ImageNetiin

ImageNetiin is a large-scale image dataset and benchmark proposed as an extension of the ImageNet project. It is designed to broaden the scope of visual recognition research by providing higher-resolution imagery, more diverse categories, and multilingual annotations to support both vision and multimodal tasks.

Dataset composition includes images organized into a hierarchical taxonomy inspired by WordNet, with thousands of fine-grained classes. In addition to image-level labels, ImageNetiin offers object localization data in the form of bounding boxes and segmentation masks for a subset of images. The dataset also includes captions and metadata in multiple languages, enabling multilingual evaluation and cross-lingual transfer learning.

Data collection and governance aim to balance scale with quality and inclusivity. Images are sourced from publicly available datasets and licensed resources, with quality control through crowd-sourced labeling, automated verification, and expert review. Ethical considerations address bias, privacy, and licensing compliance.

Usage and evaluation protocols support a range of computer vision tasks, including image classification, object detection, segmentation, and multimodal captioning. Researchers use ImageNetiin to train models, assess generalization, and compare architectures under standardized benchmarks.

Impact and considerations: If realized, ImageNetiin would complement ImageNet by offering higher-resolution data and multilingual annotations, facilitating more realistic and globally accessible research. Potential challenges include data licensing complexity, annotation quality, and biases inherent in large-scale crawled datasets.