9 dataset results for Robust Object Detection

MS COCO (Microsoft Common Objects in Context)

The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.

10,147 PAPERS • 92 BENCHMARKS

Cityscapes

Cityscapes is a large-scale database which focuses on semantic understanding of urban street scenes. It provides semantic, instance-wise, and dense pixel annotations for 30 classes grouped into 8 categories (flat surfaces, humans, vehicles, constructions, objects, nature, sky, and void). The dataset consists of around 5000 fine annotated images and 20000 coarse annotated ones. Data was captured in 50 cities during several months, daytimes, and good weather conditions. It was originally recorded as video so the frames were manually selected to have the following features: large number of dynamic objects, varying scene layout, and varying background.

3,323 PAPERS • 54 BENCHMARKS

PASCAL VOC 2007

PASCAL VOC 2007 is a dataset for image recognition. The twenty object classes that have been selected are:

119 PAPERS • 14 BENCHMARKS

DWD (Diverse Weather Dataset)

Urban-scene detection dataset that consists of five different weather conditions: daytime-sunny, night-sunny, dusk-rainy, daytime-foggy, and night-rainy. The images are collected from diverse weather datasets: Cityscapes, BDD-100k, FoggyCityscapes, and Adverse-Weather. The dataset is used to evaulate the model performance on the single-domain generalized object detection (Single-DGOD).

8 PAPERS • 1 BENCHMARK

RF100 (Roboflow 100)

The evaluation of object detection models is usually performed by optimizing a single metric, e.g. mAP, on a fixed set of datasets, e.g. Microsoft COCO and Pascal VOC. Due to image retrieval and annotation costs, these datasets consist largely of images found on the web and do not represent many real-life domains that are being modelled in practice, e.g. satellite, microscopic and gaming, making it difficult to assert the degree of generalization learned by the model.

4 PAPERS • 1 BENCHMARK

Aircraft Context Dataset

The Aircraft Context Dataset, a composition of two inter-compatible large-scale and versatile image datasets focusing on manned aircraft and UAVs, is intended for training and evaluating classification, detection and segmentation models in aerial domains. Additionally, a set of relevant meta-parameters can be used to quantify dataset variability as well as the impact of environmental conditions on model performance.

3 PAPERS • NO BENCHMARKS YET

Separated COCO

Separated COCO is automatically generated subsets of COCO val dataset, collecting separated objects for a large variety of categories in real images in a scalable manner, where target object segmentation mask is separated into distinct regions by the occluder.

3 PAPERS • 1 BENCHMARK

Occluded COCO

Occluded COCO is automatically generated subset of COCO val dataset, collecting partially occluded objects for a large variety of categories in real images in a scalable manner, where target object is partially occluded but the segmentation mask is connected.

2 PAPERS • 1 BENCHMARK

Apron Dataset

The Apron Dataset focuses on training and evaluating classification and detection models for airport-apron logistics. In addition to bounding boxes and object categories the dataset is enriched with meta parameters to quantify the models’ robustness against environmental influences.

1 PAPER • NO BENCHMARKS YET

Datasets

9 dataset results for Robust Object Detection