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This tutorial explains how to create an AI‑driven Dou Dizhu (Chinese poker) assistant that captures game screenshots, uses YOLOv5 for card detection, leverages the DouZero model for optimal move prediction, and provides a PyQt5 UI for real‑time play assistance, including environment setup and code examples.
This article explains how a unified AI platform enables rapid, data‑driven creation, training, deployment, and evaluation of OCR models for visually distinct text regions such as seals, meter readings, license plates, and VIN codes, while minimizing hardware and annotation costs.
This article describes a comprehensive solution for detecting checkboxes in document images and determining their selected or unselected status by combining YOLOv5 object detection, synthetic and semi‑synthetic data generation, specialized post‑processing, and association logic to handle varied shapes, positions, and markings.
This guide explains how to build a custom Label Studio ML backend by extending LabelStudioMLBase to wrap a YOLOv5 or MMDetection model, modify its prediction logic, launch the service, and configure the frontend for automated object‑detection annotation, including deployment details and a recruitment notice.
This guide explains how to combine the open‑source labeling platform Label Studio with the YOLOv5 object‑detection model to achieve semi‑automatic annotation, covering installation of both tools, project creation, dataset configuration, and training a custom YOLOv5 model on your own data.
This article provides a comprehensive guide on YOLOv5, covering its background from YOLOv3, detailed code analysis of the model architecture, step‑by‑step instructions for running detect.py, configuring yolov5s.yaml, exporting the model to TorchScript JIT and ONNX formats, and practical inference examples using PyTorch and ONNX Runtime.
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.
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.
To tackle the AMAP‑TECH competition’s dynamic road‑condition classification from scarce, imbalanced vehicle‑video frames, the team combined YOLOv5 object detection, ResNeXt101‑based semantic embeddings, and engineered temporal detection statistics, feeding the fused features into a five‑fold LightGBM model that achieved top weighted‑F1 performance.
This article introduces the fundamentals of object detection, explains the latest YOLOv4 and YOLOv5 architectures, and details the essential components—including data preparation, regularization, backbone, neck, and prediction innovations—along with label smoothing and advanced loss functions for improved detection performance.