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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Distilling Image Classifiers in Object Detectors
Authors: Shuxuan Guo, Jose M. Alvarez, Mathieu Salzmann
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 18,769 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,624 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on several detectors with different backbones demonstrate the effectiveness of our approach, allowing us to outperform the state-of-the-art detector-to-detector distillation methods. |
| Researcher Affiliation | Collaboration | Shuxuan Guo1, 2 Jose M. Alvarez2 Mathieu Salzmann1 1CVLab, EPFL, Lausanne 1015, Switzerland 2NVIDIA, Santa Clara, CA 95051, USA |
| Pseudocode | No | The paper describes the proposed algorithms mathematically and textually but does not include formal pseudocode blocks or algorithm listings. |
| Open Source Code | Yes | Our code is avlaible at: https://github.com/NVlabs/DICOD. |
| Open Datasets | Yes | All models are trained and evaluated on MS COCO2017 [23], which contains over 118k images for training and 5k images for validation (minival) depicting 80 foreground object classes. |
| Dataset Splits | Yes | All models are trained and evaluated on MS COCO2017 [23], which contains over 118k images for training and 5k images for validation (minival) depicting 80 foreground object classes. |
| Hardware Specification | Yes | All object detectors are trained in their default settings on Tesla V100 GPUs. |
| Software Dependencies | No | Our implementation is based on MMDetection [6] with Pytorch [29]. While software is mentioned, specific version numbers for MMDetection or PyTorch are not provided. |
| Experiment Setup | Yes | All object detectors are trained in their default settings on Tesla V100 GPUs. The SSDs follows the basic training recipe in MMDetection [6]. The lightweight Faster RCNNs are trained with a 1 training schedule for 12 epochs. ... We use a Res Net50 with input resolution 112 112 as classification teacher for all student detectors. ... the Faster RCNN-R50 and Retina Net-R50 are trained with a 2 schedule for 24 epochs. ... Ablation studies for hyperparameters are also provided. |