# Feature Extraction

Kats is a time series analysis toolkit developed by Facebook's Infrastructure Data Science Team, aimed at providing a one-stop solution for data science and engineering tasks. It supports a range of functions from understanding key statistics and features, detecting regressions and anomalies, to forecasting future trends. The main advantages of Kats include its lightweight nature, ease of use, and scalability, making it suitable for data analysts and engineers across various industries and fields.

InternViT-6B-448px-V2_5 is a visual model built upon InternViT-6B-448px-V1-5, which enhances the visual encoder's ability to extract visual features by utilizing ViT incremental learning and NTP loss (Stage 1.5). It particularly excels in domains where representation is lacking in large-scale network datasets, such as multilingual OCR data and mathematical charts. This model is part of the InternVL 2.5 series, maintaining the same 'ViT-MLP-LLM' architecture as its predecessor, while integrating the newly incrementally pretrained InternViT alongside various pretrained LLMs, including InternLM 2.5 and Qwen 2.5, utilizing a randomly initialized MLP projector.

Jina-clip-v2 is a multilingual multimodal embedding model developed by Jina AI, supporting image retrieval in 89 languages, capable of processing images at a resolution of 512x512. It offers output dimensions ranging from 64 to 1024 to meet diverse storage and processing needs. The model combines the powerful text encoder Jina-XLM-RoBERTa and the visual encoder EVA02-L14, creating aligned representations of images and texts through joint training. Jina-clip-v2 excels in multimodal search and retrieval, especially in breaking language barriers and providing cross-modal understanding.