datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Infant-Sleep-Posture-Recognition-Image-Dataset
Infant Sleep Posture Recognition Image Dataset
Currently, there are many challenges in improving infant sleep safety, including the difficulty of assessing posture and monitoring sleep status without affecting normal infant sleep. Existing monitoring equipment often relies on clothing sensors, which may cause discomfort and excessive interference. This dataset aims to address the risk of asphyxiation caused by incorrect infant sleep posture through visual recognition technology… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Infant-Sleep-Posture-Recognition-Image-Dataset.Amusement-Park-Game-Facility-Recognition-Image-Dataset
Amusement Park Game Facility Recognition Image Dataset
In the retail e-commerce sector, as consumer demand for amusement park facilities increases, merchants face challenges in quickly identifying and managing various facilities. Existing image recognition technologies still lack in accuracy and speed, especially in scenarios with diverse facility combinations. This dataset aims to enhance the precision and efficiency of amusement park facility recognition, satisfying the business… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Amusement-Park-Game-Facility-Recognition-Image-Dataset.Pansy-Recognition-Image-Dataset
Pansy Recognition Image Dataset
Currently, garden management faces the challenge of efficiently and accurately identifying flower varieties. Traditional manual identification relies on experience and is inefficient. Existing image recognition technologies still need improvement in the accuracy of specific flower types, especially in complex backgrounds. This dataset aims to address common accuracy deficiencies in pansy recognition by providing a large number of high-quality images… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Pansy-Recognition-Image-Dataset.Strawflower-Recognition-Image-Dataset
Strawflower Recognition Image Dataset
In the agriculture, forestry, and fisheries domains, horticultural management and plant care face significant challenges. Improving accuracy and efficiency through automation and intelligent systems is a crucial transformation direction for the industry. Existing solutions often use traditional manual recognition or simple feature matching methods, which are easily affected by external environments and are inefficient. This dataset aims to solve… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Strawflower-Recognition-Image-Dataset.Infant-Sleep-Posture-Recognition-Image-Dataset
Infant Sleep Posture Recognition Image Dataset
Currently, there are many challenges in improving infant sleep safety, including the difficulty of assessing posture and monitoring sleep status without affecting normal infant sleep. Existing monitoring equipment often relies on clothing sensors, which may cause discomfort and excessive interference. This dataset aims to address the risk of asphyxiation caused by incorrect infant sleep posture through visual recognition technology… See the full description on the dataset page: https://huggingface.co/datasets/shangzx/Infant-Sleep-Posture-Recognition-Image-Dataset.Ornamental-Flowers-Plum-Recognition-Image-Dataset
Ornamental Flowers Plum Recognition Image Dataset
In the current field of agriculture, forestry, and fisheries, plant recognition, especially the recognition of plum varieties, faces challenges of low efficiency and insufficient accuracy in manual recognition. Existing solutions largely depend on human experience and simple image retrieval, which are inadequate to meet the recognition needs under complex varieties and environments. This dataset aims to enhance the automation and… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Ornamental-Flowers-Plum-Recognition-Image-Dataset.Garden-Plant-Tamarisk-Recognition-Image-Dataset
Garden Plant Tamarisk Recognition Image Dataset
In agricultural and garden management, accurately identifying and classifying flowers is an important and challenging task. Traditional manual recognition methods are inefficient, easily influenced by the professional capability of the identifier, and difficult to promote in large-scale applications. Existing automated solutions, though somewhat effective, are often limited by insufficient data and inaccurate annotations. The… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Garden-Plant-Tamarisk-Recognition-Image-Dataset.Garden-Flower-Conical-Hydrangea-Image-Recognition-Dataset
Garden Flower Conical Hydrangea Image Recognition Dataset
With the rapid development of the landscaping industry, garden plants, especially flowers, have a wide variety, making accurate identification and classification a major challenge for the industry. Existing manual identification and traditional image recognition methods have shortcomings such as being time-consuming and having low accuracy. The construction of this dataset aims to enhance the accuracy and efficiency of… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Garden-Flower-Conical-Hydrangea-Image-Recognition-Dataset.Pansy-Recognition-Image-Dataset
