데이터셋 탐색

Contains normal driving videos together with a set of anomalous actions in its training set. In the test set of the DAD dataset, there are unseen anomalous actions that still need to be winnowed out from normal driving. Source: Driver Anomaly Detection: A Dataset and Contrastive Learning Approach

DDAD is a new autonomous driving benchmark from TRI (Toyota Research Institute) for long range (up to 250m) and dense depth estimation in challenging and diverse urban conditions. It contains monocular videos and accurate ground-truth depth (across a full 360 degree field of view) generated from high-density LiDARs mounted on a fleet of self-driving cars operating in a cross-continental setting. DDAD contains scenes from urban settings in the United States (San Francisco, Bay Area, Cambridge, Detroit, Ann Arbor) and Japan (Tokyo, Odaiba). Source: https://github.com/TRI-ML/DDAD Image Source: https://github.com/TRI-ML/DDAD

DADA-seg is a pixel-wise annotated accident dataset, which contains a variety of critical scenarios from traffic accidents. It is used for semantic segmentation.

DADA-2000 is a large-scale benchmark with 2000 video sequences (named as DADA-2000) is contributed with laborious annotation for driver attention (fixation, saccade, focusing time), accident objects/intervals, as well as the accident categories, and superior performance to state-of-the-arts are provided by thorough evaluations. Source: DADA: A Large-scale Benchmark and Model for Driver Attention Prediction in Accidental Scenarios Image Source: Fang et al

DadaGP is a new symbolic music dataset comprising 26,181 song scores in the GuitarPro format covering 739 musical genres, along with an accompanying tokenized format well-suited for generative sequence models such as the Transformer. The tokenized format is inspired by event-based MIDI encodings, often used in symbolic music generation models. The dataset is released with an encoder/decoder which converts GuitarPro files to tokens and back. Description from: DadaGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models Image source: https://arxiv.org/pdf/2107.14653v1.pdf

DAD-3DHeads dataset consists of 44,898 images collected from various sources (37,840 in the training set, 4,312 in the validation set, and 2,746 in the test set). The dataset is well-balanced over a wide range of poses, face expressions, and occlusions. The attribute labels are a valuable signal for subgroup analysis and for generalisation to in-the-wild deployment conditions.

The DADE dataset, short for Driving Agents in Dynamic Environments, is a synthetic dataset designed for the training and evaluation of methods for the task of semantic segmentation in the context of autonomous driving agents navigating dynamic environments and weather conditions. This dataset was generated using the CARLA simulator (version 0.9.14) to provide perfect sensor synchronization and calibration, as well as precise semantic segmentation ground truths. All data were collected within the Town12 map in CARLA. DADE dataset is divided into two sub-datasets. For both subsets, each sequence is acquired by one agent (one ego vehicle) running for some time within a 5-hour time frame, amounting to a total of 990k frames for the entire dataset. The agents travel various locations such as forest, countryside, rural farmland, highway, low density residential, community buildings, and high density residential.

needadvice is a dataset for advice classification extracted from Reddit. In this dataset, posts are annotated for whether they contain advice or not. It contains 6,148 samples for training, 816 for validation and 898 for testing. Source: https://github.com/venkatasg/Advice-EMNLP2020