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..
R-FCN: Object Detection via Region-based Fully Convolutional Networks
Authors: Jifeng Dai, Yi Li, Kaiming He, Jian Sun
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show competitive results on the PASCAL VOC datasets (e.g., 83.6% m AP on the 2007 set) with the 101-layer Res Net. Meanwhile, our result is achieved at a test-time speed of 170ms per image, 2.5-20 faster than the Faster R-CNN counterpart. |
| Researcher Affiliation | Collaboration | Jifeng Dai Microsoft Research Asia Yi Li Tsinghua University Kaiming He Microsoft Research Jian Sun Microsoft Research This work was done when Yi Li was an intern at Microsoft Research. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is made publicly available at: https://github.com/daijifeng001/r-fcn. |
| Open Datasets | Yes | We train the models on the union set of VOC 2007 trainval and VOC 2012 trainval ( 07+12 ) following [7], and evaluate on VOC 2007 test set. Object detection accuracy is measured by mean Average Precision (m AP). ... Next we evaluate on the MS COCO dataset [14] that has 80 object categories. Our experiments involve the 80k train set, 40k val set, and 20k test-dev set. |
| Dataset Splits | Yes | We train the models on the union set of VOC 2007 trainval and VOC 2012 trainval ( 07+12 ) following [7]... Our experiments involve the 80k train set, 40k val set, and 20k test-dev set. |
| Hardware Specification | Yes | Timing is evaluated on a single Nvidia K40 GPU. |
| Software Dependencies | No | The paper mentions software like ResNet and FCNs, but does not provide specific version numbers for software dependencies needed to replicate the experiment. |
| Experiment Setup | Yes | We use a weight decay of 0.0005 and a momentum of 0.9. By default we use single-scale training: images are resized such that the scale (shorter side of image) is 600 pixels [7, 19]. Each GPU holds 1 image and selects B = 128 Ro Is for backprop. We train the model with 8 GPUs (so the effective mini-batch size is 8 ). We ο¬ne-tune R-FCN using a learning rate of 0.001 for 20k mini-batches and 0.0001 for 10k mini-batches on VOC. |