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Traffic Light Detection Dataset | A Traffic Light Detection Dataset is a collection of images or data specifically curated for the development, training, and evaluation of machine learning algorithms and computer vision models that are designed to detect and recognize traffic lights in various scenarios. This type of dataset plays a crucial role in advancing autonomous driving systems, traffic management, and related applications. | View Detail |
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Coco Car Damage Detection Dataset | A car damage detection dataset is a collection of images or data used for training and evaluating machine learning models and algorithms that aim to detect and assess damage to vehicles. This type of dataset is essential for applications related to insurance claims processing, vehicle inspection, accident analysis, and automotive repair services. | View Detail |
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DeepGlobe Road Extraction Dataset | The DeepGlobe Road Extraction Dataset is a publicly available dataset used in the field of computer vision and machine learning for research related to road and infrastructure detection and segmentation from high-resolution satellite and aerial imagery. It is designed to assist in developing and evaluating algorithms and models for road extraction and mapping. | View Detail |
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Blood Cancer - Image Dataset | A "Blood Cancer - Image Dataset" typically refers to a collection of medical images, such as blood smears or bone marrow samples, that have been used for research and analysis in the field of hematology, particularly for the detection and classification of blood-related cancers, such as leukemia and lymphoma. | View Detail |
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Skin cancer: HAM10000 | The HAM10000 dataset is a publicly available dataset used in the field of dermatology and machine learning for research related to the classification of skin lesions, particularly for the detection of skin cancer. "HAM" stands for "Human Against Machine," signifying the goal of improving automated dermatological diagnoses through machine learning. | View Detail |
 
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Cars Detection | Car detection, often referred to as object detection in the context of computer vision and deep learning, is the task of identifying and locating cars within images or video frames. This is a fundamental task in various applications, including autonomous driving, traffic monitoring, security surveillance, and parking management. | View Detail |
 
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Massachusetts Buildings Dataset | It's possible that such a dataset has been created or become publicly available after that date, or it might not be widely recognized in the field of machine learning or computer vision. | View Detail |
 
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Structural Defects Network (SDNET) 2018 | The Structural Defects Network (SDNET) is a dataset specifically designed for research in the field of computer vision and machine learning, particularly for tasks related to the detection and classification of structural defects in materials, such as concrete. The SDNET 2018 dataset, as the name suggests, was released in 2018 and has been widely used in academic and industrial research for the development and evaluation of algorithms and models for defect detection and analysis. | View Detail |
 
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Flood Area Segmentation | Flood area segmentation is a computer vision and image processing technique used to identify and delineate regions affected by flooding in satellite images, aerial photographs, or other types of remotely sensed imagery. | View Detail |
 
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Semantic Segmentation for Self Driving Cars | Semantic segmentation is a crucial computer vision technique used in the context of self-driving cars to understand and interpret the environment surrounding the vehicle. It involves the process of assigning semantic labels to each pixel in an image or a series of images, effectively segmenting the image into various meaningful objects or regions. | View Detail |
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