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Self-Supervised Image Classification archive 2025-07-28

ImageNet (finetuned) Benchmark (Self-Supervised Image Classification)

65 rows 64 with code listed 2 metrics Dataset page

The archive's category list for this table covers 11 areas (Adversarial, Audio, Computer Code, Computer Vision, Medical, Methodology, Miscellaneous, Music, Natural Language Processing, Reasoning, Speech), which is its placeholder rather than an assignment; no area is shown in the breadcrumb. archive 2025-07-28

This is the task of image classification using representations learnt with self-supervised learning. Self-supervised methods generally involve a pretext task that is solved to learn a good representation and a loss function to learn with. One example of a loss function is an autoencoder based loss where the goal is reconstruction of an image pixel-by-pixel. A more popular recent example is a contrastive loss, which measure the similarity of sample pairs in a representation space, and where there can be a varying target instead of a fixed target to reconstruct (as in the case of autoencoders).

A common evaluation protocol is to train a linear classifier on top of (frozen) representations learnt by self-supervised methods. The leaderboards for the linear evaluation protocol can be found below. In practice, it is more common to fine-tune features on a downstream task. An alternative evaluation protocol therefore uses semi-supervised learning and finetunes on a % of the labels. The leaderboards for the finetuning protocol can be accessed here.

You may want to read some blog posts before reading the papers and checking the leaderboards:

There is also Yann LeCun's talk at AAAI-20 which you can watch here (35:00+).

The archive carries no text for this table; the description above is the archive's text for the task Self-Supervised Image Classification. archive 2025-07-28

Over time archive 2025-07-28

The chart needs JavaScript; the table below carries every value.

Direction inferred from the metric name, not from the archive: Top 1 Accuracy (higher is better), Number of Params (lower is better). Points are placed at the row's paper date; 65 of 65 rows carry one.

Results archive 2025-07-28

Archive rows end at the archive snapshot, 2025-07-28: no result published after that date is in this table. Rank is the archive's row order at that snapshot; not re-ranked here. Metric values are the archive's strings. Column headers sort the table in your browser; each row keeps its archive rank.

