AI image detection and recognition

Image Describer is a tool that uses artificial intelligence technology to upload images and output image descriptions according to user needs. It understands image content and generates detailed descriptions or explanations to help users better understand the meaning of the image. This tool is not only suitable for ordinary users, but also helps visually impaired people understand the content of pictures through text-to-speech function. The importance of the image description generator lies in its ability to improve the accessibility of image content and enhance the efficiency of information dissemination.

Viewly is a powerful AI image recognition application that can identify the content in images, compose poems and translate them into multiple languages ​​through AI technology. It represents the current cutting-edge technology of artificial intelligence in the fields of image recognition and language processing. Its main advantages include high recognition accuracy, multi-language support and creative AI poetry writing functions. Viewly’s background information shows that it is a continuously updated product dedicated to providing users with more innovative features. Currently, the product is available to users for free.

PimEyes is a website that uses facial recognition technology to provide a reverse image search service. Users can upload photos to find pictures or personal information on the Internet that are similar to the photo. This service is valuable in protecting privacy, locating missing persons, and verifying copyrights. Through its advanced algorithms, PimEyes provides users with a powerful tool to help them find and identify images on the web.

Ultralytics YOLO11 is a further development of previous YOLO series models, introducing new features and improvements to increase performance and flexibility. YOLO11 is designed to be fast, accurate, and easy to use, making it ideal for a wide range of object detection, tracking, instance segmentation, image classification, and pose estimation tasks.

Revisit Anything is a visual location recognition system that uses image fragment retrieval technology to identify and match locations in different images. It combines SAM (Spatial Attention Module) and DINO (Distributed Knowledge Distillation) technologies to improve the accuracy and efficiency of visual recognition. This technology has important application value in fields such as robot navigation and autonomous driving.

Joy Caption Alpha One is an AI-based image caption generator that converts image content into text descriptions. It leverages deep learning technology to generate accurate and vivid descriptions by understanding objects, scenes, and actions in images. This technology is important in assisting visually impaired people to understand image content, enhance image search capabilities, and improve the accessibility of social media content.

OpenCV is a cross-platform open source computer vision and machine learning software library that provides a range of programming functions, including but not limited to image processing, video analysis, feature detection, machine learning, etc. This library is widely used in academic research and commercial projects, and is favored by developers because of its powerful functionality and flexibility.

GOT-OCR2.0 is an open source OCR model that aims to promote optical character recognition technology towards OCR-2.0 through a unified end-to-end model. This model supports a variety of OCR tasks, including but not limited to ordinary text recognition, formatted text recognition, fine-grained OCR, multi-crop OCR and multi-page OCR. It is based on the latest deep learning technology and can handle complex text recognition scenarios with high accuracy and efficiency.

bonding_w_geimini is an image processing application developed based on the Streamlit framework. It allows users to upload pictures, perform object detection through the Gemini API, and draw the bounding box of the object directly on the picture. This application uses machine learning models to identify and locate objects in pictures, which is of great significance to fields such as image analysis, data annotation, and automated image processing.

clip-image-search is an image search tool based on Open AI's pre-trained CLIP model, capable of retrieving images through text or image queries. CLIP models are trained to map images and text into the same latent space, allowing comparison through similarity measures. The tool uses images from the Unsplash dataset and utilizes Amazon Elasticsearch Service for k-nearest neighbor search. It deploys query services through AWS Lambda functions and API gateways, and the front end is developed using Streamlit.

Segment Anything 2 for Surgical Video Segmentation is a surgical video segmentation model based on Segment Anything Model 2. It uses advanced computer vision technology to automatically segment surgical videos to identify and locate surgical tools, improving the efficiency and accuracy of surgical video analysis. This model is suitable for various surgical scenarios such as endoscopic surgery and cochlear implant surgery, and has the characteristics of high accuracy and high robustness.

