19,997 machine learning datasets
19,997 dataset results
Xavier Robin, Juergen Haas, Rafal Gumienny, Anna Smolinski, Gerardo Tauriello, and Torsten Schwede.Continuous automated model evaluation (cameo)—perspectives on the future of fully automated evaluation of structure prediction methods.Proteins: Structure, Function, and Bioinformatics, 89:1977–1986, 12 2021.ISSN 0887-3585.doi: 10.1002/prot.26213.
Our networks are saved in GEXF as follows:
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Traditional Chinese medicinal plants are often used to prevent and treat diseases for the human body. Since various medicinal plants have different therapeutic effects, plant recognition becomes an important topic. Traditional identification of medicinal plants mainly relies on human experts, which does not meet the increased requirements in clinical practice. Artificial Intelligence (AI) research for plant recognition faces challenges due to the lack of a comprehensive medicinal plant dataset. Therefore, we present a Chinese medicinal plant dataset that including 52089 images in 300 categories. Compared to the existing medicinal plant datasets, our dataset has more categories and fine-grained plant parts to facilitate comprehensive plant recognition. The plant images were collected through the Bing search engine and cleaned by a pretrained vision foundation model with human verification. Our dataset promotes the development and validation of advanced AI models for robust and accurate
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About
睡眠
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We introduce FortisAVQA, a dataset designed to assess the robustness of AVQA models. Its construction involves two key processes: rephrasing and splitting. Rephrasing modifies questions from the test set of MUSIC-AVQA to enhance linguistic diversity, thereby mitigating the reliance of models on spurious correlations between key question terms and answers. Splitting entails the automatic and reasonable categorization of questions into frequent (head) and rare (tail) subsets, enabling a more comprehensive evaluation of model performance in both in-distribution and out-of-distribution scenarios.
The Aachen-Heerlen annotated steel microstructure dataset comprises 1,705 scanning electron microscopy (SEM) images of bainitic steel samples. Each image is annotated with expert-delineated polygons highlighting martensite-austenite (MA) islands—complex blocky structures that significantly influence the mechanical properties of steel. Additionally, the dataset includes metadata detailing the chemical composition, transformation temperatures, and cooling rates of the steel samples.
This dataset includes 120 simulations of 10 loops of a 50 MW parabolic-trough solar plant with varying solar irradiances, optical efficiencies, thermal losses, ambient temperatures, input temperatures, and sector flow rates. The simulations are based on a static version of the concentrated-parameter model, as described in the corresponding article.
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