Paper Code Ran Syntology Report
1 DINOv2 (ViT-g/14, 448) 88.9%1100M ✓ Paper Code 2023 21 of 46 ran · 25 unverified report
2 PercMAE (ViT-L, dVAE) 88.6%307M ✓ Paper Code 2022 linked, not harvested report
3 DINOv2 (ViT-g/14) 88.5%1100M ✓ Paper Code 2023 21 of 46 ran · 25 unverified report
4 PeCo(ViT-H/14, 448) 88.3%632M – Paper Code 2021 linked, not harvested report
5 PercMAE (ViT-L) 88.1%307M – Paper Code 2022 linked, not harvested report
6 dBOT (ViT-H/14) 88.0%632M – Paper Code 2022 5 of 13 ran · 8 unverified report
7 MAE (ViT-H/14, 448) 87.8%632M – Paper Code 2021 71 of 137 ran · 66 unverified report
8 iBOT(ViT-L/16, 512) 87.8%307M ✓ Paper Code 2021 0 of 1 ran · 1 unverified report
9 MAE + AugSub finetune (ViT-H/14) 87.2%632M – Paper Code 2023 linked, not harvested report
10 SimMIM (SwinV2-H, 512) 87.1%658M – Paper Code 2021 9 of 14 ran · 5 unverified report
11 MAE (ViT-H/14) 86.9% – Paper Code 2021 71 of 137 ran · 66 unverified report
12 iBOT(ViT-L/16) 86.6%307M ✓ Paper Code 2021 0 of 1 ran · 1 unverified report
13 TEC_MAE (ViT-L/16, 224) 86.5% – Paper Code 2022 6 of 8 ran · 2 unverified report
14 BEiT-L (ViT) 86.3%307M ✓ Paper Code 2021 6 of 11 ran · 5 unverified report
15 CAE (ViT-L/16) 86.3%307M – Paper Code 2022 linked, not harvested report
16 MIRL (ViT-B-48) 86.2%341M – Paper Code 2023 linked, not harvested report
17 MAE + AugSub finetune (ViT-L/16) 86.1%304M – Paper Code 2023 linked, not harvested report
18 SparK (ConvNeXt-Large, 384) 86.0%198M – Paper Code 2023 5 of 14 ran · 9 unverified report
19 BootMAE(ViT-L) 85.9%307M – Paper Code 2022 6 of 7 ran · 1 unverified report
20 SEER (Regnet10B) 85.8%10000M ✓ Paper Code 2022 linked, not harvested report
21 MaskFeat (ViT-L) 85.7%307M – Paper Code 2021 linked, not harvested report
22 OFA (Large) 85.6%473M – Paper Code 2022 1 of 1 ran · 0 unverified report
23 SparK (ConvNeXt-Large) 85.4%198M – Paper Code 2023 5 of 14 ran · 9 unverified report
24 SimMIM (Swin-L) 85.4%197M – Paper Code 2021 9 of 14 ran · 5 unverified report
25 Mugs (ViT-L/16) 85.2%307M – Paper Code 2022 4 of 21 ran · 17 unverified report
26 iBOT (ViT-L/16) 84.8%307M – Paper Code 2021 0 of 1 ran · 1 unverified report
27 MIRL (ViT-S-54) 84.8%96M – Paper Code 2023 linked, not harvested report
28 ConvNeXt-Base (SparK pre-training) 84.8%89M – Paper Code 2023 5 of 14 ran · 9 unverified report
29 BEiT-B (ViT) 84.6%86M ✓ Paper Code 2021 6 of 11 ran · 5 unverified report
30 A2MIM+ (ViT-B) 84.5% – Paper Code 2022 linked, not harvested report
31 iBOT (ViT-B/16) 84.4%85M – Paper Code 2021 0 of 1 ran · 1 unverified report
32 Mugs (ViT-B/16) 84.3%85M – Paper Code 2022 4 of 21 ran · 17 unverified report
33 SEER (RegNetY-256GF) 84.2%1.3B ✓ Paper Code 2021 linked, not harvested report
34 A2MIM (ViT-B) 84.2% – Paper Code 2022 linked, not harvested report
35 MoCo v3 (ViT-L/16) 84.1%304M – Paper Code 2021 2 of 2 ran · 0 unverified report
36 mc-BEiT (ViT-B/16) 84.1%86M – Paper Code 2022 3 of 5 ran · 2 unverified report
37 ConvNeXt-Small (SparK pre-training) 84.1%50M – Paper Code 2023 5 of 14 ran · 9 unverified report
38 SimMIM (Swin-B) 84.0%88M – Paper Code 2021 9 of 14 ran · 5 unverified report
39 iBOT (ViT-B/16) 84.0%85M – Paper Code 2021 0 of 1 ran · 1 unverified report
40 EsViT (Swin-B) 83.9%87M – Paper Code 2021 2 of 6 ran · 4 unverified report
41 MAE + AugSub finetune (ViT-B/16) 83.9%87M – Paper Code 2023 linked, not harvested report
42 SEER (RegNetY-128GF) 83.8%693M ✓ Paper Code 2021 linked, not harvested report
43 SimMIM (ViT-B/16) 83.8%85M – Paper Code 2021 9 of 14 ran · 5 unverified report
44 MoCo v3 (ViT-B/16) 83.2%86M – Paper Code 2021 2 of 2 ran · 0 unverified report
45 DAMA (ViT-B/16) 83.2% – Paper Code 2022 linked, not harvested report
46 SimCLRv2 (ResNet-152, 3×+SK) 83.1%795M – Paper Code 2020 0 of 6 ran · 6 unverified report
47 ResNet-200 (SparK pre-training) 83.1%65M – Paper Code 2023 5 of 14 ran · 9 unverified report
48 DINO (ViT-B/16) 82.8%85M – Paper Code 2021 5 of 20 ran · 15 unverified report
49 ResNet-152 (SparK pre-training) 82.7%60M – Paper Code 2023 5 of 14 ran · 9 unverified report
50 Mugs (ViT-S/16) 82.6%21M – Paper Code 2022 4 of 21 ran · 17 unverified report
51 A2MIM+ (ViT-S) 82.4% – Paper Code 2022 linked, not harvested report
52 ResNet-101 (SparK pre-training) 82.2%44M – Paper Code 2023 5 of 14 ran · 9 unverified report
53 A2MIM (ViT-S) 82.2% – Paper Code 2022 linked, not harvested report
54 SwAV (ResNeXt-101-32x16d) 82.0%193M ✓ Paper Code 2020 13 of 17 ran · 4 unverified report
55 ResNet-50 (SparK pre-training) 80.6%26M – Paper Code 2023 5 of 14 ran · 9 unverified report
56 A2MIM+ (ResNet-50 RSB-A2) 80.5% – Paper Code 2022 linked, not harvested report
57 A2MIM (ResNet-50 RSB-A2) 80.4% – Paper Code 2022 linked, not harvested report
58 A2MIM+ (ResNet-50 RSB-A3) 78.9% – Paper Code 2022 linked, not harvested report
59 A2MIM (ResNet-50 RSB-A3) 78.8% – Paper Code 2022 linked, not harvested report
60 DnC (Resnet-50) 78.2% – Paper – 2021 no code linked report
61 SwAV (Resnet-50) 77.8%182M ✓ Paper Code 2020 13 of 17 ran · 4 unverified report
62 MoCo (Resnet-50) 77.3% ✓ Paper Code 2019 26 of 42 ran · 16 unverified report
63 SimCLR (Resnet-50) 77.2% ✓ Paper Code 2020 79 of 137 ran · 58 unverified report
64 MoCo (Resnet-50) 77.0% – Paper Code 2019 26 of 42 ran · 16 unverified report
65 DeeperCluster (VGG16) 74.9%138M ✓ Paper Code 2019 linked, not harvested report

All 65 rows shown. 65 link to a paper page on this site; 15 are marked as using additional training data in the archive. No GitHub stars are tracked; "Code" is the first repository the archive lists for the row. The archive carries no row tags, review links or community-submitted rows for this table; none are shown. archive 2025-07-28

Syntology Ran reads "N of M ran · U unverified": of the M code samples Syntology harvested from repositories linked to that row's paper (joined by arXiv id), N executed on a synthesized input and the other U = M−N are unverified (harvested, no recorded run). It counts code from repositories linked to that row's paper, not this result: the row's number was not reproduced and nothing here is a correctness claim. The other cell texts mean no graph line for the row: "linked, not harvested" (the archive links code, Syntology has not harvested it), "no code linked" (no code link in the archive), "not matched" (the row's paper URL matched no paper on this site). 41 rows have a graph line, from 19 distinct papers; 35 rows (17 papers) have at least one sample that ran. Counting each paper once: Syntology ran 264 of 508 samples; 244 unverified. Separately, 183 of those 508 are pointer-only (licence): the site points at that code rather than redistributing it, a licence property recorded for ran and unverified samples alike; each cell's tooltip carries the row's own pointer-only count. Read from the graph 2026-09-24. Per-sample status is on the paper page.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections