# Object Detection Training on O365 + GoldG + CC15M Source: [Object Detection Training on O365 + GoldG + CC15M]() Collection index: [llms.txt]() Data updated: 2026-02-26T06:07:35.201Z Evaluation of the computational efficiency required for training an Object Detection model on the combined O365, GoldG, and CC15M 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 + CC15M | | Metric | Training Time (GPU hours) | | Unit | TIME | | Direction | Lower 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 Time (GPU hours)](): Lower is better; unit: TIME. - [Training FPS](): Higher is better; unit: SCALAR. ## Recorded results | Method | Training Time (GPU hours) | Result date | Report paper | | --- | --- | --- | --- | | DetCLIPv2-T | 2.1 | Not recorded | 68fa6f0c1e209131cea43918 | ### DetCLIPv2-T Training Time (GPU hours): 2.1. Result ID: 6990a4823a6c3148490214f2. [Reporting paper]() [Method origin paper]() Recorded evaluation configuration: {"Pretrain-data":"O365+GoldG+CC15M"}. This record does not assert independent reproduction. Page 1 of 1. This is an index page, not the full collection.