Intelligence

AI-Powered Visual Defect Detection for Mobile App UI Testing: Methodology, Data Construction, Model Training, and Evaluation

This article presents an end‑to‑end AI‑driven visual testing solution for mobile applications, detailing the business pain points, data set construction, CNN‑based model design, training procedures, performance evaluation with ROC and confusion matrices, and future directions for improving defect detection accuracy.

UI testingcomputer visiondeep learning

0 likes · 14 min read

AI-Powered Visual Defect Detection for Mobile App UI Testing: Methodology, Data Construction, Model Training, and Evaluation

Baidu Geek Talk

Apr 13, 2022 · Artificial Intelligence

Smart Retail Product Recognition Solution Using PaddlePaddle PP-ShiTu

The article presents PaddlePaddle’s PP‑ShiTu‑based smart retail product recognition solution, detailing a complete pipeline—from data preparation and model optimization to low‑latency deployment—that overcomes high‑similarity packaging, rapid SKU changes, and costly retraining, achieving over 98 % Top‑1 recall with 0.2‑second CPU inference.

AI deploymentPP-ShiTuPaddlePaddle

0 likes · 7 min read

Smart Retail Product Recognition Solution Using PaddlePaddle PP-ShiTu

Python Programming Learning Circle

Jan 18, 2022 · Artificial Intelligence

Fashion MNIST Image Classification Using TensorFlow 2.x in Python

This tutorial demonstrates how to load the Fashion MNIST dataset, explore and preprocess the images, build and compile a neural network with TensorFlow 2.x, train the model, evaluate its accuracy, and use the trained model to make predictions on clothing images, providing complete Python code examples throughout.

Fashion MNISTPythonTensorFlow

0 likes · 16 min read

Fashion MNIST Image Classification Using TensorFlow 2.x in Python

DeWu Technology

Dec 7, 2021 · Artificial Intelligence

White Screen Detection with TensorFlow: Data Preparation, Model Building, and Deployment

The article details a TensorFlow pipeline for detecting white‑screen screenshots in a WebView, covering data preparation from labeled image folders, a CNN architecture with preprocessing, training and validation steps, model saving, inference usage, and strategies to mitigate over‑fitting.

CNNPythonTensorFlow

0 likes · 12 min read

White Screen Detection with TensorFlow: Data Preparation, Model Building, and Deployment

Laravel Tech Community

Sep 5, 2021 · Artificial Intelligence

Comprehensive Collection of Open Data Sources and Datasets for AI and Data Analysis

This article provides a curated list of publicly available data query websites, simple universal datasets, large-scale collections, and specialized datasets for machine learning, image classification, text classification, and recommendation systems, offering valuable resources for AI research and data-driven projects.

Artificial IntelligenceBig DataRecommendation systems

0 likes · 7 min read

Comprehensive Collection of Open Data Sources and Datasets for AI and Data Analysis

DataFunTalk

Aug 14, 2021 · Artificial Intelligence

Multimodal Advertisement Detection System for WeChat "KanKan" Articles

This article introduces a multimodal advertisement detection framework for WeChat KanKan that decomposes the problem into text, image, and article‑structure dimensions, presents novel models for ad text and image recognition, and describes how sequence classification and visualisation are used to filter severe ad‑spam articles.

Text ClassificationWeChatadvertisement detection

0 likes · 16 min read

Multimodal Advertisement Detection System for WeChat "KanKan" Articles

Xianyu Technology

Jun 9, 2021 · Artificial Intelligence

Applying Visual AI Techniques for Image Quality and Duplicate Detection in Xianyu Marketplace

By deploying large‑scale visual AI—including a ResNet‑101 classifier, ArcFace‑trained matching features, clustering‑based sub‑category refinement, and product‑level image indexing—Xianyu’s marketplace dramatically improves image quality, removes duplicates, enhances search relevance and feed diversity, and filters non‑compliant content.

Duplicate Detectioncomputer visiondeep learning

0 likes · 16 min read

Applying Visual AI Techniques for Image Quality and Duplicate Detection in Xianyu Marketplace

360 Tech Engineering

Apr 16, 2021 · Artificial Intelligence

Applying YOLOv5 Object Detection for Black, Color, and Normal Screen Classification in Video Frames

This article presents a method that replaces traditional manual video frame quality checks with an automated YOLOv5‑based object detection pipeline, detailing data labeling, model training, loss computation, inference code, and experimental results that show higher accuracy than ResNet for classifying black, color‑screen, and normal frames.

PythonYOLOv5deep learning

0 likes · 12 min read

Applying YOLOv5 Object Detection for Black, Color, and Normal Screen Classification in Video Frames

360 Quality & Efficiency

Apr 16, 2021 · Artificial Intelligence

Applying YOLOv5 Object Detection for Black, Color, and Blank Screen Classification in Video Frames

This article presents a method that replaces manual visual inspection with an automated YOLOv5‑based object detection pipeline to classify video frames as normal, colorful, or black screens, detailing data annotation, training, loss calculation, inference code, and showing a 97% accuracy improvement over ResNet.

PythonYOLOv5computer vision

0 likes · 11 min read

Applying YOLOv5 Object Detection for Black, Color, and Blank Screen Classification in Video Frames

360 Quality & Efficiency

Aug 7, 2020 · Artificial Intelligence

Replacing Fully Connected Layers with Fully Convolutional Networks for Variable‑Scale Image Tasks

This article analyses the drawbacks of using fully‑connected layers in convolutional neural networks for image tasks, proposes fully‑convolutional alternatives with 1×1 convolutions and strategic max‑pooling, provides TensorFlow code examples, compares model sizes and performance, and discusses deployment considerations for variable‑size inputs.

CNNFully Convolutional NetworkTensorFlow

0 likes · 7 min read

Replacing Fully Connected Layers with Fully Convolutional Networks for Variable‑Scale Image Tasks

Xianyu Technology

Jun 4, 2020 · Artificial Intelligence

NBDT: Neural-Backed Decision Trees for Interpretable Image Classification

NBDT (Neural‑Backed Decision Trees) merges a pretrained CNN with a WordNet‑derived hierarchical tree, using the network’s final‑layer weights as class embeddings and a combined classification loss, to deliver state‑of‑the‑art image classification that remains interpretable through explicit hierarchical reasoning.

CNNExplainable Machine LearningNBDT

0 likes · 11 min read

NBDT: Neural-Backed Decision Trees for Interpretable Image Classification

Python Programming Learning Circle

Mar 25, 2020 · Artificial Intelligence

Captcha Generation and Recognition Using a Convolutional Neural Network – Project Overview and Implementation

This article presents a complete Python implementation for generating captcha images, loading and preprocessing data, defining a three‑layer convolutional neural network, and training and evaluating the model with TensorBoard, achieving over 99% training accuracy and 93% test accuracy.

CNNTensorFlowcaptcha

0 likes · 7 min read

Captcha Generation and Recognition Using a Convolutional Neural Network – Project Overview and Implementation