📸 Roboflow 100: A Multi-Domain Object Detection Benchmark

Roboflow 100 (RF100) is an open-source, crowdsourced benchmark designed to evaluate the generalizability of object detection models across diverse domains, addressing the limitations of traditional datasets like Microsoft COCO and Pascal VOC. Sponsored by Intel, RF100 comprises 100 datasets with 224,714 images and 829 class labels, spanning seven imagery domains, including real-world, aerial, and microscopic categories. It serves as a complement to existing datasets by providing a broader scope of data to test model performance beyond common objects in context. The initiative aims to provide insights into how models trained on diverse data perform in the wild, particularly in domains not covered by COCO. RF100's datasets are curated from Roboflow Universe based on criteria such as effort, diversity, and quality, with images resized and annotated to eliminate class ambiguity. Initial evaluations using models like YOLOv5, YOLOv7, and GLIP show that RF100 is effective in benchmarking model performance across different domains, highlighting its potential to enhance computer vision research.