Efficient Modulation for Vision Networks
Authors: Xu Ma, Xiyang Dai, Jianwei Yang, Bin Xiao, Yinpeng Chen, Yun Fu, Lu Yuan
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we validate our Efficient Mod on four tasks: image classification on Image Net1K (Deng et al., 2009), object detection and instance segmentation on MS COCO (Lin et al., 2014), and semantic segmentation on ADE20K (Zhou et al., 2017). |
| Researcher Affiliation | Collaboration | Xu Ma1, Xiyang Dai2, Jianwei Yang2, Bin Xiao2, Yinpeng Chen2, Yun Fu1, Lu Yuan2 1Northeastern University 2Microsoft |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled 'Pseudocode' or 'Algorithm'. |
| Open Source Code | Yes | Code and checkpoints are available at https://github.com/ma-xu/Efficient Mod. |
| Open Datasets | Yes | We validate our Efficient Mod on four tasks: image classification on Image Net1K (Deng et al., 2009), object detection and instance segmentation on MS COCO (Lin et al., 2014), and semantic segmentation on ADE20K (Zhou et al., 2017). |
| Dataset Splits | Yes | We evaluate the classification performance of Efficient Mod networks on Image Net-1K. Our training recipe follows the standard practice in Dei T (Touvron et al., 2021a), details can be found in Appendix Sec. 5. |
| Hardware Specification | Yes | GPU: We chose the P100 GPU for our latency evaluation... CPU: Some models may operate with unpredictable latency on different types of hardware... We also provide all models measured latency on the Intel(R) Xeon(R) CPU E5-2680 CPU for a full comparison. |
| Software Dependencies | No | The paper states 'We implement all networks in Py Torch' but does not specify version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | The detailed training hyper parameters are presented in Table 5. (Table 5 is actually Figure 5 in the paper PDF). The table includes: Batch size 256, Optimizer Adam W, Weight decay 0.05, Learning rate 4e-3, Epochs 300, Warmup epochs 5, Hflip 0.5, Color-jitter 0.4, Mixup 0.8, Cutmix 1.0, Label smoothing 0.1, Layer Scale 1e-4, Drop path {0., 0., 0.02}. |