How HGO‑YOLO Achieves 87.4% Accuracy at 56 FPS with Only 4.6 MB Parameters

This paper presents HGO‑YOLO, a lightweight real‑time anomaly‑behavior detector that integrates HGNetv2 and GhostConv into YOLOv8, achieving 87.4% mAP with just 4.6 MB of parameters and 56 FPS on CPU, and validates its performance across multiple datasets and hardware platforms.

Computer VisionLightweight ModelsYOLO

0 likes · 25 min read

How HGO‑YOLO Achieves 87.4% Accuracy at 56 FPS with Only 4.6 MB Parameters

Alibaba Cloud Big Data AI Platform

Jul 12, 2023 · Artificial Intelligence

How ConaCLIP Boosts Lightweight Text-Image Retrieval with Dual‑Encoder Distillation

ConaCLIP introduces a fully‑connected knowledge interaction graph to distill large dual‑encoder models into compact ones, enhancing text‑image retrieval accuracy and efficiency on edge devices, with extensive experiments and supervision strategies demonstrating significant gains over existing baselines.

ConaCLIPDual EncoderLightweight Models

0 likes · 9 min read

How ConaCLIP Boosts Lightweight Text-Image Retrieval with Dual‑Encoder Distillation

Didi Tech

Jul 13, 2019 · Artificial Intelligence

Computer Vision in Transportation Workshop – Course Overview and Highlights

The Didi Computer Vision in Transportation workshop teaches fundamentals, advanced domain‑adaptation and lightweight model techniques, and real‑world applications such as driver identification and driving‑scenario analysis, delivered by Didi AI Labs experts, emphasizing practical use cases and cutting‑edge research.

Driver IdentificationLightweight Modelsai

0 likes · 6 min read

Computer Vision in Transportation Workshop – Course Overview and Highlights

How HGO‑YOLO Achieves 87.4% Accuracy at 56 FPS with Only 4.6 MB Parameters

How ConaCLIP Boosts Lightweight Text-Image Retrieval with Dual‑Encoder Distillation

Computer Vision in Transportation Workshop – Course Overview and Highlights

How HGO‑YOLO Achieves 87.4% Accuracy at 56 FPS with Only 4.6 MB Parameters