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..
Aligning Pretraining for Detection via Object-Level Contrastive Learning
Authors: Fangyun Wei, Yue Gao, Zhirong Wu, Han Hu, Stephen Lin
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, the proposed So Co achieves state-of-the-art transfer performance from Image Net to COCO. |
| Researcher Affiliation | Industry | Fangyun Wei Yue Gao Zhirong Wu Han Hu Stephen Lin Microsoft Research Asia EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/hologerry/SoCo. |
| Open Datasets | Yes | We adopt the widely used Image Net [1] which consists of 1.28 million images for self-supervised pretraining. COCO [42] and Pascal VOC [43] datasets are used for transfer learning. |
| Dataset Splits | Yes | We use the COCO train2017 set which contains 118k images with bounding box and instance segmentation annotations in 80 object categories. Transfer performance is evaluated on the COCO val2017 set. |
| Hardware Specification | Yes | The total batch size is set to 2048 over 16 Nvidia V100 GPUs. |
| Software Dependencies | No | The paper mentions software like 'Detectron2 [44] is used as the code base.' and 'We use MMDetection [49] as code base' but does not specify version numbers for these or any other software components. |
| Experiment Setup | Yes | We use a 100-epoch training schedule... We use the LARS optimizer [39] with a cosine decay learning rate schedule [40] and a warm-up period of 10 epochs. The base learning rate lrbase is set to 1.0... The weight decay is set to 1.0 e 5. The total batch size is set to 2048... |