# Object Detection Training on O365 + GoldG Source: [Object Detection Training on O365 + GoldG]() Collection index: [llms.txt]() Data updated: 2026-02-26T06:07:34.114Z Evaluation of the computational efficiency required for training an Object Detection model on the combined O365 and GoldG datasets, reporting metrics like training time and frames per second (FPS). A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. | Scope | Recorded value | | --- | --- | | Task | Object Detection Training | | Dataset (including any stated split) | O365 + GoldG | | Metric | Training FPS | | Unit | SCALAR | | Direction | Higher is better | | Results loaded | 1 of 1 | | Coverage | All results currently returned for this benchmark | Evaluation settings may differ between results. The original papers and recorded configurations are needed to establish comparability. A shared metric name alone does not establish an identical protocol. ## Metrics - [Training FPS](): Higher is better; unit: SCALAR. - [Training Time (GPU-hr)](): Lower is better; unit: TIME. ## Recorded results | Method | Training FPS | Result date | Report paper | | --- | --- | --- | --- | | GLIP-T | 1.6 | Not recorded | 68fa6f0c1e209131cea43918 | ### GLIP-T Training FPS: 1.6. Result ID: 6990a4823a6c3148490214f0. [Reporting paper]() [Method origin paper]() [Implementation]() Recorded evaluation configuration: {"Pretrain-data":"O365+GoldG"}. This record does not assert independent reproduction. Page 1 of 1. This is an index page, not the full collection.