Aligning Pretraining for Detection via Object-Level Contrastive Learning
Authors: Fangyun Wei, Yue Gao, Zhirong Wu, Han Hu, Stephen Lin
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 {fawe, yuegao, wuzhiron, hanhu, stevelin}@microsoft.com |
| 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... |