Show HN: Real-World Datasets for Benchmarking Object Detection Models
rf100.org
rf100.org
* Paper: https://arxiv.org/abs/2211.13523
* Github: https://github.com/roboflow-ai/roboflow-100-benchmark
At Roboflow, we've seen users fine-tune hundreds of thousands of computer vision models on custom datasets.
We observed that there's a huge disconnect between the types of tasks people are actually trying to perform in the wild and the types of datasets researchers are benchmarking their models on.
Datasets like MS COCO (with hundreds of thousands of images of common objects) are often used in research to compare models' performance, but then those models are used to find galaxies, look at microscope images, or detect manufacturing defects in the wild (often trained on small datasets containing only a few hundred examples). This leads to big discrepancies in models' stated and real-world performance.
We set out to tackle this problem by creating a new set of datasets that mirror many of the same types of challenges that models will face in the real world. We compiled 100 datasets from our community spanning a wide range of domains, subjects, and sizes.
We've benchmarked a couple of models (YOLOv5, YOLOv7, and GLIP) to start, but could use your help measuring the performance of others on this benchmark (check the GitHub for starter scripts showing how to pull the dataset, fine-tune models, and evaluate). We're very interested to learn which models do best in which real-world scenarios & to give researchers a new tool to make their models more useful for solving real-world problems.