Tootfinder

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data
Akram Zaytar, Caleb Robinson, Girmaw Abebe Tadesse, Tammy Glazer, Gilles Hacheme, Anthony Ortiz, Rahul M Dodhia, Juan M Lavista Ferres
https://arxiv.org/abs/2506.06235

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data
Training deep learning models on petabyte-scale Earth observation (EO) data requires separating compute resources from data storage. However, standard PyTorch data loaders cannot keep modern GPUs utilized when streaming GeoTIFF files directly from cloud storage. In this work, we benchmark GeoTIFF loading throughput from both cloud object storage and local SSD, systematically testing different loader configurations and data parameters. We focus on tile-aligned reads and worker thread pools, usin…

Revisiting Reliability in the Reasoning-based Pose Estimation Benchmark
Junsu Kim, Naeun Kim, Jaeho Lee, Incheol Park, Dongyoon Han, Seungryul Baek
https://arxiv.org/abs/2507.13314

Revisiting Reliability in the Reasoning-based Pose Estimation Benchmark
The reasoning-based pose estimation (RPE) benchmark has emerged as a widely adopted evaluation standard for pose-aware multimodal large language models (MLLMs). Despite its significance, we identified critical reproducibility and benchmark-quality issues that hinder fair and consistent quantitative evaluations. Most notably, the benchmark utilizes different image indices from those of the original 3DPW dataset, forcing researchers into tedious and error-prone manual matching processes to obtain…