Structure of the repo:

imagenet_table.csv is the dataset copied manually from the Papers with Code ImageNet Classification section.

generate_frames.py is a script which takes in the imagenet_table and generates the following files:

• top_results_at_time_t.csv: a file containing a subset of imagenet_table.csv, containing only the results that were the state of the art (on 1% accuracy) at the time they were published. I copied its results to the Top Results at Time t sheet in the Drive.

• imagenet_table.csv: a file containing three columns:

  • date: all dates from earliest one present in imagenet_table.csv to 2019-11-25
  • No extra training data: a result from top_results_at_time_t.csv published on that date on empty
  • Extra training data: a result from top_results_at_time_t.csv published on that date on empty

  I copied its results to the Accuracy to Plot sheet in the Drive.

1. Double check current results on Papers with Code
2. Download sheet "Data with Paper Links" as csv
3. Run generate_frames.py
4. Upload/copy top_results_at_time_t_CIFAR.csv to CIFAR-100 - Top results at time t sheet on drive
5. Upload/copy accuracy_to_plot_CIFAR.csv to CIFAR-100 - Percentage Correct data sheet on drive --> CIFAR-100 - Top results at time t + details automatically updates