generated from eliahuhorwitz/Academic-project-page-template
-
Notifications
You must be signed in to change notification settings - Fork 0
/
index.html
388 lines (360 loc) · 21.7 KB
/
index.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<!-- Meta tags for social media banners, these should be filled in appropriatly as they are your "business card" -->
<!-- Replace the content tag with appropriate information -->
<meta name="description" content="SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Recognition">
<meta property="og:title" content="SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Recognition"/>
<meta property="og:description" content="SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Recognition"/>
<meta property="og:url" content="https://github.com/NYU-DICE-Lab/SELECT"/>
<!-- Path to banner image, should be in the path listed below. Optimal dimenssions are 1200X630-->
<meta property="og:image" content="static/images/select.png" />
<meta property="og:image:width" content="1200"/>
<meta property="og:image:height" content="630"/>
<meta name="twitter:title" content="SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Recognition">
<meta name="twitter:description" content="SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Recognition">
<!-- Path to banner image, should be in the path listed below. Optimal dimenssions are 1200X600-->
<meta name="twitter:image" content="static/images/select.png">
<meta name="twitter:card" content="summary_large_image">
<!-- Keywords for your paper to be indexed by-->
<meta name="keywords" content="machine learning deep learning data curation data-centric artificial intelligence computer vision CLIP multimodal learning">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>🌋 SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Recognition</title>
<link rel="icon" type="image/x-icon" href="static/images/favicon.ico">
<link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro"
rel="stylesheet">
<link rel="stylesheet" href="static/css/bulma.min.css">
<link rel="stylesheet" href="static/css/bulma-carousel.min.css">
<link rel="stylesheet" href="static/css/bulma-slider.min.css">
<link rel="stylesheet" href="static/css/fontawesome.all.min.css">
<link rel="stylesheet"
href="https://cdn.jsdelivr.net/gh/jpswalsh/academicons@1/css/academicons.min.css">
<link rel="stylesheet" href="static/css/index.css">
<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
<script src="https://documentcloud.adobe.com/view-sdk/main.js"></script>
<script defer src="static/js/fontawesome.all.min.js"></script>
<script src="static/js/bulma-carousel.min.js"></script>
<script src="static/js/bulma-slider.min.js"></script>
<script src="static/js/index.js"></script>
</head>
<body>
<section class="hero">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-1 publication-title">SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Recognition</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="https://penfever.github.io/" target="_blank">Benjamin Feuer</a><sup>*</sup>,</span>
<span class="author-block">
<a href="https://chinmayhegde.github.io/lab/" target="_blank">Jiawei Xu</a><sup>*</sup>,</span>
<span class="author-block">
<a href="https://nivc.github.io/" target="_blank">Niv Cohen</a>,</span>
<span class="author-block">
<a href="https://chinmayhegde.github.io/lab/" target="_blank">Patrick Yubeaton</a>,</span>
<span class="author-block">
<a href="https://www.linkedin.com/in/mittalgovind" target="_blank">Govind Mittal</a>,</span>
<span class="author-block">
<a href="https://chinmayhegde.github.io/" target="_blank">Chinmay Hegde</a>
</span>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">New York University<br>NeurIPS 2024 (Datasets and Benchmarks)</span>
<span class="eql-cntrb"><small><br><sup>*</sup>Indicates Equal Contribution</small></span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<!-- Arxiv PDF link -->
<span class="link-block">
<a href="https://arxiv.org/pdf/2410.05057.pdf" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-file-pdf"></i>
</span>
<span>Paper</span>
</a>
</span>
<span class="link-block">
<a href="https://huggingface.co/collections/nyu-dice-lab/imagenet-666e885314f1c262fec84ef8" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fa-solid fa-database"></i>
</span>
<span>Data</span>
</a>
</span>
<span class="link-block">
<a href="https://huggingface.co/collections/nyu-dice-lab/select-baselines-666e8963b955b0e655b62d13" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fa-solid fa-database"></i>
</span>
<span>Models</span>
</a>
</span>
<!-- Github link -->
<span class="link-block">
<a href="https://github.com/jimmyxu123/SELECT" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span>
</a>
</span>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<!-- Teaser video-->
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<!--<video poster="" id="tree" autoplay controls muted loop height="100%">-->
<!-- Your video here -->
<!--<source src="static/videos/banner_video.mp4"
type="video/mp4">-->
<!--</video>-->
<!--<h2 class="subtitle has-text-centered">-->
<!--Aliquam vitae elit ullamcorper tellus egestas pellentesque. Ut lacus tellus, maximus vel lectus at, placerat pretium mi. Maecenas dignissim tincidunt vestibulum. Sed consequat hendrerit nisl ut maximus. -->
<!--</h2>-->
</div>
</div>
</section>
<!-- End teaser video -->
<!-- Paper abstract -->
<section class="section hero is-light">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
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.
<br> <br>
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, 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 it to inspect a fixed pretrained self-supervised representation.
