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csrnet

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Dense crowd counting with CSRNet + CBAM attention — MAE 61.14 / RMSE 90.91 on ShanghaiTech Part A, beating the CVPR'18 CSRNet baseline (68.2 / 115.0) — paired with a fully local Ollama multimodal agent that explains every prediction.

  • Updated Sep 12, 2026
  • Python

Deep learning-based crowd counting using a VGG16 frontend and dilated convolutions. Supports image and video analysis, density heatmap visualization, frame-by-frame counting, charts, and CSV export using Streamlit, PyTorch, and OpenCV.

  • Updated Oct 26, 2026
  • Jupyter Notebook

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