Sequencer: Deep LSTM for Image Classification
Authors: Yuki Tatsunami, Masato Taki
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | 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. |