Ping Luo, Enze Xie, Shoufa Chen, Chongjian Ge, Runjian Chen, Ding Liang, 𝑆 𝐻, 𝑆
We lifted 15 functions out of this paper's own repositories and ran 9 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| ShoufaChen/CycleMLP | canonical | 1 of 4 |
| Ahmad-Omar-Ahsan/CycleMLP | — | 6 of 9 |
| revsic/tf-mlptts | pwc_unofficial | 1 of 1 |
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| CycleFC | Ran | Ahmad-Omar-Ahsan/CycleMLP/models/cycle_mlp.py code served (permissive licence) · get_code("6ba716e31944fe22") |
| Cycle_Block | Ran | Ahmad-Omar-Ahsan/CycleMLP/models/cycle_mlp.py code served (permissive licence) · get_code("a7533815a60a5ff4") |
| MLP | Ran | Ahmad-Omar-Ahsan/CycleMLP/models/cycle_mlp.py code served (permissive licence) · get_code("65aa6021f94c1bd7") |
| MLP_Block | Ran | Ahmad-Omar-Ahsan/CycleMLP/models/cycle_mlp.py code served (permissive licence) · get_code("9f01e73d5c35c5b6") |
| OverlapPatchEmbed | Ran | Ahmad-Omar-Ahsan/CycleMLP/models/cycle_mlp.py code served (permissive licence) · get_code("93aac00d7f2d7fba") |
| Spatial_Proj | Ran | Ahmad-Omar-Ahsan/CycleMLP/models/cycle_mlp.py code served (permissive licence) · get_code("3e5be041fd16d9c9") |
| load_state | Ran | revsic/tf-mlptts/config.py code served (permissive licence) · get_code("a93e65374343828b") |
| pair | Ran | this paper's copy was not recorded; identical code first harvested from LINs-lab/DynMoE pointer only · get_code("6ba8cee9f5daea41") |
| pil_loader | Ran | ShoufaChen/CycleMLP/mcloader/mcloader.py code served (permissive licence) · get_code("6cc5ed3b37c68d9d") |
| CycleMLP | Not yet run | Ahmad-Omar-Ahsan/CycleMLP/models/cycle_mlp.py code served (permissive licence) · get_code("a58aae0835a61b41") |
| CycleMLP_Block | Not yet run | Ahmad-Omar-Ahsan/CycleMLP/models/cycle_mlp.py code served (permissive licence) · get_code("e8703140c0494e82") |
| Stage | Not yet run | Ahmad-Omar-Ahsan/CycleMLP/models/cycle_mlp.py code served (permissive licence) · get_code("20a854cf90927d91") |
| basic_blocks | Not yet run | ShoufaChen/CycleMLP/cycle_mlp.py code served (permissive licence) · get_code("f679822e5f5f467c") |
| build_transform | Not yet run | ShoufaChen/CycleMLP/datasets.py code served (permissive licence) · get_code("3bc74137ab36aa79") |
| sfc_flop_jit | Not yet run | ShoufaChen/CycleMLP/utils.py code served (permissive licence) · get_code("2a2617c52f0e6b56") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
This paper presents a simple MLP-like architecture, CycleMLP, which is a versatile backbone for visual recognition and dense predictions. As compared to modern MLP architectures, e.g. , MLP-Mixer (Tolstikhin et al., 2021), ResMLP (Touvron et al., 2021a), and gMLP (Liu et al., 2021a), whose architectures are correlated to image size and thus are infeasible in object detection and segmentation, CycleMLP has two advantages compared to modern approaches. (1) It can cope with various image sizes. (2) It achieves linear computational complexity to image size by using local windows. In contrast, previous MLPs have O(N 2 ) computations due to fully spatial connections. We build a family of models which surpass existing MLPs and even state-of-the-art Transformer-based models, e.g. Swin Transformer (Liu et al., 2021b), while using fewer parameters and FLOPs. We expand the MLP-like models' applicability, making them a versatile backbone for dense prediction tasks. CycleMLP achieves competitive results on object detection, instance segmentation, and semantic segmentation. In particular, CycleMLP-Tiny outperforms Swin-Tiny by 1.3% mIoU on ADE20K dataset with fewer FLOPs. Moreover, CycleMLP also shows excellent zero-shot robustness on ImageNet-C dataset. Code is available at https: //github.com/ShoufaChen/CycleMLP.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2107.10224")
get_code_for_paper("2107.10224")
have("2107.10224")
Connect an agent — have() is free.