19,997 machine learning datasets
19,997 dataset results
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Description: The ZakynthosTurtles dataset has been designed to support the development of numerical methods for the recognition and re-identification of individual sea turtles based on their unique scale patterns. Each turtle’s distinct scale arrangement provides a natural identifier, allowing researchers to track and study individuals over time. This dataset contains photographs captured in the wild, emphasizing the importance of non-intrusive wildlife monitoring techniques. The dataset includes 40 individual loggerhead sea turtles, with each turtle represented by four high-resolution photographs—both left and right profiles taken in two different years. This structure offers unique opportunities for investigating the stability and similarity of scale patterns over time and between opposite profiles.
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With the gradual maturity of UAV technology, it can provide extremely powerful support for smart agriculture and precise monitoring. Currently, there is no dataset related to green walnuts in the field of agricultural computer vision. Therefore, in order to promote the algorithm design in the field of agricultural computer vision, we used UAV to collect remote sensing data from 8 walnut sample plots. Considering that green walnuts have the characteristics of being affected by various lighting conditions and being occluded, we constructed a large-scale dataset with a higher fine-grained target feature - WalnutData. This dataset contains a total of 30,240 images and 7,062,080 instances, and there are 4 target categories: illuminated from the front and not occluded (A1), backlit and not occluded (A2), illuminated from the front and occluded (B1), and backlit and occluded (B2).
opendateset
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ViDoSeek, a benchmark specifically designed for visually rich document retrieval-reason-answer, fully suited for evaluation of RAG within large document corpus.
We introduce MMKE-Bench, a benchmark designed to evaluate the ability of LMMs to edit visual knowledge in real-world scenarios. MMKE-Bench incorporates three editing tasks: visual entity editing, visual semantic editing, and user-specific editing. Additionally, it uses free-form natural language to represent and edit knowledge, offering more flexibility.The benchmark consists of 2,940 pieces of knowledge and 8,363 images across 33 broad categories, with evaluation questions automatically generated and human-verified.