Papers › Identity Mappings in Deep Residual Networks
Identity Mappings in Deep Residual Networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep residual networks have emerged as a family of extremely deep architectures showing compelling accuracy and nice convergence behaviors. In this paper, we analyze the propagation formulations behind the residual building blocks, which suggest that the forward and backward signals can be directly propagated from one block to any other block, when using identity mappings as the skip connections and after-addition activation. A series of ablation experiments support the importance of these identity mappings. This motivates us to propose a new residual unit, which makes training easier and improves generalization. We report improved results using a 1001-layer ResNet on CIFAR-10 (4.62% error) and CIFAR-100, and a 200-layer ResNet on ImageNet. Code is available at: https://github.com/KaimingHe/resnet-1k-layers
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Code
Syntology Ran 3 of 25 code samples harvested from 5 repositories linked to this paper; 22 have no recorded run. Of those that ran: 1 ran · honoured contract; 2 ran · our draft was wrong.
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54 repositories listed; official and paper-mentioned ones first.
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Code Syntology ran Syntology
25 samples harvested; 3 ran; 1 honoured the contract we drafted; 22 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | CIFAR-10 | ResNet-1001 | Percentage correct | 95.4 | #135 of 265 | Archive leaderboard | report |
| Image Classification | CIFAR-100 | ResNet-1001 | Percentage correct | 77.3 | #143 of 211 | Archive leaderboard | report |
| Image Classification | ImageNet | ResNet-200 | Top 1 Accuracy | 79.9% | #728 of 1060 | Archive leaderboard | report |
| Image Classification | Kuzushiji-MNIST | PreActResNet-18 | Accuracy | 97.82 | #20 of 26 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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