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..
Green Hierarchical Vision Transformer for Masked Image Modeling
Authors: Lang Huang, Shan You, Mingkai Zheng, Fei Wang, Chen Qian, Toshihiko Yamasaki
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on the Image Net-1K [60] (BSD 3-Clause License) image classification dataset and MS-COCO [47] (CC BY 4.0 License) object detection/instance segmentation dataset. |
| Researcher Affiliation | Collaboration | 1The University of Tokyo; 2Sense Time Research; 3The University of Sydney |
| Pseudocode | Yes | Algorithm 1 Optimal Grouping |
| Open Source Code | Yes | Corresponding author. Code and pre-trained models: https://github.com/Layne H/Green MIM. |
| Open Datasets | Yes | We conduct experiments on the Image Net-1K [60] (BSD 3-Clause License) image classification dataset and MS-COCO [47] (CC BY 4.0 License) object detection/instance segmentation dataset. |
| Dataset Splits | Yes | We fine-tune the pre-trained models on the Image Net-1K dataset and report the results on the validation set in Table 2. All models are fine-tuned on the MS-COCO [47] 2017 train split (~118k images) and finally evaluated on the val split (~5k images). |
| Hardware Specification | Yes | All the experiments of our method are performed on a single machine with eight 32G Tesla V100 GPUs |
| Software Dependencies | Yes | CUDA 10.1, Py Torch [54] 1.8 |
| Experiment Setup | Yes | The models are trained for 100/200/400/800 epochs with a total batch size of 2,048. We use the Adam W optimizer [41] with the cosine annealing schedule [50]. We set the base learning rate to 1.5e 4, the weight decay to 0.05, the hyper-parameters of Adam β1 = 0.9, β2 = 0.999, the number of warmup epochs to 40 with an initial base learning rate 1.5e 7. |