WANG TF

In mmcls, dataset can load using [file_client_args](https://github.com/open-mmlab/mmclassification/blob/91b85bb4a5df075ae2690273da32819b298e4395/mmcls/datasets/custom.py#L163) which is supported by mmcv: https://github.com/open-mmlab/mmcv/blob/e417035f5d473b9f85d15ba01267d48d7f30e71e/mmcv/fileio/file_client.py#L790. So I think LMDB can be used in mmcls.

Algorithm Name：YOLO-NAS Conference/Year：2023 Datasets：MS COCO Introduction：providing superior real-time object detection capabilities and production-ready performance Repo：https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md Interpretations：https://zhuanlan.zhihu.com/p/628090349

You can using pipeline like [pyVHR_run_on_video.ipynb](https://github.com/phuselab/pyVHR/blob/master/notebooks/pyVHR_run_on_video.ipynb) ```python #### TEST holistic approach from pyVHR.analysis.pipeline import Pipeline # params videoFileName = "video.avi" # your video path roi_approach = 'holistic' # 'holistic' or...

I think the best way to test is using the video in a open dataset like UBFC. The output is closely depend on your input video.