Good Classification Measures and How to Find Them

Last update: Nov 13, 2022

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Overview

Good Classification Measures and How to Find Them

This repository contains supplementary materials for the paper "Good Classification Measures and How to Find Them" (NeurIPS 2021).

How can I run it?

To run the experiments, you will need numpy, and scikit-learn libraries.

Use the input data files, provided in /data folder, and these scripts:

binary_indicies_bruteforce.py [ ] -- to reproduce the table N3
meteo_inversion_matrix.py [full|10m|2h] -- to reproduce tables N4, N10, N11
multiclass_indicies_imagenet.py -- to reproduce tables N5 and N12
multiclass_indicies_sst.py -- to reproduce the table N6
multiclass_indicies_yeast.py -- to reproduce tables N13 and N14

Owner

Yandex Research

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Good Classification Measures and How to Find Them

Related tags

Overview

Good Classification Measures and How to Find Them

How can I run it?

Owner

Yandex Research

DecoupledNet is semantic segmentation system which using heterogeneous annotations

Source code for Task-Aware Variational Adversarial Active Learning

A framework to train language models to learn invariant representations.

Code for STFT Transformer used in BirdCLEF 2021 competition.

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We envision models that are pre-trained on a vast range of domain-relevant tasks to become key for molecule property prediction

COPA-SSE contains crowdsourced explanations for the Balanced COPA dataset

TensorFlow implementation of "TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?"

Source code for "FastBERT: a Self-distilling BERT with Adaptive Inference Time".

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Example scripts for the detection of lanes using the ultra fast lane detection model in ONNX.

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