Bootstrap Your Object Detector via Mixed Training
Authors: Mengde Xu, Zheng Zhang, Fangyun Wei, Yutong Lin, Yue Cao, Stephen Lin, Han Hu, Xiang Bai
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We introduce Mix Training, a new training paradigm for object detection that can improve the performance of existing detectors for free. ... In particular, the performance of Faster R-CNN [24] with a Res Net-50 [13] backbone is improved from 41.7 m AP to 44.0 m AP, and the accuracy of Cascade-RCNN [1] with a Swin-Small [22] backbone is raised from 50.9 m AP to 52.8 m AP. ... 4 Experiments |
| Researcher Affiliation | Collaboration | 1Huazhong University of Science and Technology 2Xi an Jiaotong University 3Microsoft Research Asia |
| Pseudocode | No | The paper includes mathematical equations (1) and (2) but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | Yes | We validate our method on the COCO2017 dataset [20]... [20] Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L. (2014). Microsoft coco: Common objects in context. In ECCV. |
| Dataset Splits | Yes | We validate our method on the COCO2017 dataset [20], which contains 80 object categories, 118k images for training (train2017), 5k images (minival) for validation and 20k images for testing (test-dev). |
| Hardware Specification | Yes | All the models run on 32 Nvidia V100. |
| Software Dependencies | No | The paper mentions optimizers like SGD and AdamW but does not provide specific version numbers for any software libraries, frameworks, or programming languages used (e.g., PyTorch, Python version). |
| Experiment Setup | Yes | In our experiments, two different data augmentation strategies are adopted: normal augmentation and strong augmentation. We summarize the details in Table 1. ... For updating the EMA model that predicts pseudo boxes, the momentum coefficient is set to 0.999. ... g is considered an easy target if its score is greater than a threshold (0.9 by default). ... For other training settings and hyper-parameters for each detector, we following the default settings if not otherwise specified. |