Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
MG-ViT: A Multi-Granularity Method for Compact and Efficient Vision Transformers
Authors: Yu Zhang, Yepeng Liu, Duoqian Miao, Qi Zhang, Yiwei Shi, Liang Hu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments Prove the effectiveness of the multigranularity strategy. For instance, on Image Net, without any loss of performance, MG-Vi T reduces 47% FLOPs of LV-Vi T-S and 56% FLOPs of Dei T-S. |
| Researcher Affiliation | Academia | Yu Zhang1 Yepeng Liu2 Duoqian Miao1 Qi Zhang1 Yiwei Shi3 Liang Hu1 1Tongji University 2University of Florida 3University of Bristol |
| Pseudocode | No | The paper contains architectural diagrams and mathematical formulas but does not include any explicit pseudocode blocks or sections labeled 'Algorithm'. |
| Open Source Code | No | The paper does not include any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We select LV-Vi T [17] and Dei T [15] to assess the performance of MG-Vi T on Image Net [18]. ...object detection and semantic segmentation on the MS-COCO [19] and ADE20K [20] datasets, respectively. |
| Dataset Splits | No | The paper mentions using standard datasets like ImageNet, MS-COCO, and ADE20K, which have predefined splits. However, it does not explicitly state the training/validation/test dataset splits by percentage, absolute sample counts, or specific split files. |
| Hardware Specification | Yes | All metrics are measured on a single NVIDIA RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions using the 'Adam W optimizer' but does not specify version numbers for other key software components or libraries such as Python, PyTorch/TensorFlow, or CUDA. |
| Experiment Setup | Yes | The resolution of input images in our experiments is 224 224. In SGIS, we split each image into 7 7 patches. rh and rt are set to 0.1 and 0.4, respectively. For Dei T-S, we inserted a total of three PPSM modules in the 3rd, 7th, and 10th layers. For conducting the training process, we set the batch size to 256 and use Adam W optimizer to train all models for 300 epochs. |