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xkx's Tech General Store
xkx's Tech General Store
Apr 16, 2026 · Artificial Intelligence

Understanding Vision Transformers: Core ViT Principles and Multimodal Applications

This article explains the Vision Transformer (ViT) architecture, compares it with CNNs and traditional NLP Transformers, details its encoding process and attention mechanisms, and demonstrates a practical leaf‑disease classification project that showcases ViT’s role in multimodal AI systems.

AI FundamentalsDeep LearningMultimodal AI
0 likes · 10 min read
Understanding Vision Transformers: Core ViT Principles and Multimodal Applications
Data Party THU
Data Party THU
Feb 1, 2026 · Artificial Intelligence

How Tiny Perturbations Can Fool 95% Accurate Image Classifiers

Despite achieving over 95% accuracy on ImageNet, popular models like ResNet, VGG, and EfficientNet can be easily misled by carefully crafted adversarial examples using FGSM, revealing deep learning’s inherent vulnerability and prompting the need for robust defense strategies.

FGSMPyTorchadversarial examples
0 likes · 11 min read
How Tiny Perturbations Can Fool 95% Accurate Image Classifiers
vivo Internet Technology
vivo Internet Technology
Nov 5, 2025 · Frontend Development

Build an AI‑Powered Draw‑and‑Guess Game with TensorFlow.js on the Frontend

This tutorial walks you through the entire process of creating a browser‑based AI version of the classic "draw‑and‑guess" game, covering model selection, dataset preparation, CNN training with TensorFlow.js, model integration into a Vue front‑end, performance optimizations, and deployment steps.

TensorFlow.jsimage classificationquickdraw
0 likes · 16 min read
Build an AI‑Powered Draw‑and‑Guess Game with TensorFlow.js on the Frontend
AI Frontier Lectures
AI Frontier Lectures
Sep 9, 2025 · Artificial Intelligence

Can UniConvNet Expand Receptive Fields While Preserving Gaussian Distribution?

The paper introduces UniConvNet, a novel convolutional architecture that expands the effective receptive field (ERF) of ConvNets without breaking the asymptotically Gaussian distribution (AGD), achieving superior accuracy‑parameter and accuracy‑FLOPs trade‑offs across image classification, detection, and segmentation benchmarks.

Deep LearningEffective Receptive FieldUniConvNet
0 likes · 9 min read
Can UniConvNet Expand Receptive Fields While Preserving Gaussian Distribution?
Alibaba Cloud Developer
Alibaba Cloud Developer
Jul 23, 2025 · Artificial Intelligence

Build a Handwritten Digit Recognizer with TensorFlow: Step‑by‑Step MNIST Tutorial

Learn the fundamentals of deep learning by building, training, and evaluating a TensorFlow model that recognizes handwritten digits from the MNIST dataset, covering data preparation, network architecture, activation functions, optimizer choices, model compilation, training loops, evaluation metrics, and visualization of predictions.

KerasMNISTNeural Network
0 likes · 20 min read
Build a Handwritten Digit Recognizer with TensorFlow: Step‑by‑Step MNIST Tutorial
JavaEdge
JavaEdge
Feb 24, 2025 · Artificial Intelligence

Build a CIFAR‑10 Image Classifier with PyTorch – A Java Developer’s Guide

This tutorial walks Java developers through building, training, evaluating, and deploying a CIFAR‑10 image classifier using PyTorch, covering data loading, preprocessing, network definition, loss and optimizer setup, GPU acceleration, model saving, and per‑class accuracy analysis.

CIFAR-10Deep LearningGPU
0 likes · 18 min read
Build a CIFAR‑10 Image Classifier with PyTorch – A Java Developer’s Guide
AI Code to Success
AI Code to Success
Feb 19, 2025 · Artificial Intelligence

How to Build Traffic‑Sign Recognition and Sentiment Analysis with Keras – A Step‑by‑Step Guide

This article walks through practical Keras tutorials for image‑based traffic‑sign classification and text‑based sentiment analysis, covering data preparation, preprocessing, model construction, training, evaluation, deployment, and a concise comparison of Keras with TensorFlow and PyTorch.

