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.

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.

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.

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.

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

Extract sections of images from your image by using OpenAI's CLIP and YoloSmall.

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.

Artwork Image Generator is an artificial intelligence model designed to generate artistic images in various styles.

Object Classification, also known as Object Recognition, is a computer vision task that involves identifying and categorizing objects within an image or a video frame. The goal is to train a model to recognize and assign labels to different objects or classes present in the visual data.

The main aim for this dataset is to cover a wide variety of social biases that are implied in text, both subtle and overt, and make the biases representative of real world discrimination that people experience RWJF 2017.

PathVQA consists of 32,799 open-ended questions from 4,998 pathology images where each question is manually checked to ensure correctness.

Cifar-10 is an important resource in the field of image recognition and classification, used to train and test machine learning models and neural networks.

TAL-SCQ5K are high-quality mathematical competition datasets created by TAL Education Group.