SAM-guided Graph Cut for 3D Instance Segmentation is a deep learning method that utilizes 3D geometry and multi-view image information for 3D instance segmentation. This method effectively utilizes 2D segmentation models for 3D instance segmentation through a 3D to 2D query framework, constructs superpoint graphs through graph cut problems, and achieves robust segmentation performance for different types of scenes through graph neural network training.

SA-V Dataset is an open-world video dataset designed for training general object segmentation models, containing 51K diverse videos and 643K spatio-temporal segmentation masks (masklets). This dataset is used for computer vision research and is allowed to be used under the CC BY 4.0 license. Video content is diverse and includes topics such as places, objects, and scenes, with masks ranging from large-scale objects such as buildings to details such as interior decorations.

Segment Anything Model 2 (SAM 2) is a visual segmentation model launched by FAIR, the AI ​​research department of Meta Corporation. It implements real-time video processing through a simple transformer architecture and streaming memory design. The model builds a model loop data engine through user interaction, collecting SA-V, the largest video segmentation dataset to date. SAM 2 is trained on this dataset and provides strong performance across a wide range of tasks and vision domains.

Meta Segment Anything Model 2 (SAM 2) is a next-generation model developed by Meta for real-time, promptable object segmentation in videos and images. It achieves state-of-the-art performance and supports zero-shot generalization, i.e., no need for custom adaptation to apply to previously unseen visual content. The release of SAM 2 follows an open science approach, with the code and model weights shared under the Apache 2.0 license, and the SA-V dataset also shared under the CC BY 4.0 license.

RapidLayout is an open source tool that focuses on document image layout analysis. It can analyze the layout structure of document category images and locate various parts such as titles, paragraphs, tables, and pictures. It supports layout analysis in multiple languages ​​and scenarios, including Chinese and English, and can meet the needs of different business scenarios.

roboflow/sports is an open source computer vision toolset focusing on applications in the sports field. It utilizes advanced image processing technologies such as object detection, image segmentation, key point detection, etc. to solve challenges in sports analysis. This toolset was developed by Roboflow to promote the application of computer vision technology in the sports field and is continuously optimized through community contributions.

Album AI is an experimental project that uses gpt-4o-mini as a visual model to automatically identify the metadata of image files in albums, and utilizes RAG technology to achieve dialogue with the album. It can be used as a traditional photo album or as an image knowledge base to assist large language models in content generation.

TruthPix is ​​an AI image detection tool designed to help users identify photos that have been tampered with by AI. Through advanced AI technology, this application can quickly and accurately identify traces of cloning and tampering in images, thereby preventing users from being misled by false information on social media and other platforms. The main advantages of this application include: high security, all detection is completed on the device, no data is uploaded; detection speed is fast, it only takes less than 400 milliseconds to analyze an image; it supports a variety of AI-generated image detection technologies, such as GANs, Diffusion Models, etc.

MASt3R is an advanced model for 3D image matching developed by Naver Corporation, which focuses on improving geometric 3D vision tasks in the field of computer vision. This model utilizes the latest deep learning technology and can achieve accurate 3D matching between images through training, which is of great significance to fields such as augmented reality, autonomous driving, and robot navigation.

image-textualization is an automated framework for generating rich and detailed image descriptions. The framework leverages deep learning technology to automatically extract information from images and generate accurate and detailed description text. This technology has important application value in areas such as image recognition, content generation and assisting the visually impaired.

HunyuanCaptioner is a text-to-image technology model based on LLaVA that can generate text descriptions that are highly consistent with images, including object descriptions, object relationships, background information, image styles, etc. It supports single-graph and multi-graph reasoning in Chinese and English, and can be demonstrated locally through Gradio.

Florence-2 is an advanced vision-based model developed by Microsoft that uses a cue-based approach to handle a wide range of visual and visual-linguistic tasks. The model is able to interpret simple textual cues and perform tasks such as image description, object detection and segmentation. It leverages the FLD-5B dataset, which contains 5.4 billion annotations covering 126 million images and is proficient in multi-task learning. Its sequence-to-sequence architecture enables it to perform well in both zero-shot and fine-tuned settings, proving to be a competitive vision-based model.

