9 dataset results for Multi-Label Image Classification

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

BigEarthNet

BigEarthNet consists of 590,326 Sentinel-2 image patches, each of which is a section of i) 120x120 pixels for 10m bands; ii) 60x60 pixels for 20m bands; and iii) 20x20 pixels for 60m bands.

64 PAPERS • 3 BENCHMARKS

MSCOCO

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55 PAPERS • NO BENCHMARKS YET

VizWiz-Classification

Our goal is to improve upon the status quo for designing image classification models trained in one domain that perform well on images from another domain. Complementing existing work in robustness testing, we introduce the first test dataset for this purpose which comes from an authentic use case where photographers wanted to learn about the content in their images. We built a new test set using 8,900 images taken by people who are blind for which we collected metadata to indicate the presence versus absence of 200 ImageNet object categories. We call this dataset VizWiz-Classification.

21 PAPERS • 3 BENCHMARKS

COCO-MLT

The COCO-MLT is created from MS COCO-2017, containing 1,909 images from 80 classes. The maximum of training number per class is 1,128 and the minimum is 6. We use the test set of COCO2017 with 5,000 for evaluation. The ratio of head, medium, and tail classes is 22:33:25 in COCO-MLT.

12 PAPERS • 2 BENCHMARKS

VOC-MLT

We construct the long-tailed version of VOC from its 2012 train-val set. It contains 1,142 images from 20 classes, with a maximum of 775 images per class and a minimum of 4 images per class. The ratio of head, medium, and tail classes after splitting is 6:6:8. We evaluate the performance on VOC2007 test set with 4952 images.

12 PAPERS • 2 BENCHMARKS

Sewer-ML

Sewer-ML is a sewer defect dataset. It contains 1.3 million images, from 75,618 videos collected from three Danish water utility companies over nine years. All videos have been annotated by licensed sewer inspectors following the Danish sewer inspection standard, Fotomanualen. This leads to consistent and reliable annotations, and a total of 17 annotated defect classes.

4 PAPERS • NO BENCHMARKS YET

Bengali.AI Handwritten Graphemes

This dataset contains images of individual hand-written Bengali characters. Bengali characters (graphemes) are written by combining three components: a grapheme_root, vowel_diacritic, and consonant_diacritic. Your challenge is to classify the components of the grapheme in each image. There are roughly 10,000 possible graphemes, of which roughly 1,000 are represented in the training set. The test set includes some graphemes that do not exist in the train but has no new grapheme components. It takes a lot of volunteers filling out sheets like this to generate a useful amount of real data; focusing the problem on the grapheme components rather than on recognizing whole graphemes should make it possible to assemble a Bengali OCR system without handwriting samples for all 10,000 graphemes.

2 PAPERS • 1 BENCHMARK

dacl10k (dacl10k: Dataset for Semantic Bridge Damage Segmentation)

dacl10k stands for damage classification 10k images and is a multi-label semantic segmentation dataset for 19 classes (13 damages and 6 objects) present on bridges.

1 PAPER • NO BENCHMARKS YET

Datasets

9 dataset results for Multi-Label Image Classification