Deep LearningKerasPyTorch
0 likes · 19 min read
How to Build Traffic‑Sign Recognition and Sentiment Analysis with Keras – A Step‑by‑Step Guide
Python Programming Learning Circle
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
Huolala Tech
Huolala Tech
Nov 28, 2024 · Artificial Intelligence

How AI-Powered OCR Transforms Freight Document and Vehicle Verification

This article explains how AI-driven OCR combined with deep‑learning image classification streamlines ticket, document, and license‑plate verification in freight logistics, detailing system architecture, algorithmic components, and future prospects for unified large‑model OCR solutions.

Artificial IntelligenceOCRimage classification
0 likes · 12 min read
How AI-Powered OCR Transforms Freight Document and Vehicle Verification
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Oct 8, 2023 · Artificial Intelligence

Why the Scale‑Aware Modulation Transformer Outperforms CNNs and Vision Transformers with Fewer Parameters

The Scale‑Aware Modulation Transformer (SMT) introduces a lightweight SAM module and an Evolutionary Hybrid Network that together achieve higher accuracy on ImageNet, COCO, and ADE20K while using significantly fewer parameters and FLOPs than existing CNN and Transformer baselines.

SMTScale‑Aware ModulationSemantic Segmentation
0 likes · 12 min read
Why the Scale‑Aware Modulation Transformer Outperforms CNNs and Vision Transformers with Fewer Parameters
Rare Earth Juejin Tech Community
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
Rare Earth Juejin Tech Community
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.

Deep LearningPretrained ModelsPyTorch
0 likes · 13 min read
Practical Implementation of Vision Transformer (ViT) for Image Classification in PyTorch
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Oct 12, 2022 · Artificial Intelligence

Unlock Vision AI: How EasyCV Streamlines Datasets and Model Training

This article introduces EasyCV, an open‑source all‑in‑one visual algorithm platform that abstracts diverse data sources, provides SOTA self‑supervised models, and offers ready‑to‑download datasets for image classification, object detection, segmentation, and pose estimation, complete with configuration examples.

Deep LearningEasyCVcomputer vision
0 likes · 9 min read
Unlock Vision AI: How EasyCV Streamlines Datasets and Model Training
HomeTech
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.

CVPRDeep LearningFine-Grained Classification
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
Java Architect Essentials
Java Architect Essentials
Sep 18, 2022 · Industry Insights

Why AI Porn Detection Still Struggles: Key Challenges and the Need for Human Moderators

AI-powered porn detection leverages deep neural networks to classify images, but faces serious hurdles such as visual similarity with benign content, subjective standards of obscenity, and vulnerabilities stemming from training data, making human moderators indispensable for reliable content safety.

AI moderationContent SafetyDeep Learning
0 likes · 3 min read
Why AI Porn Detection Still Struggles: Key Challenges and the Need for Human Moderators
Programmer DD
Programmer DD
Sep 13, 2022 · Artificial Intelligence

Why AI Porn Detection Still Struggles: Key Challenges Explained

AI-based porn detection uses deep neural networks to classify images, but faces tough hurdles such as visual similarity with benign content, subjective standards for nudity, and vulnerabilities from training‑data dependence, meaning human moderators remain essential for reliable safety.

AI moderationContent SafetyDeep Learning
0 likes · 3 min read
Why AI Porn Detection Still Struggles: Key Challenges Explained
Zhuanzhuan Tech
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.

.aiActive Learningimage classification
0 likes · 10 min read
Designing a Scalable Image Classification System for Prohibited Item Detection in a Second‑hand E‑commerce Platform
JD Tech
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.

Deep LearningUI testingcomputer vision
0 likes · 14 min read
AI-Powered Visual Defect Detection for Mobile App UI Testing: Methodology, Data Construction, Model Training, and Evaluation
Code DAO
Code DAO
May 27, 2022 · Artificial Intelligence

Building an Image Classification Model with CNNs

This article explains how to train a convolutional neural network on a remote GPU for image classification, covering convolution, padding, activation, pooling, dropout, flattening, fully‑connected layers, dataset preparation, model definition, training, and prediction using TensorFlow/Keras.

CNNFood-101GPU training
0 likes · 13 min read
Building an Image Classification Model with CNNs
Baidu Geek Talk
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
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.

