3,275 machine learning datasets
3,275 dataset results
The ISIC 2017 dataset was published by the International Skin Imaging Collaboration (ISIC) as a large-scale dataset of dermoscopy images. The Task 3 challenge dataset for lesion classification contains 2,000 images for training including 374 melanoma, 254 seborrheic keratosis and the remainder as benign nevi (1372).
BRATS 2014 is a brain tumor segmentation dataset.
CLIC is a dataset for learned image compression. The dataset contains both RGB and grayscale images.
The Messidor database has been established to facilitate studies on computer-assisted diagnoses of diabetic retinopathy. The research community is welcome to test its algorithms on this database. In this section, you will find instructions on how to download the database.
Cata7 is the first cataract surgical instrument dataset for semantic segmentation. The dataset consists of seven videos while each video records a complete cataract surgery. All videos are from Beijing Tongren Hospital. Each video is split into a sequence of images, where resolution is 1920×1080 pixels. To reduce redundancy, the videos are downsampled from 30 fps to 1 fps. Also, images without surgical instruments are manually removed. Each image is labeled with precise edges and types of surgical instruments. This dataset contains 2,500 images, which are divided into training and test sets. The training set consists of five video sequences and test set consists of two video sequence.
ARID is a large-scale, multi-view object dataset collected with an RGB-D camera mounted on a mobile robot.
NYU-VP is a new dataset for multi-model fitting, vanishing point (VP) estimation in this case. Each image is annotated with up to eight vanishing points, and pre-extracted line segments are provided which act as data points for a robust estimator. Due to its size, the dataset is the first to allow for supervised learning of a multi-model fitting task.
Dataset to address the problem of detecting people Looking At Each Other (LAEO) in video sequences.
The BanglaWriting dataset contains single-page handwritings of 260 individuals of different personalities and ages. Each page includes bounding-boxes that bounds each word, along with the unicode representation of the writing. This dataset contains 21,234 words and 32,787 characters in total. Moreover, this dataset includes 5,470 unique words of Bangla vocabulary. Apart from the usual words, the dataset comprises 261 comprehensible overwriting and 450 incomprehensible overwriting. All of the bounding boxes and word labels are manually-generated. The dataset can be used for complex optical character/word recognition, writer identification, and handwritten word segmentation. Furthermore, this dataset is suitable for extracting age-based and gender-based variation of handwriting.
Chinese Text in the Wild is a dataset of Chinese text with about 1 million Chinese characters from 3850 unique ones annotated by experts in over 30000 street view images. This is a challenging dataset with good diversity containing planar text, raised text, text under poor illumination, distant text, partially occluded text, etc.
Cube++ is a novel dataset for the color constancy problem that continues on the Cube+ dataset. It includes 4890 images of different scenes under various conditions. For calculating the ground truth illumination, a calibration object with known surface colors was placed in every scene.
The Flick Cropping Dataset consists of high quality cropping and pairwise ranking annotations used to evaluate the performance of automatic image cropping approaches.
HJDataset is a large dataset of Historical Japanese Documents with Complex Layouts. It contains over 250,000 layout element annotations of seven types. In addition to bounding boxes and masks of the content regions, it also includes the hierarchical structures and reading orders for layout elements. The dataset is constructed using a combination of human and machine efforts.
Intrinsic Images in the Wild is a large scale, public dataset for intrinsic image decompositions of real-world scenes selected from the OpenSurfaces dataset. Each image is annotated with crowdsourced pairwise comparisons of material properties.
The IPN Hand dataset is a benchmark video dataset with sufficient size, variation, and real-world elements able to train and evaluate deep neural networks for continuous Hand Gesture Recognition (HGR).
N-Digit MNIST is a multi-digit MNIST-like dataset.
The NYU Symmetry database contains 176 single-symmetry and 63 multiple-symmetry images (.png files) with accompanying ground-truth annotations (.mat files). Also included are a .m file to visualize the annotations on top of the images, and a .txt file with instructions on how to interpret the .mat annotations.
PedX is a large-scale multi-modal collection of pedestrians at complex urban intersections. The dataset provides high-resolution stereo images and LiDAR data with manual 2D and automatic 3D annotations. The data was captured using two pairs of stereo cameras and four Velodyne LiDAR sensors.
Contains 10,000 fine-grained SKU-level products frequently bought by online customers in JD.com.
Simitate is a hybrid benchmarking suite targeting the evaluation of approaches for imitation learning. It consists on a dataset containing 1938 sequences where humans perform daily activities in a realistic environment. The dataset is strongly coupled with an integration into a simulator. RGB and depth streams with a resolution of 960×540 at 30Hz and accurate ground truth poses for the demonstrator's hand, as well as the object in 6 DOF at 120Hz are provided. Along with the dataset the 3D model of the used environment and labelled object images are also provided.