In a search for the perfect dataset to test segmentation code, the focus is on real-world data primarily composed of categorical features. The ideal dataset should be rich in categorical variables, making it suitable for segmentation analysis. This type of dataset is crucial for developing and refining algorithms that can effectively categorize and segment data based on non-numeric attributes. The emphasis on real-world data ensures that the segmentation code will be applicable and effective in practical scenarios. The dataset’s composition, with a high percentage of categorical features, is essential for achieving accurate and meaningful segmentation results.
Dan Hendrycks
@DanHendrycks
路
Jan 23 We鈥檙e releasing Humanity鈥檚 Last Exam, a dataset with 3,000 questions developed with hundreds of subject matter experts to capture the human frontier of knowledge and reasoning.State-of-the-art AIs get <10% accuracy and are highly overconfident.
@ai_risk
@scaleai
Wevolver
@WevolverApp
路
19h Crack Segmentation Using
@Ultralytics
YOLO11YOLO11’s instance segmentation capabilities can be applied to detect and segment cracks on roads, walls, and other construction surfaces. This can support tasks such as infrastructure monitoring and building inspections by helping
Muhammad Rizwan Munawar
@muhammdrizwanmr
路
7h Segment plants using
@ultralytics
YOLO11 +
@AIatMeta
SAM 2No manual segmentation, I used SAM2 for auto-annotation and later trained the YOLO11 model on the dataset.Learn more https://docs.ultralytics.com/tasks/segment/#plants #segmentation #ai #ml
Vincent Granville
@granvilleDSC
路
4h Universal Dataset to Test, Enhance and Benchmark AI Algorithms https://mltblog.com/4ia7r2DThis scientific research has three components. First, my most recent advances towards solving one of the most famous, multi-century old conjectures in number theory. One that kids in
Ali
@alibrahimzada
路
Apr 5 AlphaTrans decomposes source unit tests to address the “test translation coupling effect” problem. Test decomposition unburdens validation of fragments from incorrect translations. 62.41% of test fragments for unit tests that would have been marked as failed achieves a test pass.
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