Learning to Segment Object Candidates
Authors: Pedro O. O. Pinheiro, Ronan Collobert, Piotr Dollar
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | 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 first 5000 images of the MS COCO 2014 validation set [21]. |
| Researcher Affiliation | Collaboration | Pedro O. Pinheiro Ronan Collobert Piotr Doll ar pedro@opinheiro.com locronan@fb.com pdollar@fb.com 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 first 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). |