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..
Learning to Segment Object Candidates
Authors: Pedro O. O. Pinheiro, Ronan Collobert, Piotr Dollar
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We train on MS COCO [21] and evaluate the model on two object detection datasets, PASCAL VOC [7] and MS COCO.In this section, we evaluate the performance of our approach on the PASCAL VOC 2007 test set [7] and on the ๏ฌrst 5000 images of the MS COCO 2014 validation set [21]. |
| Researcher Affiliation | Collaboration | Pedro O. Pinheiro Ronan Collobert Piotr Doll ar EMAIL EMAIL EMAIL Facebook AI Research Pedro O. Pinheiro is with the Idiap Research Institute in Martigny, Switzerland and Ecole Polytechnique F ed erale de Lausanne (EPFL) in Lausanne, Switzerland. This work was done during an internship at FAIR. |
| Pseudocode | No | The paper describes the network architecture and procedures in text and with diagrams (Figure 1), but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | All the experiments were conducted using Torch71. 1http://torch.ch. This link refers to the Torch7 framework used, not the specific source code for the methodology described in the paper. There is no explicit statement about releasing their code. |
| Open Datasets | Yes | We train on MS COCO [21] and evaluate the model on two object detection datasets, PASCAL VOC [7] and MS COCO. The references [21] and [7] provide citations to these publicly available datasets. |
| Dataset Splits | Yes | Our model is trained on the COCO training set which contains about 80,000 images and a total of nearly 500,000 segmented objects. and Design architecture and hyper-parameters were chosen using a subset of the MS COCO validation data [21] (non-overlapping with the data we used for evaluation). and We evaluate the performance of our approach on the PASCAL VOC 2007 test set [7] and on the ๏ฌrst 5000 images of the MS COCO 2014 validation set [21]. |
| Hardware Specification | Yes | Our model takes around 5 days to train on a Nvidia Tesla K40m. |
| Software Dependencies | No | All the experiments were conducted using Torch71. 1http://torch.ch. This mentions Torch7, but does not provide a specific version number for Torch or other software dependencies with their versions. |
| Experiment Setup | Yes | We considered a learning rate of .001. We trained our model using stochastic gradient descent with a batch size of 32 examples, momentum of .9, and weight decay of .00005. and Aside from the pre-trained VGG features, weights are initialized randomly from a uniform distribution. and Both of these layers are followed by Re LU non-linearity and a dropout [28] procedure with a rate of 0.5. and To binarize predicted masks we simply threshold the continuous output (using a threshold of .1 for PASCAL and .2 for COCO). |