<br> <br>
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 <a href="https://github.com/jimmyxu123/SELECT"> https://github.com/jimmyxu123/SELECT</a>.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- End paper abstract -->
<section class="section">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h5 class="subtitle is-5">October 8, 2024</h5>
<div class="content has-text-justified">
<p class="c18"><span class="c9">Introducing </span><strong>SELECT</strong><span class="c0">: A Large-Scale Benchmark for Data Curation in Image Classification</span>
</p>
<p class="c18"><span class="c9">Data curation is a critical yet often overlooked aspect of machine learning. The process of collecting, organizing, and preparing datasets significantly impacts model performance, but until now, there hasn't been a comprehensive way to evaluate different curation strategies. Enter SELECT, a new large-scale benchmark designed to systematically compare various data curation methods for image classification tasks.</span>
</p>
</div>
</div>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">What is SELECT?</h2>
<div class="content has-text-justified">
<p class="c18">
<span class="c0">SELECT (Systematic Evaluation of Large-scale Efficient Curation Techniques) is a benchmark that allows researchers to assess the effectiveness of different data curation strategies. It provides a standardized way to measure how well curated datasets perform across a range of metrics, including: </span>
</p>
<ol class="c28 lst-kix_st344of4rgcu-0 start" start="1">
<li class="c18 c29 li-bullet-0">
<span class="c0">Base accuracy on ImageNet validation set </span>
</li>
<li class="c18 c29 li-bullet-0"><span class="c0">Out-of-distribution (OOD) robustness </span></li>
<li class="c18 c29 li-bullet-0"><span class="c0">Performance on downstream tasks </span></li>
<li class="c18 c29 li-bullet-0"><span class="c0">Effectiveness for self-supervised learning </span></li>
</ol>
<p class="c10"><span class="c0"></span></p>
<p class="c18"><span class="c9">The benchmark also includes several analytical metrics to help understand dataset properties without requiring model training, such as class imbalance measures and image quality scores.</span></p>
<img src="static/images/select.jpg" alt="SELECT benchmark" />
</div>
</div>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Introducing ImageNet++</h2>
<div class="content has-text-justified">
<p class="c18">
<span class="c0">To establish baseline performance for different curation strategies, we created ImageNet++, the largest and most diverse set of ImageNet-1K training set variations to date. ImageNet++ consists of 5 new dataset "shifts" in addition to the original ImageNet-1K: </span>
</p>
<ol class="c28 lst-kix_st344of4rgcu-0 start" start="1">
<li class="c18 c29 li-bullet-0">
<span class="c0">OI1000: A subset of OpenImages dataset using crowdsourced labeling </span>
</li>
<li class="c18 c29 li-bullet-0"><span class="c0">LA1000 (img2img): Images from LAION dataset selected using embedding-based search </span></li>
<li class="c18 c29 li-bullet-0"><span class="c0">LA1000 (txt2img): Another LAION subset selected using text-based embedding search </span></li>
<li class="c18 c29 li-bullet-0"><span class="c0">SD1000 (img2img): Synthetic images generated from ImageNet using Stable Diffusion </span></li>
<li class="c18 c29 li-bullet-0"><span class="c0">SD1000 (txt2img): Synthetic images generated using class names as prompts </span></li>
</ol>
<p class="c10"><span class="c0"></span></p>
<p class="c18"><span class="c9">The benchmark also includes several analytical metrics to help understand dataset properties without requiring model training, such as class imbalance measures and image quality scores.</span></p>
<img src="static/images/imagenetpp.jpg" alt="The ImageNet++ distribution shifts" />
</div>
</div>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Key Findings</h2>
<div class="content has-text-justified">
<p class="c18">
<span class="c0">After extensive experimentation, training over 130 models on these datasets, we uncovered several important insights: </span>
</p>
<ol class="c28 lst-kix_st344of4rgcu-0 start" start="1">
<li class="c18 c29 li-bullet-0"><span class="c0"><strong>Expert curation still reigns supreme:</strong> Despite advances in AI and data collection methods, no reduced-cost strategy outperformed the original expert-curated ImageNet dataset across all metrics.</span></li>
<li class="c18 c29 li-bullet-0"><span class="c0"><strong>Embedding-based search shows promise:</strong> Among the reduced-cost methods, embedding-based search (used in LA1000 shifts) consistently outperformed synthetic data generation approaches.</span></li>
<li class="c18 c29 li-bullet-0"><span class="c0"><strong>Human curation isn't always best:</strong> Surprisingly, the crowdsourced OI1000 dataset often underperformed compared to automated methods like LA1000, likely due to greater label imbalance.</span></li>
<li class="c18 c29 li-bullet-0"><span class="c0"><strong>Bigger isn't always better:</strong> The smallest dataset, LA1000 (img2img), often outperformed larger datasets, highlighting the importance of curation quality over quantity.</span></li>
