SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image
  Classification
Data curation is the problem of how to collect and organize samples into a
dataset that supports efficient learning. Despite the centrality of the task,
little work has been devoted towards a large-scale, systematic comparison of
various curation methods. In this work, we take steps towards a formal
evaluation of data curation strategies and introduce SELECT, the first
large-scale benchmark of curation strategies for image classification.
  In order to generate baseline methods for the SELECT benchmark, we create a
new dataset, ImageNet++, which constitutes the largest superset of ImageNet-1K
to date. Our dataset extends ImageNet with 5 new training-data shifts, each
approximately the size of ImageNet-1K itself, and each assembled using a
distinct curation strategy. We evaluate our data curation baselines in two
ways: (i) using each training-data shift to train identical image
classification models from scratch (ii) using the data itself to fit a
pretrained self-supervised representation.
  Our findings show interesting trends, particularly pertaining to recent
methods for data curation such as synthetic data generation and lookup based on
CLIP embeddings. We show that although these strategies are highly competitive
for certain tasks, the curation strategy used to assemble the original
ImageNet-1K dataset remains the gold standard. We anticipate that our benchmark
can illuminate the path for new methods to further reduce the gap. We release
our checkpoints, code, documentation, and a link to our dataset at
https://github.com/jimmyxu123/SELECT.
670535523c11ec9fe7f35755
Benjamin Feuer
claimed_verified
2024-10-08T15:16:35.375Z
62f7f4efe7c1c9bf10c81465
https://cdn-avatars.huggingface.co/v1/production/uploads/62f7f4efe7c1c9bf10c81465/AYlOg0fkP1o4GAP-8Y3xt.jpeg
Benjamin Feuer
user
penfever
670535523c11ec9fe7f35756
Jiawei Xu
670535523c11ec9fe7f35757
Niv Cohen
670535523c11ec9fe7f35758
Patrick Yubeaton
670535523c11ec9fe7f35759
Govind Mittal
670535523c11ec9fe7f3575a
Chinmay Hegde
claimed_verified
2025-03-03T16:07:46.798Z
631620f6894404e25068856f
/avatars/52c30caa0ee11347f82420a14ec19996.svg
Chinmay Hegde
user
chegde
SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image
  Classification
Data curation is the problem of how to collect and organize samples into a
dataset that supports efficient learning. Despite the centrality of the task,
little work has been devoted towards a large-scale, systematic comparison of
various curation methods. In this work, we take steps towards a formal
evaluation of data curation strategies and introduce SELECT, the first
large-scale benchmark of curation strategies for image classification.
  In order to generate baseline methods for the SELECT benchmark, we create a
new dataset, ImageNet++, which constitutes the largest superset of ImageNet-1K
to date. Our dataset extends ImageNet with 5 new training-data shifts, each
approximately the size of ImageNet-1K itself, and each assembled using a
distinct curation strategy. We evaluate our data curation baselines in two
ways: (i) using each training-data shift to train identical image
classification models from scratch (ii) using the data itself to fit a
pretrained self-supervised representation.
  Our findings show interesting trends, particularly pertaining to recent
methods for data curation such as synthetic data generation and lookup based on
CLIP embeddings. We show that although these strategies are highly competitive
for certain tasks, the curation strategy used to assemble the original
ImageNet-1K dataset remains the gold standard. We anticipate that our benchmark
can illuminate the path for new methods to further reduce the gap. We release
our checkpoints, code, documentation, and a link to our dataset at
https://github.com/jimmyxu123/SELECT.
A benchmark named SELECT evaluates various large-scale data curation strategies for image classification, showing that traditional ImageNet-1K curation remains superior to recent methods like synthetic data and CLIP-based lookup.
SELECT data curation ImageNet++ image classification synthetic data generation CLIP embeddings self-supervised representation