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Image classification, object detection, segmentation, face recognition, OCR, image generation, video understanding
16,469 datasets
2,000 high-resolution images of Japanese and Sri Lankan textiles categorized by cultural origin. The collection facilitates cross-cultural comparative analysis and pattern recognition for traditional fabric designs.
4 categories of book cover images including fiction, history, technical, and law are provided for visual classification tasks. The data captures the distinct aesthetic differences between academic, professional, and creative publishing genres through their respective jacket designs.
100 video sequences containing 140,000 frames and 1.21 million manually annotated bounding boxes of vehicles. The dataset covers four vehicle categories—car, bus, van, and others—captured across varying weather, scale, and occlusion levels.
3D CT scan volumes of carbonized Herculaneum scrolls stored in NPZ format for the specific task of surface detection. The data enables the identification of papyrus layers within a volumetric space to support the virtual unwrapping of ancient texts.
3 novels' worth of structured data mapping characters, locations, and organizations within the Killing Eve literary series by Luke Jennings. The dataset tracks entities and their specific interpersonal or professional relations across the entire book trilogy.
14,493 images in 224x224x3 JPG format categorized into 100 distinct sports classes. The collection is partitioned into 13,493 training, 500 testing, and 500 validation samples for supervised learning tasks.
Food ingredient images organized into categories for image classification tasks. The collection provides labeled visual data for training computer vision models to recognize common culinary items.
3,475 high-resolution street scene images captured at 2048×1024 pixels. The dataset features dense pixel-level annotations across 35 distinct object and surface classes.
472 organic charge neutral molecules are cataloged in this thermochemistry dataset calculated at 298 K. The data provides molecular properties derived from the B3LYP functional with D3B(J) dispersion corrections and the def2-SVP basis set.
25,000 images of natural scenes categorized into 6 distinct classes: buildings, forest, glacier, mountain, sea, and street. The data is organized into three directories for training, testing, and prediction to facilitate standardized model evaluation across 150x150 pixel RGB images.
355 YOLO-formatted images featuring vehicle number plates specifically from the state of Gujarat, India. The collection is curated for Automatic Number Plate Recognition (ANPR) and includes bounding box annotations for object detection.
Synthetic images of medical prescriptions comprise this dataset, which is designed for Optical Character Recognition (OCR) and document analysis. The collection simulates realistic healthcare documents featuring multiple medicine entries per page to support model training without privacy risks.
Kaggle hosts a dataset titled 'hw ocr full line', which likely contains images of handwritten text lines for optical character recognition tasks. The dataset's specific scale, origin, and creation date are not provided in the available metadata. Platform tags suggest it is intended for computer vision applications involving handwriting analysis.
A dataset for object detection, likely containing images of Lego bricks. The data is formatted for use with the YOLO (You Only Look Once) object detection framework. It is hosted on the Kaggle platform.
An optical character recognition dataset published on Kaggle. The title 'am_ocr1' suggests it likely contains images of text for machine reading tasks. Specifics on volume, source, and creation date are unavailable from the provided metadata.
Kaggle hosts this computer vision dataset titled 'dhbk_hb_emb cnn gei 045 v24.4'. The title suggests it may involve convolutional neural network (CNN) embeddings or features. Its specific content, size, and origin require verification after download.
OCR Check Point is a dataset hosted on Kaggle. Its title and platform tags suggest it contains images for testing Optical Character Recognition (OCR) systems. The dataset's specific content, size, and origin require verification after download.
OCR-checkpoint-thongtu is a dataset published on Kaggle. The platform tags suggest it contains resources related to optical character recognition, likely a model checkpoint. The dataset's specific content, size, and origin require verification after download.
Kaggle hosts a pre-trained Convolutional Neural Network model titled 'dhbk_hb_model cnn gei 045 v24.4'. The model's specific architecture, training data, and performance metrics are not detailed in the provided metadata. Its release and update timeline are also unspecified.
Kaggle hosts a dataset titled 'fire-detection-yolo', which likely contains images for training and evaluating object detection models focused on fire identification. The dataset's specific scale, source, and creation date are not provided in the available metadata. Its content and structure must be verified after download.