Deep LearningFashion-MNISTPython
0 likes · 16 min read
Fashion MNIST Image Classification Using TensorFlow 2.x in Python
Code DAO
Code DAO
Jan 15, 2022 · Artificial Intelligence

How Tuun’s Automated Data Augmentation Boosts AI Model Accuracy

The article explains how Tuun, an open‑source Bayesian‑optimization tool, automatically searches data‑augmentation policies for machine‑learning models, details the setup with Microsoft NNI, provides code and configuration examples, and presents experiments on CIFAR‑10/100 and SVHN showing that Tuun‑generated policies match or surpass expert‑tuned strategies and further improve performance when combined.

AutoMLBayesian OptimizationNNI
0 likes · 14 min read
How Tuun’s Automated Data Augmentation Boosts AI Model Accuracy
Code DAO
Code DAO
Dec 1, 2021 · Artificial Intelligence

Building a Satellite Image Classifier with PyTorch ResNet34

This article walks through creating a satellite image classification pipeline using PyTorch and a pretrained ResNet34 model, covering dataset preparation, project structure, data loading, model definition, training, validation, loss/accuracy plotting, and inference on new images with detailed code examples and results.

Deep LearningPyTorchPython
0 likes · 17 min read
Building a Satellite Image Classifier with PyTorch ResNet34
Laravel Tech Community
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 DataOpen Data
0 likes · 7 min read
Comprehensive Collection of Open Data Sources and Datasets for AI and Data Analysis
DataFunTalk
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.

Multimodal AIText ClassificationWeChat
0 likes · 16 min read
Multimodal Advertisement Detection System for WeChat "KanKan" Articles
21CTO
21CTO
Jun 28, 2021 · Artificial Intelligence

How Multimodal AI Detects Pornographic Videos: Image & Audio Fusion Explained

This article outlines a multimodal AI framework for detecting pornographic video content by combining image and audio analysis, detailing the challenges of visual and speech-based recognition, describing the DCNet and RANet model architectures, fusion strategies, and reporting experimental accuracy of 93.4% on a 3k test set.

.aiAudio ClassificationDeep Learning
0 likes · 5 min read
How Multimodal AI Detects Pornographic Videos: Image & Audio Fusion Explained
Xianyu Technology
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.

Deep Learningcomputer visionduplicate detection
0 likes · 16 min read
Applying Visual AI Techniques for Image Quality and Duplicate Detection in Xianyu Marketplace
Alimama Tech
Alimama Tech
May 20, 2021 · Artificial Intelligence

How Alibaba’s AI Powers Brand Risk Detection: Models, Data, and Results

This article details Alibaba's AliMama brand risk identification system, covering the challenges of counterfeit detection, the construction of large‑scale brand datasets, the design of classification, logo detection, and variation models, their optimization, evaluation metrics, and future directions for AI‑driven brand protection.

.aiAlibabaDeep Learning
0 likes · 22 min read
How Alibaba’s AI Powers Brand Risk Detection: Models, Data, and Results
360 Tech Engineering
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.

PythonYOLOv5image classification
0 likes · 12 min read
Applying YOLOv5 Object Detection for Black, Color, and Normal Screen Classification in Video Frames
360 Quality & Efficiency
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.

Deep LearningPythonYOLOv5
0 likes · 11 min read
Applying YOLOv5 Object Detection for Black, Color, and Blank Screen Classification in Video Frames
360 Quality & Efficiency
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 NetworkModel Optimization
0 likes · 7 min read
Replacing Fully Connected Layers with Fully Convolutional Networks for Variable‑Scale Image Tasks
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Jul 29, 2020 · Artificial Intelligence

Boosting Small Industrial Image Datasets with ModelArts Augmentation and Evaluation

This article describes a practical workflow for expanding a limited industrial solar‑panel defect dataset using flip augmentation, ModelArts smart labeling, and targeted data‑balancing techniques, then evaluates the impact on a ResNet‑50 classifier with detailed accuracy and recall metrics, demonstrating how thoughtful augmentation can improve defect detection performance.

Deep LearningModelArtsdata augmentation
0 likes · 10 min read
Boosting Small Industrial Image Datasets with ModelArts Augmentation and Evaluation
Xianyu Technology
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.

