Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
ResT: An Efficient Transformer for Visual Recognition
Authors: Qinglong Zhang, Yu-Bin Yang
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 18,224 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,419 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We comprehensively validate Res T on image classification and downstream tasks. Experimental results show that the proposed Res T can outperform the recently state-of-the-art backbones by a large margin, demonstrating the potential of Res T as strong backbones. |
| Researcher Affiliation | Academia | Qing-Long Zhang, Yu-Bin Yang State Key Laboratory for Novel Software Technology Nanjing University, Nanjing 21023, China EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and models will be made publicly available at https://github.com/wofmanaf/Res T. |
| Open Datasets | Yes | We comprehensively validate the effectiveness of the proposed Res T on the commonly used benchmarks, including image classification on Image Net-1k and downstream tasks, such as object detection, and instance segmentation on MS COCO2017. |
| Dataset Splits | Yes | Image Net-1k, which contains 1.28M training images and 50k validation images from 1,000 classes." and "MS COCO2017, which contains 118k training, 5k validation, and 20k test-dev images. |
| Hardware Specification | Yes | Throughput (images / s) is measured on a single V100 GPU, following [26]." and "A batch size of 2048 (using 8 GPUs with 256 images per GPU), an initial learning rate of 5e-4, a weight decay of 0.05, and gradient clipping with a max norm of 5 are used. |
| Software Dependencies | No | The paper mentions software components and techniques such as Adam W optimizer, GELU activation, Instance Normalization, Batch Normalization, ReLU activation, and Layer Normalization, but it does not provide specific version numbers for any of these software dependencies or frameworks (e.g., PyTorch, TensorFlow). |
| Experiment Setup | Yes | For image classification, we employ the Adam W [20] optimizer for 300 epochs using a cosine decay learning rate scheduler and 5 epochs of linear warm-up. A batch size of 2048 (using 8 GPUs with 256 images per GPU), an initial learning rate of 5e-4, a weight decay of 0.05, and gradient clipping with a max norm of 5 are used. |