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Image classification, object detection, segmentation, face recognition, OCR, image generation, video understanding
16,486 datasets
A dataset of object detection results likely generated by the YOLO11n model. The dataset appears to focus on printed circuit board (PCB) components, suggesting it contains bounding box annotations and confidence scores. It is hosted on Kaggle, but the specific source, size, and creation details are not provided in the available metadata.
A dataset for object detection and computer vision tasks, containing images with associated tags. The dataset's specific content, size, and origin are not detailed in the provided input.
Posdocrativ is a computer vision dataset hosted on Kaggle. The dataset's specific content, size, and creation details are not provided in the available metadata. Further verification is required after download to confirm its exact nature and scope.
RUOD-YOLO is a dataset published on Kaggle. The title suggests it is designed for training and evaluating YOLO (You Only Look Once) object detection models. The dataset's specific contents, scale, and origin require verification after download.
47,420 road images collected from six countries including Japan, India, Czech Republic, China, Norway, and the United States, featuring 12,135 annotated damage instances. The dataset categorizes road distress into four classes: longitudinal cracks, transverse cracks, alligator cracks, and potholes.
Anthropic released this dataset in late 2024 containing 1 million to 10 million evaluation transcripts generated during reinforcement learning (RL) runs. The data documents the reasoning processes of 'model organisms' used to research and develop training-time mitigations for alignment faking.
YOLO_APD is a dataset for object detection tasks, published on Kaggle. The dataset's specific content, scale, and origin are not detailed in the provided metadata. Users must download the dataset to verify its exact composition and suitability for their projects.
LINEMOD is a dataset hosted on Kaggle. The title indicates it contains images, masks, labels, .ply files, and 2D keypoints, which suggests a collection for object detection and pose estimation tasks. The dataset's author, organization, and specific scale are unknown from the provided input.
A computer vision dataset from Kaggle titled 'LINEMOD - Images/Labels/Masks/Ply/2d-Keypoints'. The title suggests it contains images, labels, masks, 3D point cloud files (.ply), and 2D keypoint annotations. The author, organization, and specific scale are unknown.
VSL-Keypoints is a dataset hosted on Kaggle, likely containing images annotated with keypoint coordinates for computer vision tasks. The dataset's specific content, size, and creation details are not provided in the available metadata. Further verification is required to confirm the exact nature and scope of the visual data.
A dataset of images containing International Electrotechnical Commission (IEC) symbols, formatted for training YOLO v5 object detection models. The dataset appears to be augmented, which likely increases the number of training examples. It is hosted on the Kaggle platform, but details on the creator, collection date, and original source are unavailable.
A dataset titled 'dhbk_hb_model cnn gei 000 v24.7' published on Kaggle. The title suggests it is likely a Convolutional Neural Network (CNN) model or related data, possibly version 24.7. Specific details on the creator, size, and content are not provided in the metadata.
dhbk_hb_emb cnn gei 000 v24.7 is a dataset published on Kaggle. Its title and platform tags suggest it contains feature embeddings generated by a Convolutional Neural Network (CNN), likely for computer vision tasks. The specific source, scale, and creation details are not provided in the available metadata.
Text steganalysis involves detecting hidden messages within digital text or images. This dataset, sourced from Kaggle, likely contains examples for training models to distinguish between clean and steganographically altered content. The specific volume, features, and creation details require verification after download.
A dataset likely containing images of shrimp with annotations for object detection. The original annotations appear to have been converted from the COCO (Common Objects in Context) format to the YOLO (You Only Look Once) format. It is published on the Kaggle platform, but details on the number of images, annotation classes, and creation date are not provided in the metadata.
A Kaggle dataset titled 'cracks_yolo and drywall_segment'. The dataset likely contains images of building materials, such as drywall, with annotations for cracks and segmentation. Its specific content, scale, and origin require verification after download.
A pre-trained Convolutional Neural Network (CNN) model, likely for image-based tasks, published on Kaggle. The specific architecture, training data, and performance metrics are not detailed in the available metadata. The version identifier 'v24.4' suggests it is part of an iterative development series.
A dataset of image embeddings likely generated by a Convolutional Neural Network (CNN), published on Kaggle. The title suggests the embeddings may be derived from a specific model version (v24.4) and could be related to a 'gei' or 'hb' domain. The specific source images, data volume, and creation date are not provided in the available metadata.
A dataset titled 'YOLO-Classify_0-9,95' suggests a collection of images for object detection and classification tasks, likely using the YOLO (You Only Look Once) model framework. It is hosted on the Kaggle platform, but detailed metadata such as author, size, and creation date are not provided. The title implies the data may be organized for classifying objects into categories, possibly numbered from 0 to 9.
An open-source synthetic dataset for computer vision tasks, created by Simuletic. It is designed for fall detection, pose estimation, and incident monitoring from overhead CCTV camera perspectives. The dataset includes keypoint annotations for human posture, which may help models distinguish between standing and fallen individuals.