<li class="c18 c29 li-bullet-0"><span class="c0"><strong>Image-conditioned methods outperform text-based ones:</strong> Across different curation strategies, methods that used images as a starting point (img2img) generally performed better than those using only text descriptions (txt2img).</span></li>
</ol>
<img src="static/images/data-curation-perf.png" alt="Comparative performance of data curation methods, visualized in a radar plot" />
</div>
</div>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Implications and Future Work</h2>
<div class="content has-text-justified">
<p class="c18">
<span class="c0">The SELECT benchmark and the insights gained from ImageNet++ open up several important avenues for future research: </span>
</p>
<ol class="c28 lst-kix_st344of4rgcu-0 start" start="1">
<li class="c18 c29 li-bullet-0"><span class="c0"><strong>Improving reduced-cost curation:</strong> While no method matched expert curation, the strong performance of embedding-based search suggests promising directions for developing more efficient curation techniques.</span></li>
<li class="c18 c29 li-bullet-0"><span class="c0"><strong>Addressing class imbalance:</strong> The poor performance of the crowdsourced OI1000 dataset highlights the critical importance of maintaining class balance during data collection.</span></li>
<li class="c18 c29 li-bullet-0"><span class="c0"><strong>Developing better quality metrics:</strong> Current image and label quality metrics showed little correlation with actual model performance, indicating a need for more sophisticated evaluation methods.</span></li>
<li class="c18 c29 li-bullet-0"><span class="c0"><strong>Refining synthetic data generation:</strong> While synthetic data underperformed in this study, there's potential to improve these methods to better complement real-world datasets.</span></li>
</ol>
<p class="c18">
<span class="c0">We are very excited about the future of SELECT and actively would like to partner with researchers do develop new methods for data curation. If you’d like to contribute or have
any questions, <a href="mailto:[email protected]">please get in touch</a>. </span>
</p>
</div>
</div>
</div>
</div>
</section>
<!-- Citation -->
<div class="citation">
<h3>Citation</h3>
<pre><code>
Feuer, B., Xu, J., Cohen, N., Yubeaton, P., Mittal, G., & Hegde, C. (2024).
SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Classification.
arXiv preprint arXiv:2410.05057.
</code></pre>
<div class="citation-links">
<a href="https://arxiv.org/abs/2410.05057" target="_blank" rel="noopener noreferrer">arXiv</a>
<a href="https://arxiv.org/pdf/2410.05057.pdf" target="_blank" rel="noopener noreferrer">PDF</a>
<button class="bibtex-toggle">BibTeX</button>
</div>
<pre class="bibtex-content" style="display: none;"><code>
@misc{feuer2024selectlargescalebenchmarkdata,
title={SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Classification},
author={Benjamin Feuer and Jiawei Xu and Niv Cohen and Patrick Yubeaton and Govind Mittal and Chinmay Hegde},
year={2024},
eprint={2410.05057},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2410.05057},
}
</code></pre>
</div>
<style>
.citation {
background-color: #f5f5f5;
border: 1px solid #ddd;
border-radius: 4px;
padding: 15px;
margin: 20px 0;
}
.citation h3 {
margin-top: 0;
}
.citation pre {
background-color: #fff;
border: 1px solid #ddd;
border-radius: 4px;
padding: 10px;
white-space: pre-wrap;
word-wrap: break-word;
}
.citation-links {
margin-top: 10px;
}
.citation-links a, .citation-links button {
display: inline-block;
margin-right: 10px;
padding: 5px 10px;
background-color: #007bff;
color: white;
text-decoration: none;
border-radius: 4px;
border: none;
cursor: pointer;
}
.citation-links a:hover, .citation-links button:hover {
background-color: #0056b3;
}
</style>
<script>
document.querySelector('.bibtex-toggle').addEventListener('click', function() {
var bibtexContent = document.querySelector('.bibtex-content');
if (bibtexContent.style.display === 'none') {
bibtexContent.style.display = 'block';
this.textContent = 'Hide BibTeX';
} else {
bibtexContent.style.display = 'none';
this.textContent = 'BibTeX';
}
});
</script>
<!-- End citation -->
<footer class="footer">
<div class="container">
<div class="columns is-centered">
<div class="column is-8">
<div class="content">
<p>
This page was built using the <a href="https://github.com/eliahuhorwitz/Academic-project-page-template" target="_blank">Academic Project Page Template</a> which was adopted from the <a href="https://nerfies.github.io" target="_blank">Nerfies</a> project page.
You are free to borrow the of this website, we just ask that you link back to this page in the footer. <br> This website is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/" target="_blank">Creative
Commons Attribution-ShareAlike 4.0 International License</a>.
</p>
</div>
</div>
</div>
</div>
</footer>
<!-- Statcounter tracking code -->
<!-- You can add a tracker to track page visits by creating an account at statcounter.com -->
<!-- End of Statcounter Code -->
</body>
</html>