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..
Auto-scaling Vision Transformers without Training
Authors: Wuyang Chen, Wei Huang, Xianzhi Du, Xiaodan Song, Zhangyang Wang, Denny Zhou
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our As-Vi T achieves strong performance on classification (83.5% top-1 on Image Net-1k) and detection (52.7% m AP on COCO). and Table 5 demonstrates comparisons of our As-Vi T to other models. Compared to the previous both Transformer-based and CNNbased architectures, As-Vi T achieves stateof-the-art performance with a comparable number of parameters and FLOPs. |
| Researcher Affiliation | Collaboration | 1University of Texas, Austin 2University of Technology Sydney 3Google EMAIL EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1: Training-free Vi T Topology Search. and Algorithm 2: Training-free Auto-Scaling Vi Ts. |
| Open Source Code | Yes | Our code is available at https://github.com/VITA-Group/As Vi T. |
| Open Datasets | Yes | Our As-Vi T achieves strong performance on classification (83.5% top-1 on Image Net-1k) and detection (52.7% m AP on COCO). and We benchmark our As-Vi T on Image Net-1k (Deng et al., 2009). Object detection is conducted on COCO 2017... |
| Dataset Splits | Yes | Object detection is conducted on COCO 2017 that contains 118,000 training and 5000 validation images. |
| Hardware Specification | Yes | the end-to-end model design and scaling process costs only 12 hours on one V100 GPU. and We set the default image size as 224 224, and use Adam W (Loshchilov & Hutter, 2017) as the optimizer with cosine learning rate decay (Loshchilov & Hutter, 2016). A batch size of 1024, an initial learning rate of 0.001, and a weight decay of 0.05 are adopted. |
| Software Dependencies | No | We use Tensorflow and Keras for training implementations and conduct all training on TPUs. The paper mentions software by name but does not provide specific version numbers. |
| Experiment Setup | Yes | We set the default image size as 224 224, and use Adam W (Loshchilov & Hutter, 2017) as the optimizer with cosine learning rate decay (Loshchilov & Hutter, 2016). A batch size of 1024, an initial learning rate of 0.001, and a weight decay of 0.05 are adopted. |