CNNDecision TreesExplainable Machine Learning
0 likes · 11 min read
NBDT: Neural-Backed Decision Trees for Interpretable Image Classification
Taobao Frontend Technology
Taobao Frontend Technology
May 25, 2020 · Frontend Development

How to Build Front‑End AI Experiments with Pipcook: From Setup to Real‑World Image Classification

This comprehensive guide walks front‑end developers through preparing hardware and OS, installing Python and Node environments, launching Pipcook's visual board, running handwritten digit and image classification experiments, creating and augmenting training samples, configuring pipelines, training models, and understanding deployment, all using the Pipcook framework.

data augmentationimage classificationmachine learning
0 likes · 34 min read
How to Build Front‑End AI Experiments with Pipcook: From Setup to Real‑World Image Classification
Python Programming Learning Circle
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.

Pythonimage classification
0 likes · 7 min read
Captcha Generation and Recognition Using a Convolutional Neural Network – Project Overview and Implementation
Ctrip Technology
Ctrip Technology
Jan 16, 2020 · Artificial Intelligence

Ctrip's Marco Polo Platform: AI‑Driven Content Generation, Semantic Matching, and Productization

The article details Ctrip’s Marco Polo content platform, describing its data, algorithm, and functional layers, and explains how AI techniques such as NLP, semantic matching, named‑entity recognition, and image classification are applied to automate product‑centric content mining, article generation, quality rating, and first‑image selection.

.aiCtripNLP
0 likes · 16 min read
Ctrip's Marco Polo Platform: AI‑Driven Content Generation, Semantic Matching, and Productization
UCloud Tech
UCloud Tech
Dec 10, 2019 · Artificial Intelligence

Train and Deploy a CIFAR‑10 Image Classification Model with UAI Platform

This tutorial walks university students through the complete workflow of using the CIFAR‑10 dataset to train a convolutional neural network for image classification and then deploying the model as an online inference service on the UAI‑Train and UAI‑Inference platforms.

CIFAR-10Deep LearningDocker
0 likes · 6 min read
Train and Deploy a CIFAR‑10 Image Classification Model with UAI Platform
Alibaba Cloud Developer
Alibaba Cloud Developer
Dec 3, 2019 · Artificial Intelligence

How Alibaba Detects ‘Disgusting’ Images on Taobao with AI

This article describes Alibaba's AI system for automatically filtering nauseating product images on Taobao, covering challenges such as cold‑start, class imbalance, and diverse visual features, and detailing solutions like semi‑supervised learning, active learning, OHEM‑cascade, attention mechanisms, and the resulting business impact.

Active LearningAttention MechanismSemi-supervised Learning
0 likes · 15 min read
How Alibaba Detects ‘Disgusting’ Images on Taobao with AI
Alibaba Cloud Developer
Alibaba Cloud Developer
Sep 12, 2019 · Artificial Intelligence

How a Simple Learning‑Rate Trick Detects 90% of Noisy Labels in Image Data

Training deep neural networks on large‑scale weakly labeled image data suffers from noisy annotations that degrade performance, but a simple algorithm that adjusts the learning‑rate during training can automatically identify up to 90% of noisy samples, improving dataset cleanliness and model accuracy without manual intervention.

Deep Learningdata cleaningimage classification
0 likes · 15 min read
How a Simple Learning‑Rate Trick Detects 90% of Noisy Labels in Image Data
Huawei Cloud Developer Alliance
Huawei Cloud Developer Alliance
Aug 30, 2019 · Artificial Intelligence

Distinguish Leopards vs Jaguars Using ModelArts AutoML

This guide walks you through configuring Huawei Cloud ModelArts, creating an AutoML image‑classification project, labeling cat and leopard photos, training a model, deploying it as an online service, and testing predictions to accurately differentiate between leopards and jaguars.

.aiHuawei CloudModelArts
0 likes · 5 min read
Distinguish Leopards vs Jaguars Using ModelArts AutoML
HomeTech
HomeTech
Apr 18, 2019 · Artificial Intelligence

An Overview of Image Processing Techniques and Common Tools for Beginners

This article provides a concise introduction to image processing, covering its hierarchical structure, fundamental techniques such as classification, detection, segmentation, geometric transformation, and the most widely used libraries and deep‑learning frameworks for newcomers.

Image processingcomputer visionimage classification
0 likes · 9 min read
An Overview of Image Processing Techniques and Common Tools for Beginners
ITPUB
ITPUB
Feb 23, 2019 · Artificial Intelligence

Explore a 1.59 Million Image NSFW Dataset with 159 Fine-Grained Categories

A data scientist from Besedo has open‑sourced a massive NSFW image dataset containing 1.589 million pictures, organized into 159 primary categories and further sub‑categories, with download scripts and GitHub links, requiring about 500 GB of storage and cautioning against viewing in the office.