Pansy Recognition Image Dataset
Currently, garden management faces the challenge of efficiently and accurately identifying flower varieties. Traditional manual identification relies on experience and is inefficient. Existing image recognition technologies still need improvement in the accuracy of specific flower types, especially in complex backgrounds. This dataset aims to address common accuracy deficiencies in pansy recognition by providing a large number of high-quality images… See the full description on the dataset page: https://huggingface.co/datasets/shangzx/Pansy-Recognition-Image-Dataset.Pharmacy-Drug-Image-Recognition-Dataset
Pharmacy Drug Image Recognition Dataset
The dataset is characterized by 99% annotation accuracy and 95% annotation consistency. It uses innovative multimodal image enhancement technology and quality assessment methods specific to medical images, ensuring excellent performance and a 92% recognition accuracy in various usage environments. In comparison with other medical image datasets, this dataset stands out with its refined category annotations and diverse collection conditions… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Pharmacy-Drug-Image-Recognition-Dataset.Face-Recognition-Image-Dataset
Face Recognition Image Dataset
Currently, face recognition is widely used in smart devices, but factors such as environmental changes and lighting effects pose challenges to recognition accuracy. Existing datasets often lack diversity and annotation quality, limiting the algorithm's performance improvement. This dataset aims to improve the accuracy of recognition algorithms by providing a rich diversity of high-quality face images. During data collection, various types of camera… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Face-Recognition-Image-Dataset.Pomegranate-Fruit-Recognition-Image-Dataset-for-Garden-Flowers
Pomegranate Fruit Recognition Image Dataset for Garden Flowers
In the current agricultural sector, efficiently recognizing and managing garden plants, particularly pomegranate fruits, is a significant challenge. Conventional manual recognition and management methods are time-consuming, labor-intensive, and have low accuracy. The application of existing image recognition technologies in complex environments still faces many bottlenecks. The construction of this dataset aims to solve… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Pomegranate-Fruit-Recognition-Image-Dataset-for-Garden-Flowers.Garden-Floral-and-Flower-Tobacco-Recognition-Image-Dataset
Garden Floral and Flower Tobacco Recognition Image Dataset
In modern agriculture and garden management, swiftly and accurately recognizing and classifying flower species is a major challenge. Traditional manual recognition methods are inefficient and prone to errors. Existing solutions rely on general image datasets, which often lack the precision and diversity specific to flower recognition. This dataset focuses on enhancing the recognition capabilities of flowers and flower… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Garden-Floral-and-Flower-Tobacco-Recognition-Image-Dataset.Amaryllis-Image-Recognition-Dataset
Amaryllis Image Recognition Dataset
Currently, with the rapid development of the horticulture industry, the diversification and increase in the number of flower varieties pose challenges to flower management. Current solutions such as manual recognition and recording are inefficient and prone to errors. The establishment of the Amaryllis Image Recognition Dataset aims to leverage advanced image recognition technology to achieve fast and accurate flower classification, optimizing… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Amaryllis-Image-Recognition-Dataset.Garden-Flower-Digitalis-Recognition-Image-Dataset
Garden Flower Digitalis Recognition Image Dataset
In modern horticultural management, accurate identification and monitoring of garden plant species is crucial. However, existing techniques often face issues such as insufficient image resolution and the time-consuming and inaccurate nature of manual recognition. This dataset aims to improve the accuracy and efficiency of automated recognition by providing high-quality images of Digitalis. The images in the dataset were collected… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Garden-Flower-Digitalis-Recognition-Image-Dataset.Rice-Tillering-Recognition-Image-Dataset
Rice Tillering Recognition Image Dataset
The core advantage of this dataset lies in its high quality and high precision annotations, with accuracy exceeding 95%. It provides multi-angle, consistent image data. By introducing advanced semi-automatic annotation techniques and data augmentation techniques such as flipping, rotation, and color adjustment, the diversity and practicality of the dataset are enhanced. When applied to rice tillering recognition tasks, this dataset has… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Rice-Tillering-Recognition-Image-Dataset.