Multi-Task Learning

FSDKaggle2019 is an audio dataset containing 29,266 audio files annotated with 80 labels of the AudioSet Ontology. FSDKaggle2019 has been used for the DCASE Challenge 2019 Task 2, which was run as a Kaggle competition titled Freesound Audio Tagging 2019. The dataset allows development and evaluation of machine listening methods in conditions of label noise, minimal supervision, and real-world acoustic mismatch. FSDKaggle2019 consists of two train sets and one test set. One train set and the test set consists of manually-labeled data from Freesound, while the other train set consists of noisily labeled web audio data from Flickr videos taken from the YFCC dataset. The curated train set consists of manually labeled data from FSD: 4970 total clips with a total duration of 10.5 hours. The noisy train set has 19,815 clips with a total duration of 80 hours. The test set has 4481 clips with a total duration of 12.9 hours. Source: https://labs.freesound.org/datasets/ Image Source: https://labs.freesound.org/datasets/

PGDP5K is a dataset consisting of 5000 diagram samples composed of 16 shapes, covering 5 positional relations, 22 symbol types and 6 text types, labeled with more fine-grained annotations at primitive level, including primitive classes, locations and relationships, where 1,813 non-duplicated images are selected from the Geometry3K dataset and other 3,187 images are collected from three popular textbooks across grades 6-12 on mathematics curriculum websites by taking screenshots from PDF books.

WWW Crowd provides 10,000 videos with over 8 million frames from 8,257 diverse scenes, therefore offering a comprehensive dataset for the area of crowd understanding.

This dataset is constructed and based on the online free-access fictions that are tagged with sci-fi, urban novel, love story, youth, etc. It is used for Writing Polishment with Smile (WPS) a task that aims to polish plain text with similes. All similes are extracted by rich regular expression, and the extraction precision is estimated as 92% by labelling 500 random extracted samples. It contains 5M samples for training and 2.5k for validation and test respectively. Source: https://github.com/mrzjy/writing-polishment-with-simile

This is a dataset for segmentation and classification of epistemic activities in diagnostic reasoning texts. Source: Analysis of Automatic Annotation Suggestions for Hard Discourse-Level Tasks in Expert Domains

Publicly available dataset in the hotel domain (50M versus 0.9M) and additionally, the largest recommendation dataset in a single domain and with textual reviews (50M versus 22M). Source: HotelRec: a Novel Very Large-Scale Hotel Recommendation Dataset

An annotated dataset is released to enable dynamic scene classification that includes 80 hours of diverse high quality driving video data clips collected in the San Francisco Bay area. The dataset includes temporal annotations for road places, road types, weather, and road surface conditions. Source: Dynamic Traffic Scene Classification with Space-Time Coherence

Multimodal Dyadic Behavior (MMDB) dataset is a unique collection of multimodal (video, audio, and physiological) recordings of the social and communicative behavior of toddlers. The MMDB contains 160 sessions of 3-5 minute semi-structured play interaction between a trained adult examiner and a child between the age of 15 and 30 months. The MMDB dataset supports a novel problem domain for activity recognition, which consists of the decoding of dyadic social interactions between adults and children in a developmental context. Source: MMDB

OSAI introduces OpenTTGames - an open dataset aimed at evaluation of different computer vision tasks in Table Tennis: ball detection, semantic segmentation of humans, table and scoreboard and fast in-game events spotting. It includes full-HD videos of table tennis games recorded at 120 fps with an industrial camera. Every video is equipped with an annotation containing the frame numbers and corresponding targets for this particular frame: manually labeled in-game events (ball bounces, net hits, or empty event targets) and/or ball coordinates and segmentation masks, which were labeled with deep learning-aided annotation models.

RoboPianist is a benchmarking suite for high-dimensional control, targeted at testing high spatial and temporal precision, coordination, and planning, all with an underactuated system frequently making-and-breaking contacts. The proposed challenge is mastering the piano through bi-manual dexterity, using a pair of simulated anthropomorphic robot hands. The initial version covers a broad set of 150 variable-difficulty songs. Source: RoboPianist: A Benchmark for High-Dimensional Robot Control Image Source: RoboPianist: A Benchmark for High-Dimensional Robot Control

Huggingface Datasets is a great library, but it lacks standardization, and datasets require preprocessing work to be used interchangeably. tasksource automates this and facilitates reproducible multi-task learning scaling. Each dataset is standardized to either MultipleChoice, Classification, or TokenClassification dataset with identical fields. We do not support generation tasks as they are addressed by promptsource. All implemented preprocessings are in tasks.py or tasks.md. A preprocessing is a function that accepts a dataset and returns the standardized dataset. Preprocessing code is concise and human-readable.

CQR is an extension to the Stanford Dialogue Corpus. It contains crowd-sourced rewrites to facilitate research in dialogue state tracking using natural language as the interface. Source: A dataset for resolving referring expressions in spoken dialogue via contextual query rewrites (CQR)

ExHVV is a novel dataset that offers natural language explanations of connotative roles for three types of entities -- heroes, villains, and victims, encompassing 4,680 entities present in 3K memes. Source: What do you MEME? Generating Explanations for Visual Semantic Role Labelling in Memes Image Source: https://arxiv.org/pdf/2212.00715v1.pdf

The Cifar10Mnist dataset is created using CIFAR-10 and MNIST data sources. Since the CIFAR-10 training set consists of 50000 images and the MNIST training set contains 60000 digits, the first 50000 digits from MNIST are padded on top of the CIFAR-10 images after making them slightly translucent. A first training dataset is then obtained (50000 images). Furthermore, the remaining 10000 MNIST digits are padded on top of 10000 random CIFAR10 images (with a fixed seed). This gives the possibility of having a second training dataset of 60000 images. For the test set, the 10000 CIFAR-10 images are padded over the 10000 MNIST digits.

