All Models

Model for Face Anti-Spoofing Challenge

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.

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.

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.

Face detection is a computer vision technique that involves identifying and locating human faces within an image or video. The goal of face detection is to detect the presence of faces, and draw bounding boxes around them, without necessarily identifying specific facial features or landmarks.

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.

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.

Video to Canny Edge is the process of converting a video into a Canny edge representation, where edges in the video are emphasized and separated. Canny Edge is a popular algorithm in image processing and is often used to detect edges in images and videos.

This is a task focused on enhancing the efficiency of image matching by leveraging lightweight yet highly discriminative features. XFeat employs optimized feature extraction techniques to identify key points and patterns in images, making it suitable for fast and accurate image comparison.

DehazeFormer is a deep learning model designed for single image haze removal. Leveraging a transformer-based architecture, it effectively restores image clarity and contrast by removing haze, making it suitable for applications in autonomous driving, remote sensing, and image enhancement.

Color Harmonization is a computational model designed to adjust and enhance the color balance of an image based on harmony templates, as proposed in the paper by Daniel Cohen-Or et al. The model improves visual aesthetics by aligning image colors with established color harmony principles. It supports multiple harmony templates and can be integrated with user interfaces for visual quality assessment using metrics from interfacemetrics.aalto.fi.

Background Replacement is a powerful tool that enables users to easily change the background of their images, opening up endless possibilities for creative transformations, and visual enhancements.

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.

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.

We present an open-source reproduction of Meta AI's LLaMa 2. However, with significantly reduced model sizes, LiteLlama-460M-1T has 460M parameters trained with 1T tokens.

Archer Image Generator user Archer Diffusion, is a highly specialized Image generation AI Model of type Safetensors / Checkpoint AI Model created by AI community user civitai. Derived from the powerful Stable Diffusion (SD 1.5) model, Archer Diffusion has undergone an extensive fine-tuning process, leveraging the power of a dataset consisting of images generated by other AI models or user-contributed data. This fine-tuning process ensures that Archer Diffusion is capable of generating images that are highly relevant to the specific use-cases it was designed for, such as landscapes, nitrosocke, archer.

Motivation behind this pipeline partly comes from FLAN and partly comes from InstructPix2Pix. The main idea is to first create an instruction prompted dataset (as described in our blog) and then conduct InstructPix2Pix style training. The end objective is to make Stable Diffusion better at following specific instructions that entail image transformation related operations.

Model for Housing Prices Challenge

Model for Movie Reviews Challenge

Model for Pothole Detection Challenge