起初想使用opencv-dnn做部署,可是opencv-dnn加载onnx文件时报错了,因而使用onnxruntime做部署。
本套部署程序对应的paper是旷世研究院在ECCV2022发表的一篇文章《Real-Time Intermediate Flow Estimation for Video Frame Interpolation》,
模型简称rife,onnx模型文件只有8.79M,模型已经很小了,
训练源码在 https://github.com/megvii-research/ECCV2022-RIFE ,有3.9k个star。
而谷歌在ECCV2022发布的做视频帧插值的文章《FILM: Frame Interpolation for Large Motion》,
模型简称film,训练源码在 https://github.com/google-research/frame-interpolation ,有2.6k个star
,但是它的onnx模型文件却有133M,相比于旷世发布的rife,大很多了。因此旷世发布的rife更适合在工业界落地应用
没有合适的资源?快使用搜索试试~ 我知道了~
onnxruntime部署实时视频帧插值包含C++和Python源码+模型+说明.zip
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onnxruntime部署实时视频帧插值包含C++和Python源码+模型+说明.zip
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