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..
BEiT: BERT Pre-Training of Image Transformers
Authors: Hangbo Bao, Li Dong, Songhao Piao, Furu Wei
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on image classification and semantic segmentation show that our model achieves competitive results with previous pre-training methods. |
| Researcher Affiliation | Collaboration | Hangbo Bao , Li Dong , Songhao Piao , Furu Wei Harbin Institute of Technology Microsoft Research |
| Pseudocode | Yes | Algorithm 1 Blockwise Masking |
| Open Source Code | Yes | https://github.com/microsoft/unilm |
| Open Datasets | Yes | We pretrain BEIT on the training set of Image Net-1K (Russakovsky et al., 2015), which contains about 1.2M images. |
| Dataset Splits | No | The paper mentions using 'training set of Image Net-1K' and evaluating on 'ILSVRC-2012 Image Net dataset' but does not provide specific training/validation/test split percentages or sample counts. |
| Hardware Specification | Yes | The 500k training steps take about five days using 16 Nvidia Telsa V100 32GB GPU cards. |
| Software Dependencies | No | The paper mentions optimizers (Adam) and other models (SETR-PUP) by reference, but does not specify the versions of software libraries or frameworks used (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | The paper includes detailed hyperparameters in '2.5 PRE-TRAINING SETUP', 'G HYPERPARAMETERS FOR PRE-TRAINING (Table 12)', 'H HYPERPARAMETERS FOR IMAGE CLASSIFICATION FINE-TUNING (Table 13)', and 'I HYPERPARAMETERS FOR ADE20K SEMANTIC SEGMENTATION FINE-TUNING (Table 14)' sections, covering learning rates, batch sizes, optimizers, and other settings. |