A 3D ResNet-50 video classifier trained on a small dashcam dataset reaches 0.88 validation F1 for four forestry work elements, with acknowledged overfitting and limited data.
PyTorchVideo: A Deep Learning Library for Video Understanding
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abstract
We introduce PyTorchVideo, an open-source deep-learning library that provides a rich set of modular, efficient, and reproducible components for a variety of video understanding tasks, including classification, detection, self-supervised learning, and low-level processing. The library covers a full stack of video understanding tools including multimodal data loading, transformations, and models that reproduce state-of-the-art performance. PyTorchVideo further supports hardware acceleration that enables real-time inference on mobile devices. The library is based on PyTorch and can be used by any training framework; for example, PyTorchLightning, PySlowFast, or Classy Vision. PyTorchVideo is available at https://pytorchvideo.org/
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Spatiotemporal Analysis of Forest Machine Operations Using 3D Video Classification
A 3D ResNet-50 video classifier trained on a small dashcam dataset reaches 0.88 validation F1 for four forestry work elements, with acknowledged overfitting and limited data.