Models

> Multilang Express Translator is a lightweight, production-ready multilingual translation API that instantly translates any input text into 5 major European languages: English, French, Spanish, German, and Italian. Powered by Helsinki-NLP’s efficient open-source models, this AI model is designed for content creators, e-commerce sellers, legal professionals, and SaaS developers who need fast, accurate translations across multiple markets.

Model for Face Anti-Spoofing Challenge

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.

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.

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.

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.

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Model for Spaceship Titanic Challenge

Model for Movie Reviews Challenge

Model for Housing Prices Challenge

Fine-tuned version of BaseAlpha (a92b6157) optimized for media recommendation systems. Handles multimodal inputs (video, image, text) with 15% improved accuracy on media-specific benchmarks. Suitable for content personalization and engagement prediction.

Fine-tuned version of BaseAlpha optimized for media recommendation tasks. Supports multimodal inputs and dynamic content filtering

Demo model

Northern Lights

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Low light Image Enhancement is a task focused on improving the quality and visibility of images captured in low-light conditions. This task involves applying image processing techniques and algorithms to enhance details, reduce noise, and increase brightness in photos taken in dimly lit environments.

Image Super-Resolution with SeemoRe is a task aimed at improving the process of image super-resolution by leveraging expertise in the field. This task involves incorporating techniques that identify and utilize expert knowledge or specialized information to enhance the efficiency and accuracy of image upscaling.

Image Super-Resolution with SMFANet involves utilizing the SMFANet model architecture to enhance the resolution and quality of images. SMFANet is a deep learning network designed for super-resolution tasks, aiming to generate high-quality, detailed images from low-resolution inputs.

Semantic-Guided Low-Light Network is a task that integrates semantic information into the process of enhancing the quality of images captured in low-light conditions. By incorporating semantic guidance, this task aims to improve the accuracy and effectiveness of enhancing low-light images by considering the context and content of the scene.

Image Blending with Multiple Methods is a task that involves combining two or more images seamlessly to create a composite image using a variety of blending techniques. By leveraging multiple blending methods, such as alpha blending, gradient blending, or Laplacian pyramid blending, this task enables the merging of images while preserving the visual coherence and integrity of the final composition.