Hotels-50K: A Global Hotel Recognition Dataset
Authors: Abby Stylianou, Hong Xuan, Maya Shende, Jonathan Brandt, Richard Souvenir, Robert Pless726-733
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present a baseline approach based on a standard network architecture and a collection of data-augmentation approaches tuned to this problem domain.In order to set the baseline for performance on the Hotels50K dataset, we compare two off-the-shelf pre-trained networks trained for object and scene recognition to a method using data and augmentation schemes specifically tailored to hotel recognition.Table 4 shows the results for the ablation experiment. |
| Researcher Affiliation | Collaboration | 1George Washington University 2Adobe Research 3Temple University |
| Pseudocode | No | The paper describes the methods verbally (e.g., "Our method uses the Hotels-50K training set as input to fine tune a Resnet-50 model...") and visually (Figure 8 for data augmentation), but it does not present any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The Hotels-50K dataset, pre-trained models and code to replicate our baseline approaches can be found at https://github.com/GWUvision/Hotels-50K. |
| Open Datasets | Yes | The Hotels-50K dataset, pre-trained models and code to replicate our baseline approaches can be found at https://github.com/GWUvision/Hotels-50K. |
| Dataset Splits | No | The paper mentions "The test set consists of 17,954 images from the Traffick Cam mobile application from 5,000 different hotels, which are a subset of those found in the training set." and "Training parameters were selected using cross-validation." However, it does not explicitly detail the size or composition of a distinct validation set or the specific splits for the cross-validation used for parameter selection. |
| Hardware Specification | No | The paper describes the models and training process but does not provide any specific hardware details such as GPU models, CPU types, or memory used for the experiments. |
| Software Dependencies | No | The paper mentions using "Resnet-50 model, pre-trained for Image Net" and a "VGG model trained on the Places365 dataset", and references a triplet loss paper. It also mentions MS-COCO for masks. However, it does not provide specific version numbers for any software libraries, frameworks, or dependencies used (e.g., Python, PyTorch/TensorFlow, CUDA versions). |
| Experiment Setup | Yes | The final model was fine-tuned for 65,000 iterations with 120 images per batch.Training parameters were selected using cross-validation. |