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..
DetNAS: Backbone Search for Object Detection
Authors: Yukang Chen, Tong Yang, Xiangyu Zhang, GAOFENG MENG, Xinyu Xiao, Jian Sun
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, we show the effectiveness of Det NAS on various detectors, for instance, one-stage Retina Net and the two-stage FPN. We empirically find that networks searched on object detection shows consistent superiority compared to those searched on Image Net classification. The resulting architecture achieves superior performance than hand-crafted networks on COCO with much less FLOPs complexity. |
| Researcher Affiliation | Collaboration | 1National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2Megvii Technology EMAIL EMAIL |
| Pseudocode | Yes | We formulate the supernet training process in Algorithm 1 in the supplementary material. We formulate this process as Algorithm 2 in the supplementary material. |
| Open Source Code | Yes | Code and models have been made available at: https://github.com/megvii-model/Det NAS. |
| Open Datasets | Yes | For Image Net classification dataset, we use the commonly used 1.28M training images for supernet pre-training. We train on 8 GPUs with a total of 16 images per minibatch for 90k iterations on COCO and 22.5k iterations on VOC. |
| Dataset Splits | Yes | We split the detection datasets into a training set for supernet fine-tuning, a validation set for architecture search, and a test set for final evaluation. For VOC, the validation set contains 5k images randomly selected from trainval2007 + trainval2012 and the remains for supernet fine-tuning. For COCO, the validation set contains 5k images randomly selected from trainval35k [13] and the remains for supernet fine-tuning. |
| Hardware Specification | Yes | For the small search space, GPUs are GTX 1080Ti . For the large search space, GPUs are Tesla V100. |
| Software Dependencies | No | The paper mentions 'Detectron [6]' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For Image Net classification dataset, we use the commonly used 1.28M training images for supernet pre-training. To train the one-shot supernet backbone on Image Net, we use a batch size of 1024 on 8 GPUs for 300k iterations. We set the initial learning rate to be 0.5 and decrease it linearly to 0. The momentum is 0.9 and weight decay is 4 10 5. We train on 8 GPUs with a total of 16 images per minibatch for 90k iterations on COCO and 22.5k iterations on VOC. The initial learning rate is 0.02 which is divided by 10 at {60k, 80k} iterations on COCO and {15k, 20k} iterations on VOC. We use weight decay of 1 10 4 and momentum of 0.9. |