Dec 2, 2021 · Artificial Intelligence

Transfer Learning with ShuffleNetV2 for Flower Classification

This article walks through building a PyTorch ShuffleNetV2 model, preparing the Kaggle Flowers dataset, training with transfer learning on a GPU, visualizing loss and accuracy, and performing inference on five test images, achieving nearly 90% validation accuracy after 95 epochs.

CNNPyTorchShuffleNetV2

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Transfer Learning with ShuffleNetV2 for Flower Classification

Tencent Cloud Developer

Mar 6, 2020 · Artificial Intelligence

WeChat "Scan" Object Detection: Mobile AI Model Design, Optimization, and Deployment

The paper presents a lightweight, anchor‑free CenterNet‑based object‑ness detector for WeChat’s Scan feature, built on a ShuffleNetV2 backbone with enlarged 5×5 depth‑wise convolutions, a streamlined detection head, and a Pyramid Interpolation Module, then quantized, ONNX‑converted and NCNN‑deployed to achieve a 436 KB model running in ~15 ms per frame on an iPhone 8 CPU.

CenterNetMobile AIModel Optimization

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WeChat "Scan" Object Detection: Mobile AI Model Design, Optimization, and Deployment