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..
Sequencer: Deep LSTM for Image Classification
Authors: Yuki Tatsunami, Masato Taki
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we compare Sequencers with previous studies on the IN-1K benchmark [39]. We also carry out ablation studies, transfer learning studies, and analysis of the results to demonstrate the effectiveness of Sequencers. |
| Researcher Affiliation | Collaboration | Yuki Tatsunami1,2 Masato Taki1 1Rikkyo University, Tokyo, Japan 2Any Tech Co., Ltd., Tokyo, Japan |
| Pseudocode | Yes | The Py Torch-like pseudocode is shown in Appendix B.1. |
| Open Source Code | Yes | Our source code is available at https://github.com/okojoalg/sequencer. |
| Open Datasets | Yes | We utilize IN-1K [39], which has 1000 classes and contains 1,281,167 training images and 50,000 validation images. |
| Dataset Splits | Yes | We utilize IN-1K [39], which has 1000 classes and contains 1,281,167 training images and 50,000 validation images. |
| Hardware Specification | Yes | Training and inference throughput and their peak memory were measured with 16 images per batch on a single V100 GPU. |
| Software Dependencies | No | The paper mentions using 'Py Torch [56] and timm [80] library' but does not specify the version numbers for these software dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | We adopt Adam W optimizer [50]. Following the previous study [72], we adopt the base learning rate batch size 512 5 10^4. The batch sizes for Sequencer2D-S, Sequencer2D-M, and Sequencer2D-L are 2048, 1536, and 1024, respectively. As a regularization method, stochastic depth [30] and label smoothing [66] are employed. As data augmentation methods, mixup [87], cutout [12], cutmix [85], random erasing [88], and randaugment [11] are applied. |