19,997 machine learning datasets
19,997 dataset results
Lesion Boundary Segmentation Dataset is a dataset for lesion segmentation from the ISIC2018 challenge. The dataset contains skin lesions and their corresponding annotations.
IG-1B-Targeted is an internal Facebook AI Research dataset that consists of 940 million public images with 1.5K hashtags matching with 1000 ImageNet1K synsets.
This dataset consists of images and annotations in Bengali. The images are human annotated in Bengali by two adult native Bengali speakers. All popular image captioning datasets have a predominant western cultural bias with the annotations done in English. Using such datasets to train an image captioning system assumes that a good English to target language translation system exists and that the original dataset had elements of the target culture. Both these assumptions are false, leading to the need of a culturally relevant dataset in Bengali, to generate appropriate image captions of images relevant to the Bangladeshi and wider subcontinental context. The dataset presented consists of 9,154 images.
This brain anatomy segmentation dataset has 1300 2D US scans for training and 329 for testing. A total of 1629 in vivo B-mode US images were obtained from 20 different subjects (age<1 years old) who were treated between 2010 and 2016. The dataset contained subjects with IVH and without (healthy subjects but in risk of developing IVH). The US scans were collected using a Philips US machine with a C8-5 broadband curved array transducer using coronal and sagittal scan planes. For every collected image ventricles and septum pellecudi are manually segmented by an expert ultrasonographer. We split these images randomly into 1300 Training images and 329 Testing images for experiments. Note that these images are of size 512 × 512.
GAD, or Gene Associations Database, is a corpus of gene-disease associations curated from genetic association studies.
TRANCE extends CLEVR by asking a uniform question, i.e. what is the transformation between two given images, to test the ability of transformation reasoning. TRANCE includes three levels of settings, i.e. Basic (single-step transformation), Event (multi-step transformation), and View (multi-step transformation with variant views). Detailed information can be found in https://hongxin2019.github.io/TVR.
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.
TICaM is a Time-of-flight In-car Cabin Monitoring dataset for vehicle interior monitoring using a single wide-angle depth camera. This dataset addresses the deficiencies of other available in-car cabin datasets in terms of the ambit of labeled classes, recorded scenarios and provided annotations; all at the same time. It consists of an exhaustive list of actions performed while driving and multi-modal labeled images (depth, RGB and IR), with complete annotations for 2D and 3D object detection, instance and semantic segmentation as well as activity annotations for RGB frames. Additional to real recordings, it also contains a synthetic dataset of in-car cabin images with same multi-modality of images and annotations, providing a unique and extremely beneficial combination of synthetic and real data for effectively training cabin monitoring systems and evaluating domain adaptation approaches.
The AHP dataset consists of 56,599 images in total which are collected from several large-scale instance segmentation and detection datasets, including COCO, VOC (w/ SBD), LIP, Objects365 and OpenImages. Each image is annotated with a pixel-level segmentation mask of a single integrated human.
TAS500 is a semantic segmentation dataset for autonomous driving in unstructured environments. TAS500 offers fine-grained vegetation and terrain classes to learn drivable surfaces and natural obstacles in outdoor scenes effectively.
CSFCube is an expert annotated test collection to evaluate models trained to perform faceted Query by Example. This test collection consists of a diverse set of 50 query documents, drawn from computational linguistics and machine learning venues
RC-49 is a benchmark dataset for generating images conditional on a continuous scalar variable. It is made by rendering 49 3-D chair models from ShapeNet individually. Each chair model is rendered at 899 yaw angles from $0.1^{\circ}$ to $89.9^{\circ}$ with a stepsize of $0.1^{\circ}$. This dataset contains 44,051 RGB images of size $64\times64$ with corresponding yaw angles as labels.
Honda Egocentric View-Intersection Dataset (HEV-I) is introduced to enable research on traffic participants interaction modelling, future object localization, as well as learning driver action in challenging driving scenarios. The dataset includes 230 video clips of real human driving in different intersections from the San Francisco Bay Area, collected using an instrumented vehicle equipped with different sensors including cameras, GPS/IMU, and vehicle states signals.
Timers and Such is an open source dataset of spoken English commands for common voice control use cases involving numbers. The dataset has four intents, corresponding to four common offline voice assistant uses: SetTimer, SetAlarm, SimpleMath, and UnitConversion. The semantic label for each utterance is a dictionary with the intent and a number of slots.
iPer is a new dataset, with diverse styles of clothes in videos, for the evaluation of human motion imitation, appearance transfer, and novel view synthesis. There are 30 subjects of different conditions of shape, height, and gender. Each subject wears different clothes and performs an A-pose video and a video with random actions. There are 103 clothes in total. The whole dataset contains 206 video sequences with 241,564 frames.
This dataset arises from the READ project (Horizon 2020).
Twitter-MEL is a multimodal entity linking (MEL) dataset built from Twitter. The dataset consists of tweets that had both text and images, with a total of 2.6M timeline tweets and 20k entities.
SSN (short for Semantic Scholar Network) is a scientific papers summarization dataset which contains 141K research papers in different domains and 661K citation relationships. The entire dataset constitutes a large connected citation graph.