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Há 21 horas · We introduce RepNeXt, a novel model series integrates multi-scale feature representations and incorporates both serial and parallel structural reparameterization (SRP) to enhance network depth and width without compromising inference speed. Extensive experiments demonstrate RepNeXt's superiority over current leading lightweight CNNs and ViTs ...
Há 5 dias · If fine-tuned, SuperAnimal models are 10–100× more data efficient than prior transfer-learning-based approaches. We illustrate the utility of our models in behavioral classification and ...
Há 2 dias · Figure 3 shows the backend interface of ImageNet that MTurk workers see when they accept a task from ImageNet. In this example, workers are instructed to select images that contain the object or depict the concept of a “delta,” defined as a low triangular area of alluvial deposits where a river divides before entering a larger body of water.
Há 23 horas · 06-26-2024. 0 0 48. Intel® Liftoff Program member, Ultralytics is leading the charge in the field of AI and computer vision, being dedicated to developing cutting-edge technologies that transform industries and enhance human capabilities. Utralytics specializes in creating state-of-the-art deep learning models and solutions that empower ...
Há 1 dia · To address these challenges, we introduce "Implicit-Zoo": a large-scale dataset requiring thousands of GPU training days designed to facilitate research and development in this field. Our dataset includes diverse 2D and 3D scenes, such as CIFAR-10, ImageNet-1K, and Cityscapes for 2D image tasks, and the OmniObject3D dataset for 3D vision tasks.
Há 1 dia · Here, we investigate the impact of transfer learning with domain-specific RadImageNet dataset and non-medical ImageNet on the robustness of classifying thyroid nodules into benign and malignant. We retrospectively collected 822 ultrasound images of thyroid nodules of patients who underwent fine needle aspiration in our institute.
Há 1 dia · Neural Architecture Search (NAS) methods are widely employed to address the time-consuming and costly challenges associated with manual operation and design of deep convolutional neural networks (DCNNs). Nonetheless, prevailing methods still encounter several pressing obstacles, including limited network architecture design, excessively lengthy ...