Florence-2-large is an advanced vision-based model developed by Microsoft that uses a cue-based approach to handle a wide range of visual and visual-linguistic tasks. The model is able to interpret simple textual cues to perform tasks such as image description, object detection and segmentation. It leverages the FLD-5B dataset containing 540 million images with 5.4 billion annotations and is proficient in multi-task learning. Its sequence-to-sequence architecture enables it to perform well in both zero-shot and fine-tuned settings, proving to be a competitive vision-based model.

emo-visual-data is a public emoticon visual annotation data set. It collects 5329 emoticons through visual annotation completed using the glm-4v and step-free-api projects. This dataset can be used to train and test large multi-modal models and is of great significance for understanding the relationship between image content and text description.

Grounding DINO 1.5 is a series of advanced models developed by IDEA Research to push the boundaries of open-world object detection technology. The series consists of two models: Grounding DINO 1.5 Pro and Grounding DINO 1.5 Edge, which are optimized for a wide range of application scenarios and edge computing scenarios respectively.

PaliGemma is an advanced visual language model released by Google. It combines the image encoder SigLIP and the text decoder Gemma-2B, which can understand images and text, and achieve interactive understanding of images and text through joint training. This model is designed for specific downstream tasks, such as image description, visual question answering, segmentation, etc., and is an important tool in the field of research and development.

AI Image Description Generator is an image description generator based on ERNIE 3.5 or GEMINI-PRO-1.5 API, which can accurately extract the key elements in the image and interpret the creative intention behind it. It supports multiple languages, integrates with the clerk.com user management platform, and uses Next.js to build full-stack web applications. This technology is widely used in the fields of scientific research, artistic creation, and mutual search between images and text.

ImageInWords (IIW) is a human-involved iterative annotation framework for curating hyper-detailed image descriptions and generating a new dataset. This dataset achieves state-of-the-art results by evaluating automated and human-parallelism (SxS) metrics. The IIW dataset significantly improves upon previous datasets and GPT-4V output in generating descriptions across multiple dimensions, including readability, comprehensiveness, specificity, hallucination, and human-likeness. Furthermore, models fine-tuned using IIW data performed well in text-to-image generation and visual language reasoning, able to generate descriptions that were closer to the original images.

ImagenHub is a one-stop library for standardizing the inference and evaluation of all conditional image generation models. The project began by defining seven salient tasks and creating a high-quality evaluation dataset. Second, we build a unified inference pipeline to ensure fair comparison. Third, we design two manual evaluation metrics, namely semantic consistency and perceptual quality, and develop comprehensive guidelines to evaluate the generated images. We train expert reviewers to evaluate model outputs based on proposed metrics. This manual evaluation achieved high inter-rater agreement on 76% of the models. We comprehensively evaluate about 30 models and observe three key findings: (1) The performance of existing models is generally unsatisfactory, with 74% of models scoring below 0.5 overall, except for text-guided image generation and topic-driven image generation. (2) We checked the claims in published papers and found that 83% of the claims were correct. (3) Except for topic-driven image generation, none of the existing automatic evaluation metrics have a Spearman correlation coefficient higher than 0.2. In the future, we will continue our efforts to evaluate newly released models and update the leaderboard to track progress in the field of conditional image generation.

Scenic is a code library focused on computer vision research based on attention models. It provides functions such as optimized training and evaluation loops, baseline models, etc., and is suitable for multi-modal data such as images, videos, and audios. Provide SOTA models and baselines to support rapid prototyping at a free price.

SPRIGHT is a large-scale visual language dataset and model focusing on spatial relationships. It builds the SPRIGHT dataset by re-describing 6 million images, significantly increasing the number of spatial phrases in the descriptions. The model was fine-tuned and trained on 444 images containing a large number of objects to optimize the generation of images with spatial relationships. SPRIGHT achieves state-of-the-art spatial consistency across multiple benchmarks while improving image quality scores.

ComfyUI-PixelArt-Detector is an open source tool for detecting pixel art, which can be integrated into ComfyUI to help users identify and process pixel art images.

