19,997 machine learning datasets
19,997 dataset results
The JPersonaChat dataset is built by NTT CS LAB for Japanese dialog transformers models. Please refer to https://github.com/nttcslab/japanese-dialog-transformers/tree/main?tab=readme-ov-file for detailed information.
A dataset of abdominal CT studies in NifTi format from the open-source medical data repository Medical Decathlon was utilized. To expedite the partitioning process, the MONAILabel plugin of the MONAI framework within the 3D Slicer program was employed. A radiologist with 15 years of experience conducted a validation process, wherein the boundaries of the colon markup were verified on each slice. The existing colorectal cancer markings in the dataset remained unaltered. Validation by a radiologist reduced the size of the validated dataset to 122 studies. In this case, the 122 studies were categorized into three subsets based on the quality of the data: The "good" subset comprises 100 studies, while the "bad" subset contains 17 cropped studies (in which the entire colon is not visible on the image). The "bad" subset comprises five studies. Two of these studies were of poor quality and could not identify the entire colon. Two further studies involved colon stomas following surgery, while
Dataset Description
Tiny ImageNet-A is a subset of the Tiny ImageNet test set consisting of 3,374 images comprising real-world, unmodified, and naturally occurring examples that are misclassified by ResNet-18. The sampling process of Tiny ImageNet-A roughly follows the concept of ImageNet-A introduced by Hendrycks et al. ("Natural Adversarial Examples"). For further information on the sampling process visit the original paper.
EuroSAT-C is an open-source data set comprising algorithmically generated corruptions applied to the EuroSAT test set following the concept of ImageNet-C. It comprises 19 different corruptions (15 test corruptions and 4 validation corruptions) spanning 5 severity levels resulting in 108,000 images for the validation set and 405,000 images for the test set. For further information on the corruptions visit the original GitHub repository of ImageNet-C.
ConSLAM is a real-world dataset collected periodically on a construction site to measure the accuracy of mobile scanners' SLAM algorithms.
The National Institute of Informatics - Chiba University (NII-CU) Multispectral Aerial Person Detection Dataset consists of 5,880 pairs of aligned RGB+FIR (Far infrared) images captured from a drone flying at heights between 20 and 50 meters, with the cameras pointed at 45 degrees down. We applied lens distortion correction and a homography warping to align the thermal images with the RGB images. We then labeled the people visible on the images with rectangular bounding boxes. The footage shows a baseball field and surroundings in Chiba, Japan, recorded in January 2020.
The Swiss Drone data set was recorded around Cheseaux-sur-Lausanne in Switzerland using a senseFly eBee Classic in 2014 (SenseFly, 2020). The 100 images were captured from a top-down perspective at a flight height of approximately 80 m above the ground at a resolution of 4608 x 3456 pixels. The Okutama Drone data set was recorded and annotated by NII (Laurmaa, 2016) in 2016 using a DJI Phantom 4 at a resolution of 3840 x 2160 pixels. The 91 images were captured over Okutama, west of Tokyo, Japan, from a drone at a flight height of approximately 90 m above the ground. Here, the flight height may have varied more as Okutama is located in a narrow valley with uneven ground.
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They make available a dataset of 655 places, collected by non-expert users world-wide.
A multi-tasking oral ulcer dataset (Autooral dataset) is proposed. Autooral dataset contains two major tasks of disease segmentation and classification. The labeling of the Autooral dataset was done by three experienced dentists. At the end of the annotation, we formed 420 images of oral data with high quality after cropping and removal operations. We standardize the image size to 256*256. The original image is a 24-bit RGB image, the ground truth for the segmentation task is an 8-bit image, and there are five different disease types for the classification task (including cancerous ulcers, traumatic ulcers and traumatic blood blisters, herpes-like aphthous ulcers, mild aphthous ulcers, severe aphthous ulcers). The ratio of the 5 different ulcer types for the classification task was 9:9:15:18:22 (with a few exclusions). Further, by chi-square test, there were significant differences in gender (p=0.04) and age (p=0.01) of the patients among the 5 ulcer types. All-age coverage, a 13-year
This is a part of dataset and models of the paper published in TMLR 2024 (Transactions on Machine Learning Research, https://jmlr.org/tmlr/).
This is a part of dataset of the paper published in TMLR 2024 (Transactions on Machine Learning Research, https://jmlr.org/tmlr/).
We compile successful jailbreaks into the Multi-Turn Human Jailbreaks (MHJ) dataset, consisting of 2,912 prompts across 537 multi-turn conversations. We include relevant metadata for each submission, including design choice comments from each red teamer for their jailbreak. The resulting attack success rate (ASR) of our human red teaming is shown as follows.
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The OnlySports Dataset is a comprehensive collection of sports-related text data, comprising approximately 600 billion tokens. This massive corpus was carefully curated from the FineWeb dataset, a cleaned subset of CommonCrawl spanning from 2013 to present. The dataset creation involved a two-step process:
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This research introduces the dataset that we created to test voice emotional recognition models with Vietnamese data. The data set is the result of research, testing, and filtering 250 emotional segments from movie, movie series and live show divided equally for 5 basic emotional states of humans: “anger, happiness, sadness, neutral and anxiety”, VNEMOS contains approximately 30 minutes long. This dataset brings a balanced and diverse emotional set to study emotional recognition issues related to understanding human mood and emotions. Through evaluation Our dataset achieved a 89\% accuracy, showcasing the adeptness at capturing the essence of positive emotions and robust learning capabilities in real-world scenarios. By clarifying these nuances. Our research will contribute much to future work on emotion recognition and signal processing in Vietnamese and data using human psychological science. Detailed information of the dataset is in the following link: “bit.ly/VNEMOS”.
The data set contains multimodal sensor data generated by a tracked mobile robot in an outdoor and an indoor environemnt. Sensors include radar (indurad iSDR-C), LiDAR (SICK TiM), and IMU (Phidgets IMU).