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..
Hotels-50K: A Global Hotel Recognition Dataset
Authors: Abby Stylianou, Hong Xuan, Maya Shende, Jonathan Brandt, Richard Souvenir, Robert Pless726-733
AAAI 2019 | Venue PDF | 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. |