PrObeD: Proactive Object Detection Wrapper
Authors: Vishal Asnani, Abhinav Kumar, Suya You, Xiaoming Liu
NeurIPS 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on MS-COCO, CAMO, COD10K, and NC4K datasets show improvement over different detectors after applying Pr Obe D. |
| Researcher Affiliation | Collaboration | Vishal Asnani Michigan State University EMAIL Abhinav Kumar Michigan State University EMAIL Suya You DEVCOM Army Research Laboratory EMAIL Xiaoming Liu Michigan State University EMAIL |
| Pseudocode | No | The paper describes the stages and architecture of Pr Obe D but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | Our models/codes are available at https: //github.com/vishal3477/Proactive-Object-Detection. |
| Open Datasets | Yes | Our experiments use the MS-COCO 2017 [44] dataset for GOD, while we use CAMO [39], COD10K [17], and NC4K [47] datasets for COD. |
| Dataset Splits | Yes | MS-COCO 2017 Val Split [44]: It includes 118,287 images for training and 5K for testing. COD10K Val Split [17]: It includes 4,046 camouflaged images for training and 2,026 for testing. CAMO Val Split [39]: It includes 1K camouflaged images for training and 250 for testing. NC4K Val [47]: It includes 4,121 NC4K images. |
| Hardware Specification | Yes | averaged across 1, 000 images, on a NVIDIA V 100 GPU. |
| Software Dependencies | No | The paper mentions using 'PyTorch [51]' but does not provide a specific version number for PyTorch or any other software component used in the experiments. |
| Experiment Setup | No | The paper describes the general training process (fine-tuning, end-to-end training, loss functions) but does not provide specific hyperparameters like learning rate, batch size, or number of epochs in the main text. |