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fix(ML-BOM): correct broken ImageNet link #119

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2 changes: 1 addition & 1 deletion ML-BOM/en/0x23-Design-Model-Card-Quantitative-Analysis.md
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Expand Up @@ -50,7 +50,7 @@ Benchmarks use standardized datasets to objectively compare model quality, effic

* [Computer Vision](0x90-Appendix-A_Glossary.md#computer-vision) (e.g., digital image or video recognition): These benchmarks measure the performance, accuracy, and efficiency of models in tasks like image classification, object detection, segmentation, and tracking. Some example "vision" datasets include:

* [ImageNet](image-net.org): large-scale dataset for computer vision, featuring over 14 million annotated, high-resolution images across thousands of object categories organized by the [WordNet](https://en.wikipedia.org/wiki/WordNet) hierarchy.
* [ImageNet](https://www.image-net.org): large-scale dataset for computer vision, featuring over 14 million annotated, high-resolution images across thousands of object categories organized by the [WordNet](https://en.wikipedia.org/wiki/WordNet) hierarchy.
* [MathVista](https://huggingface.co/datasets/AI4Math/MathVista): Used to evaluating math reasoning in Visual Contexts. It consists of three datasets, *IQTest*, *FunctionQA*, and *PaperQA*, which are tailored to evaluate visual reasoning on puzzle test figures, algebraic reasoning over functional plots, and scientific reasoning with academic paper figures, respectively.
* [MNIST](https://www.tensorflow.org/datasets/catalog/mnist) (Modified National Institute of Standards and Technology database): a large database of handwritten digits (glyphs) that is commonly used for training various image processing systems.

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