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https://github.com/fivethirtyeight/data - all the data used in 538's analysis projects. Lots of US sports and politics related data
https://github.com/OpportunityInsights/EconomicTracker - One of my current favorites, this is some data being used to track the US economic recovery post COVID. This has a ton of interesting things - Covid related data (including things like lockdown dates, changes in local policy, unemployment changes, etc. at the state and local levels), employment, consumer spending, education related statistics, and Google/Apple mobility reports.
https://github.com/awesomedata/awesome-public-datasets- lots and lots of random datasets broken out by category.
PROBLEM STATEMENT Develop an efficient common strategy and relevant implementation to extract the video-based models in the black box and grey box setting across the following 2 problem statements. 1.Action Classification Model Extraction for Swin-T Model for Action Classification on Kinetics-400 dataset. Download the model from here- https://github.com/SwinTransformer/Video-Swin-Transformer 2.Video Classification Model Extraction for MoViNet-A2-Base Model for Video Classification on Kinetics- 600 dataset Download the model from here- https://tfhub.dev/tensorflow/movinet/a2/base/kinetics-600/classification/3 Blackbox Setting Do not use any relevant data set available and use synthetic or generated data without using the Kinetics series dataset. Also, do not use the same model architecture as the original model to train the extracted model. Greybox Setting You can use 5% of original data (balanced representation of classes) as a starting point to generate the attack dataset. Also, do not use the same model architecture as the original model to train the extracted model. Can someone explain the problem statement in a easy / understandable way ?? What I think is the models have already been provided and we have to do something in Blackbox and greybox . Can someone explain in brief what we have to do in the blackbox / greybox??