Griffon is the first high-resolution (over 1K) LVLM with localization capabilities that can describe everything in your area of ​​interest. In its latest version, Griffon supports visual language coreferences. You can enter an image or some description. Griffon excels in REC, object detection, object counting, visual/phrase localization, and REG. Pricing: Free trial.

Magi is a model for automatically generating text records for comics. It is able to detect characters, text blocks and panels in comics and arrange them in the correct order. Additionally, the model is able to cluster characters, match text with its corresponding speakers, and perform OCR to extract text.

The Extreme Space AI Laboratory is a new feature in the home private cloud product launched by Beijing Zenith Star Intelligent Information Technology Co., Ltd. It includes functions such as natural language search, similar image search, and image text recognition, aiming to help users manage and use images stored in JiSpace more quickly.

Yolov9 is an implementation of the YOLOv9 paper that uses programmable gradient information to learn what the user wants to learn. This project is an open source deep learning model, mainly used for target detection tasks, with the advantages of efficiency and accuracy.

YOLOv8 is the latest version of the YOLO series of target detection models, which can accurately and quickly identify and locate multiple objects in images or videos, and track their movement in real time. Compared with previous versions, YOLOv8 has greatly improved detection speed and accuracy, and supports a variety of additional computer vision tasks, such as instance segmentation, pose estimation, etc. YOLOv8 can be deployed on different hardware platforms in a variety of formats, providing a one-stop end-to-end target detection solution.

Vision Arena is an open source platform created by Hugging Face for testing and comparing the effects of different computer vision models. It provides a friendly interface that allows users to upload images and process them through different models to visually compare the quality of the results. The platform is pre-installed with mainstream image classification, object detection, semantic segmentation and other models, and also supports custom models. The key advantages are that it is open source and free, easy to use, supports multi-model parallel testing, and is conducive to model effect evaluation and selection. It is suitable for computer vision R&D personnel, algorithm engineers and other roles, and can accelerate the experiment and optimization of computer vision models.

JoyTag is an advanced AI visual model for labeling images with a focus on sex positivity and inclusivity. Using Danbooru tag mode, it is suitable for various images from hand drawings to photography. Supports multi-label classification with more than 5000 labels, can be used for automatic image annotation, and is suitable for a wide range of applications such as training diffusion models lacking text pairs. The model has excellent performance, is based on ViT architecture, and uses CNN stem and GAP header.

YOLO-World is an advanced real-time open vocabulary object detector based on the You Only Look Once (YOLO) series of detectors, with enhanced open vocabulary detection capabilities through visual-language modeling and pre-training on large-scale data sets. It adopts a new reparameterizable visual-linguistic path aggregation network (RepVL-PAN) and region-text contrastive loss to facilitate the interaction between visual and linguistic information. YOLO-World efficiently detects various objects in a zero-shot manner with high efficiency. On the challenging LVIS dataset, YOLO-World achieves 35.4 AP and 52.0 FPS on V100, outperforming many state-of-the-art methods in both accuracy and speed. Furthermore, the fine-tuned YOLO-World performs well on multiple downstream tasks, including object detection and open vocabulary instance segmentation.

Yi-VL-34B is an open source version of the Yi Visual Language (Yi-VL) model, a multi-modal model capable of understanding and recognizing images and conducting multiple rounds of conversations about images. Yi-VL performs well in the latest benchmarks, ranking first in both MMM and CMMMU benchmarks.

SPARC is a simple method for image-text pair pre-training, aiming to pre-train more fine-grained multi-modal representations from image-text pairs. It leverages sparse similarity measures and grouping image patches and language tags to learn representations that simultaneously encode global and local information through contrastive fine-grained sequence losses and contrastive losses between global image and text embeddings. SPARC shows improvements on both image-level tasks with coarse-grained information and region-level tasks with fine-grained information, including classification, retrieval, object detection, and segmentation. In addition, SPARC improves the model's credibility and image description capabilities.

