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Image classification, object detection, segmentation, face recognition, OCR, image generation, video understanding
16,478 datasets
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.
Kaggle hosts the svresnetse-23.03 model, a pre-trained neural network likely based on the ResNet architecture. The model's specific training data, performance metrics, and intended image classification tasks are not detailed in the available metadata. Its release identifier suggests a version from March 2023.
Kaggle hosts this dataset titled 'yolo11L_old'. The dataset likely contains images and annotations for training or benchmarking object detection models, specifically related to the YOLO (You Only Look Once) architecture. Its author, organization, and specific content details are not provided in the available metadata.
Kaggle hosts this dataset of images with object detection annotations, likely formatted for the YOLO (You Only Look Once) model framework. The dataset's specific size, source, and creation date are not provided in the metadata. Its content and scope must be verified after download.
A dataset of satellite images likely intended for object detection tasks, as suggested by the title and platform tags referencing YOLO (You Only Look Once). It is hosted on Kaggle and appears to be related to the SVAMITVA initiative, a land records modernization scheme in India. The specific content, scale, and creation details require verification after download.
SVAMITVA is a government-led initiative in India for mapping rural inhabited land parcels. This dataset, titled 'SVAMITVA YOLO11x', likely contains satellite or aerial imagery prepared for object detection tasks, possibly related to property boundaries. It is hosted on Kaggle, but detailed metadata about its creation and content is not provided.
Kaggle hosts the VulCNN-NLP-Model, a machine learning model likely designed for processing text related to software vulnerabilities. The dataset's specific contents, such as training data or model parameters, are not detailed in the available metadata. Its author, size, and last update date are unknown.
A pre-trained Convolutional Neural Network model, likely for image classification or feature extraction, published on Kaggle. The specific architecture, training data, and performance metrics require verification after download. The title suggests it is version 24.14 of a model potentially named 'dhbk_hb_model'.
A dataset published on Kaggle, categorized under Computer Vision. The title suggests it may involve CNN (Convolutional Neural Network) embeddings, potentially for image analysis. The specific content, scale, and origin require verification after download.
A dataset likely containing model weights or related data for the VGG19 convolutional neural network architecture pre-trained on the ImageNet dataset. It was published on Kaggle, but the author, organization, and last update date are unknown. The specific content, such as the number of files or their format, is not detailed in the available metadata.
A dataset for Optical Character Recognition (OCR) tasks in the Telugu language, published on the Hugging Face platform by author Divs0910. The dataset was last updated on 2026-02-04. Its specific content and scale require verification after download.
A Convolutional Neural Network (CNN) model, likely for image classification or analysis, published on Kaggle. The title suggests it is a versioned release (v24.6) of a model, possibly from an academic or research project. Specific details on architecture, training data, and performance are not provided in the available metadata.
A pre-trained Generative Adversarial Network model checkpoint shared on Kaggle. The model's specific architecture, training data, and performance metrics are not detailed in the provided metadata. Users must download and inspect the model files to understand its capabilities and intended application.
YOLO26n is a computer vision dataset focused on detecting persons and faces. It is hosted on Kaggle, but its specific scale, annotation details, and creation date are not provided in the available metadata. The dataset's content and structure require verification after download.
YOLO26n Person Face Detection Weights is a pre-trained model for object detection tasks, published on Kaggle. The dataset likely contains the neural network weights for detecting persons and faces in images. Specific details on the training data, model architecture, and performance metrics are not provided in the available metadata.
YOLOv8m model checkpoints trained for 30 epochs on an RGBD dataset. The dataset likely contains RGB and depth images for object detection tasks. It is published on Kaggle, but the specific source, size, and annotation details are not provided in the metadata.
Annotated images of Eastern Cottontail rabbits formatted for use with the YOLO object detection framework. The dataset is hosted on Kaggle and is intended for computer vision tasks. Specific details on the number of images, annotation format, and collection source are not provided in the available metadata.
A set of pretrained weights for the YOLOv8m (You Only Look Once version 8 medium) model, published on Kaggle. The dataset likely contains the model parameters necessary for performing object detection tasks. Specific details on the training data, performance metrics, or version are not provided in the metadata.
Raw images of five denominations of Indian circulation coins: 1, 2, 5, 10, and 20 rupees. The dataset, created by satyamshorrf, was last updated on December 20, 2025, and is intended for computer vision tasks but contains no labels or annotations.
cnn-dataset is a dataset hosted on Kaggle. The platform tags suggest it is related to a pre-trained model, likely for computer vision applications. The dataset's specific content, size, and origin are not detailed in the available metadata.