AI researchGitHubNSFW dataset
0 likes · 3 min read
Explore a 1.59 Million Image NSFW Dataset with 159 Fine-Grained Categories
ITPUB
ITPUB
Feb 16, 2019 · Artificial Intelligence

A 1.59 Million‑Image NSFW Dataset Released for Advanced Content Filtering

Data scientist Evgeny Bazarov has open‑sourced a 1.589 million‑image NSFW dataset organized into 159 fine‑grained categories, providing GitHub links, download scripts, and a 500 GB storage requirement, enabling researchers to build more precise adult‑content detection models.

GitHubNSFW datasetcomputer vision
0 likes · 3 min read
A 1.59 Million‑Image NSFW Dataset Released for Advanced Content Filtering
Architects Research Society
Architects Research Society
Feb 9, 2019 · Artificial Intelligence

Introduction to TensorFlow and Building a Simple Neural Network for Image Classification

This article introduces TensorFlow, explains when neural networks are appropriate, outlines the general workflow for solving image‑based problems, and provides a step‑by‑step Python implementation of a multilayer perceptron that classifies handwritten digits, while also discussing TensorFlow's strengths, limitations, and alternatives.

Deep LearningPythonTensorFlow
0 likes · 14 min read
Introduction to TensorFlow and Building a Simple Neural Network for Image Classification
Qizhuo Club
Qizhuo Club
Jul 30, 2018 · Artificial Intelligence

Mastering Inception v3: From Codebase to Rose Recognition with TensorFlow

This article walks through the Inception v3 TensorFlow codebase, explains its design principles, details the training script flags and loss calculations, shows how to fine‑tune the model on a flower dataset, and provides practical tips for building custom datasets and optimizing hyper‑parameters for image classification.

CNNInceptionPython
0 likes · 25 min read
Mastering Inception v3: From Codebase to Rose Recognition with TensorFlow
Tencent Cloud Developer
Tencent Cloud Developer
Jun 25, 2018 · Artificial Intelligence

Using MLP for Image Classification: Implementation, Results, and Limitations

The article demonstrates how a simple fully‑connected MLP can be trained on a small 64×64×3 cat‑vs‑non‑cat dataset, achieving perfect training accuracy but only 78 % test accuracy, and explains that parameter explosion, vanishing gradients, and lack of spatial invariance limit MLPs, motivating the shift to CNNs.

H5pyMLPPython
0 likes · 15 min read
Using MLP for Image Classification: Implementation, Results, and Limitations
Baidu Intelligent Testing
Baidu Intelligent Testing
Jun 5, 2018 · Artificial Intelligence

Applying Deep Learning for Automated UI Bug Detection in Mobile Apps

To address the rising cost of manual UI testing on diverse mobile devices, the article presents a deep‑learning‑based solution using PaddlePaddle that automatically detects UI style bugs such as misaligned controls, text overlap, and blank spaces through data‑driven model training, image preprocessing, and classification.

AutomationDeep LearningPaddlePaddle
0 likes · 10 min read
Applying Deep Learning for Automated UI Bug Detection in Mobile Apps
21CTO
21CTO
Oct 9, 2017 · Artificial Intelligence

How Wukong’s AI Porn Detection System Achieves 99.5% Accuracy

This article explains the challenges of image‑based porn detection, details the multi‑label classification approach of the Wukong system, and reveals the deep‑learning techniques—including CNN evolution, transfer learning, loss functions, adversarial training, and GAN‑based data augmentation—that enable over 99.5% accuracy with massive daily request volumes.

CNNGaNadversarial training
0 likes · 18 min read
How Wukong’s AI Porn Detection System Achieves 99.5% Accuracy
21CTO
21CTO
Sep 30, 2017 · Artificial Intelligence

Top 10 Cutting-Edge Deep Learning Architectures for Computer Vision

This article surveys recent breakthroughs in deep learning for computer vision, explains what constitutes an advanced architecture, outlines common vision tasks, and provides concise overviews plus paper and Keras implementation links for ten influential models such as AlexNet, VGG, ResNet, and GAN.

CNNKerasimage classification
0 likes · 15 min read
Top 10 Cutting-Edge Deep Learning Architectures for Computer Vision