VMamba is a visual state space model that combines the advantages of convolutional neural networks (CNNs) and visual transformers (ViTs) to achieve linear complexity without sacrificing global perception. The Cross-Scan module (CSM) is introduced to solve the problem of direction sensitivity, which can show excellent performance in various visual perception tasks. As the image resolution increases, it shows more significant advantages over existing benchmark models.

GenSAM is an approach to camouflage object detection (COD) that uses Cross-modal Chains of Thought Prompting (CCTP) technology to understand visual cues and leverages universal text cues to obtain reliable visual cues. This method automatically generates and optimizes visual cues at test time through Progressive Mask Generation (PMG) without additional training, achieving efficient and accurate camouflage target segmentation.

Open-Vocabulary SAM is a vision-based model based on SAM and CLIP, focusing on interactive segmentation and recognition tasks. It implements a unified framework of SAM and CLIP through two unique knowledge transfer modules, SAM2CLIP and CLIP2SAM. Extensive experiments on various datasets and detectors show that Open-Vocabulary SAM is more effective in segmentation and recognition tasks, significantly outperforming naive baselines that simply combine SAM and CLIP. Furthermore, combined with training on image classification data, the method can segment and identify approximately 22,000 categories.

Pose Anything is a general graph-based pose estimation method designed to make keypoint localization applicable to arbitrary object classes, using a single model and requiring a minimum of supporting images with annotated keypoints. This method utilizes the geometric relationship between key points through a newly designed graph transformation decoder to improve the accuracy of key point positioning. Pose Anything outperforms the previous state-of-the-art on the MP-100 benchmark and achieves significant improvements in both 1-shot and 5-shot settings. Compared with previous CAPE methods, the end-to-end training of this method shows scalability and efficiency.

GenAlt generates descriptive alternative text for online images, providing assistance to those who need it. Just right-click on the image and click "Get Alt Text from GenAlt" to get the image's description as its alt text. To view the generated caption and copy it to your clipboard, simply select "Copy AI Image Description from GenAlt". Some GenAlt testimonials from users are as follows: 1. “GenAlt helps me understand photos...better than existing tools.” —Accessibility advocate and Twitch streamer 2. “GenAlt is really more helpful than other apps on the internet and helps me describe pictures better.” — Remi, high school sophomore 3. “GenAlt is easy to use and helps make social media more accessible to me.” —Aaron, freshman

Pixplain is an AI-powered browser plug-in that allows users to interact with images and videos, just like you have always wanted. Pixplain uses the latest AI models such as GPT-4 vision to better understand image content and provide explanations. Main functions: - Get explanations of images and page content with one click - Supports top AI models such as GPT-4 Easily copy, update or modify prompts for a smoother creative experience - You can move the Pixplain window to get the best page view

GLEE is a general object base model for pictures and videos. It realizes the positioning and recognition of objects in images and videos through a unified framework, and can be applied to various object perception tasks. GLEE enables efficient zero-shot transfer and generalization while maintaining state-of-the-art performance by jointly training various data sources from different levels of supervision to form a universal object representation. It also has good scalability and robustness.

Vision AI offers three computer vision products, including Vertex AI Vision, custom machine learning models, and the Vision API. You can use these products to extract valuable information from images, perform image classification and search, and create a variety of computer vision applications. Vision AI provides an easy-to-use interface and powerful pre-trained models to meet different user needs.

DeepFace is a lightweight face recognition and facial attribute analysis (age, gender, emotion and ethnicity) library. It wraps state-of-the-art models: VGG-Face, Google FaceNet, OpenFace, Facebook DeepFace, DeepID, ArcFace, Dlib and SFace. The library provides functions such as face verification, face recognition, and facial attribute analysis. The strength of DeepFace lies in its high accuracy and diverse model selection.

AI VISION is a breakthrough image recognition application that leverages advanced image recognition technology to recognize images and provide instant answers to your questions. With unparalleled accuracy, whether you're a curious explorer, a dedicated student, or a professional who needs fast, accurate information, AI VISION has what you need. It also offers real-time answering capabilities, a seamless user experience, and endless possibilities. AI VISION is suitable for educational research, travel insights, or satisfying curiosity, allowing you to make smarter, more informed decisions every time you encounter an image.

