Background Removal is an image processing technique used to separate the main object from the background of a photo. Removing the background helps highlight the product, subject, or character, bringing a professional and aesthetically pleasing look to the image.

Color Extraction is a task in computer vision that involves the extraction and analysis of colors from images or videos. The objective of this task is to identify and isolate specific colors or color ranges present in the visual data.

The goal of Image To Anime was to create a new version of the image that would possess the same clean lines and evoke the characteristic feel found in anime productions, capturing the unique artistry and aesthetics associated with this style.

ZeroShot Image Classification CLIP is a task in the field of machine learning and image processing, aiming to predict the class or label of an image that has not been previously classified, in a dataset that the model has not been trained on with those classes.

Image Restoration is a compute vision task which restoring from the degraded images to clean images.

Anime backgrounds, also known as anime backgrounds art or anime scenery, refer to the visual elements that form the backdrop of animated scenes in anime. These backgrounds are carefully designed and illustrated to provide the setting, atmosphere, and context for the characters and events within the anime.

This is an important task in the fields of natural language processing and computer vision. It involves answering questions based on the content of a text document in the form of an image.

Zero-shot text classification is a technique used in natural language processing (NLP) to classify text into predefined categories without requiring any labeled training data for those specific categories.

Face mesh detection, also known as facial landmark detection or face pose estimation, is the task of identifying and localizing specific keypoints or landmarks on a human face. It involves detecting the positions of facial features, such as eyes, eyebrows, nose, mouth, and jawline, in an image or video.

Named Entity Recognition with BERT utilizes cutting-edge technology to accurately identify and categorize named entities in textual data. By leveraging BERT's advanced capabilities, this tool streamlines information extraction processes by recognizing entities like names of individuals, organizations, and locations within text, enhancing text analysis efficiency.

MINDS-14 is a dataset designed for the intent detection task with spoken data. It encompasses 14 distinct intents extracted from a commercial system in the e-banking domain.

LongBench is a comprehensive benchmark for multilingual and multi-task purposes, with the goal to fully measure and evaluate the ability of pre-trained language models to understand long text

The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified Good and Featured articles on Wikipedia.

MathVista: Diverse benchmark for mathematical reasoning in visual contexts. Includes 6,141 examples from 31 datasets.