A large-scale dataset for recognizing kinship in multimedia which extend FIW with multimedia data (i.e., video, audio, and contextual transcripts). Source: Families In Wild Multimedia (FIW-MM): A Multi-Modal Database for Recognizing Kinship

A large-scale machine comprehension dataset (based on the COCO images and captions). Source: Understanding Image and Text Simultaneously: a Dual Vision-Language Machine Comprehension Task

A large scale, C2C marketplace e-commerce dataset.

The noun–noun compounds dataset created by Fares (2016) consists of compounds annotated with two different taxonomies of relations; that is, for each noun–noun compound there are two distinct relations, drawing on different linguistic schools. The dataset was derived from existing linguistic resources, such as NomBank (Meyers et al., 2004) and the Prague Czech-English Dependency Treebank 2.0 (PCEDT; Hajič et al., 2012). Source: Noun-Noun Compound Dataset

A large-scale dataset of user annotations on seven common photographic defects. Source: Learning to Detect Multiple Photographic Defects

The pic2kal benchmark for calorie prediction contains 308,000 images from over 70,000 recipes including photographs, ingredients and instructions, matched with nutritional information. Source: https://arxiv.org/abs/2011.01082 Image Source: https://github.com/phiresky/pic2kcal

SemanticSugarBeets, a novel and high-quality dataset containing 953 monocular RGB images and 2920 annotations of sugar beets, enables a wide range of learning tasks including object detection, semantic segmentation, instance segmentation and mass estimation for post-harvest and post-storage analysis.

The availability of well-curated datasets has driven the success of Machine Learning (ML) models. Despite greater access to earth observation data in agriculture, there is a scarcity of curated and labelled datasets, which limits the potential of its use in training ML models for remote sensing (RS) in agriculture. To this end, we introduce a first-of-its-kind dataset called SICKLE, which constitutes a time-series of multi-resolution imagery from 3 distinct satellites: Landsat-8, Sentinel-1 and Sentinel-2. Our dataset constitutes multi-spectral, thermal and microwave sensors during January 2018 - March 2021 period. We construct each temporal sequence by considering the cropping practices followed by farmers primarily engaged in paddy cultivation in the Cauvery Delta region of Tamil Nadu, India; and annotate the corresponding imagery with key cropping parameters at multiple resolutions (i.e. 3m, 10m and 30m). Our dataset comprises 2, 370 season-wise samples from 388 unique plots, having an average size of 0.38 acres, for classifying 21 crop types across 4 districts in the Delta, which amounts to approximately 209, 000 satellite images. Out of the 2, 370 samples, 351 paddy samples from 145 plots are annotated with multiple crop parameters; such as the variety of paddy, its growing season and productivity in terms of per-acre yields. Ours is also one among the first studies that consider the growing season activities pertinent to crop phenology (spans sowing, transplanting and harvesting dates) as parameters of interest. We benchmark SICKLE on three tasks: crop type, crop phenology (sowing, transplanting, harvesting), and yield prediction.

The dataset contains more than 100k code patch pairs extracted from open source projects on GitHub. Each pair comes with the erroneous and the fixed version of the corresponding code snippet. Instead of the whole file, the code snippets are extracted to focus on the problematic region (error line + other lines around it). For each sample, the repository name, the commit id, and the file names are provided so that one can access the complete files in case of interest. The dataset only has JavaScript programs and the error are detected by the popular static code analyzer ESLint. The dataset can be used in the fields of: program repair, code generation, bug finding, transfer learning and many more fields related to machine learning for code

The TimberVision dataset consists of more than 2k annotated RGB images and contains a total of 51k trunk components including cut and lateral surfaces, thereby surpassing any existing dataset in this domain in terms of both quantity and detail by a large margin. The dataset can be used to train oriented object detection and instance segmentation and evaluate the influence of multiple scene parameters on model performance. Additionally, a generic framework is provided to fuse the components detected by the models for both tasks into unified trunk representations. Furthermore, geometric properties are derived automatically and multi-object tracking is applied to further enhance robustness.

A large video dataset with dynamic content. Source: Deep Homography Estimation for Dynamic Scenes

CVGL Camera Calibration Dataset consists of 49 camera configurations with town 1 having 25 configurations while town 2 having 24 configurations. The parameters modified for generating the configurations include fov, x, y, z, pitch, yaw, and roll. Here, fov is the field of view, (x, y, z) is the translation while (pitch, yaw, and roll) is the rotation between the cameras. The total number of image pairs is 79, 320, out of which 18, 083 belong to Town 1 while 61, 237 belong to Town 2, the difference in the number of images is due to the length of the tracks. Source: Multi-task Learning for Camera Calibration Image Source: https://arxiv.org/pdf/2211.12432v1.pdf

a large video dataset captured with UAVs in different complex real-world scenes, with multiple representations, suitable for multi-task learning.