Cola is a method that uses a language model (LM) to aggregate the output of 2 or more visual-language models (VLM). Our model assembly method is called Cola (COordinative LAnguage model or visual reasoning). Cola works best with LM fine-tuning (called Cola-FT). Cola is also effective in zero-shot or few-shot context learning (called Cola-Zero). In addition to performance improvements, Cola is also more robust to VLM errors. We show that Cola can be applied to a variety of VLMs (including large multimodal models such as InstructBLIP) and 7 datasets (VQA v2, OK-VQA, A-OKVQA, e-SNLI-VE, VSR, CLEVR, GQA), and that it consistently improves performance.

GenAlt is an online auxiliary text tool for generating image descriptions. Simply right-click on an image and click "Get Alt Text for GenAlt" to get the image's description as alt text. GenAlt has received some positive reviews from users, allowing them to better understand images. You can improve the accessibility of your images by installing this plugin.

Stable Signature is a method of embedding watermarks into images that uses latent diffusion models (LDM) to extract and embed watermarks. This method is highly stable and robust and can maintain the readability of the watermark under a variety of attacks. Stable Signature provides pre-trained models and code implementations that users can use to embed and extract watermarks.

Lexy is an image text extraction tool based on AI technology. It can automatically identify text in images and extract them to facilitate subsequent processing and analysis by users. Lexy has high accuracy and fast recognition speed, and is suitable for various image text extraction scenarios. Whether it is an individual user who needs to extract text from pictures or an enterprise user who needs to perform large-scale image text processing, Lexy can meet your needs.

GenAlt uses artificial intelligence to generate descriptive alt text for online images that don’t have image descriptions! Just right-click on the image, hit GenAlt Get Image Description, and you'll get the image's description as its alt text. Please note: GenAlt will display a brief popup of the title generated for the image.

ALT AI: Add Alt Text to Image Description is an accessibility tool that adds Alt text to any page on the internet. ALT AI aims to improve the web experience for visually impaired users. Using the ALT AI Chrome extension, you can automatically add Alt text to every image on your page, replacing any existing inaccurate Alt descriptions. Screen readers will read out ALT AI-generated Alt text to help users better understand the content on the page.

AI QR Code Reader is a QR code recognition plug-in based on artificial intelligence, which can efficiently recognize QR codes of various shapes, colors and rotations. It has fast and accurate recognition capabilities and allows for convenient QR code scanning in the browser. No matter what shape, color and rotation angle the QR code is, it can be easily recognized. By right-clicking on the plug-in icon, you can open the QR code recognition interface, and the recognition results will be displayed in the form of a bubble pop-up window.

Bing image creation tool is an intelligent search tool provided by Microsoft Bing, which can help users quickly find the image information they want and get rewards.

Finding Photos can help you resolve photo clutter. With Find Photos, you can easily search your photo library by objects, text, and even people, harnessing the power of artificial intelligence. No longer will you have to spend hours searching for that perfect selfie or a funny photo of your adorable dog. Find Photos helps you index your photos and make them easy to search with just a few clicks. Plus, finding photos is not only practical, it’s fun! You can use it to rediscover old times and create collages of your favorite photos. Because we value the security of your photos, our app is equipped with best-in-class security features to ensure your intimate photos stay private. You can trust Find Photos to keep your memories safe.

Face Age is a facial skin analyzer based on artificial intelligence technology. It can quickly analyze the user's skin age by scanning the user's facial photos and provide targeted skin care suggestions. Face Age has precise analysis capabilities and intelligent algorithms, which can help users understand their skin conditions and choose appropriate skin care products and care methods. Face Age also supports multiple languages ​​and provides user-friendly interface and operation process. Whether you are a beauty enthusiast or a skin care professional, you can get accurate skin analysis results through Face Age.