UCF Sports Action — dbcollection 0.2.6 documentation

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UCF-Anomaly-Detection-Dataset - Download Summary

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  • This is a dataset on crime in 2014, subdivided by race and offense. Originally found in table 43 in the FBI Web Site "2014 Crime in the United States".This is the same data as in a previous block, but normalized such that "Race" is now a single column rather than a collection of different allows the data to be easily used with and other utilities/
  • A paragraph describing the characteristic of the dataset: its source, reference, etc. categories list of str, default=None. If None (default), load all the categories. If not None, list of category names to load (other categories ignored). load_content bool, default=True. Whether …
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datasets.csv · GitHub

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UCF101 - Center for Research in Computer Vision – UCF CRCV

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  • Hi @blackeagle01, Can you please let me know, If I can use the same code, to train UCF CRIME dataset? Thanks Guru
  • Data acquisition in R (1/4) • Ilya Kashnitsky

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Related Concepts arXiv:2004.00222v3 [cs.CV] 11 Apr 2020

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  • DeepMIL Pytorch Version. Unofficial implemention of "Real-world Anomaly Detection in Surveillance Videos" CVPR2018. The feature extractor is here: -pytorch. we have released I3D features of UCF-Crime, …

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San Francisco Crime Classification | Kaggle

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ucf101 | TensorFlow Datasets

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  • 6.1 Dot density maps. Dot density maps use points on a map to explore spatial relationships. The Houston crime dataset contains the date, time, and address of six types of criminal offenses reported between January and August 2010. The longitude and latitude of each offence was added using geocode function, which takes an address and returns coordinates using the Google Maps API.
  • UCF101 is an action image recognition dataset of real action videos, collected from YouTube UCF101 Ddivided into 101 action categories, this data set is an extension of UCF50 data set …
  • Dataset - UNC Charlotte FAQ - UNC Charlotte
  • 1.5.2 Final Project. For the final project, you will develop a computational analysis of a topic and dataset of your own choice. This is an opportunity to explore (an aspect of) your final term project in more detail – or take a deep-dive in a topic or dataset that you’re interested in.

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