Best AI tools for< Evaluate Detection Models >

The Deepfake Detection Challenge Dataset is a project initiated by Facebook AI to accelerate the development of new ways to detect deepfake videos. The dataset consists of over 100,000 videos and was created in collaboration with industry leaders and academic experts. It includes two versions: a preview dataset with 5k videos and a full dataset with 124k videos, each featuring facial modification algorithms. The dataset was used in a Kaggle competition to create better models for detecting manipulated media. The top-performing models achieved high accuracy on the public dataset but faced challenges when tested against the black box dataset, highlighting the importance of generalization in deepfake detection. The project aims to encourage the research community to continue advancing in detecting harmful manipulated media.

Datumbox is a machine learning platform that offers a powerful open-source Machine Learning Framework written in Java. It provides a large collection of algorithms, models, statistical tests, and tools to power up intelligent applications. The platform enables developers to build smart software and services quickly using its REST Machine Learning API. Datumbox API offers off-the-shelf Classifiers and Natural Language Processing services for applications like Sentiment Analysis, Topic Classification, Language Detection, and more. It simplifies the process of designing and training Machine Learning models, making it easy for developers to create innovative applications.

Face Shape Detector is an advanced AI tool that analyzes facial landmarks in uploaded photos to identify the user's face shape and provide percentage distributions for different face shapes. It utilizes sophisticated algorithms to assess key metrics such as jawline, forehead width, and cheekbone structure, delivering detailed insights into facial proportions. Users can explore the power of facial analysis, understand their unique face shape, and receive quick and accurate results through this intuitive tool.

AI Undetect is a leading AI detection and humanization tool designed to evaluate and rewrite AI-generated content to make it undetectable by AI detectors. The tool offers various AI detectors, a humanizer feature, and supports multiple languages. Users can access detailed AI detection reports and bypass AI detection effortlessly. AI Undetect is suitable for marketers, writers, bloggers, journalists, and researchers looking to ensure the authenticity and credibility of their content.

iCAD is an AI-powered application designed for cancer detection, specifically focusing on breast cancer. The platform offers a suite of solutions including Detection, Density Assessment, and Risk Evaluation, all backed by science, clinical evidence, and proven patient outcomes. iCAD's AI-powered solutions aim to expose the hiding place of cancer, providing certainty and peace of mind, ultimately improving patient outcomes and saving more lives.

JobSynergy is an AI-powered platform that revolutionizes the hiring process by automating and conducting interviews at scale. It offers a real-world interview simulator that adapts dynamically to candidates' responses, custom questions and metrics evaluation, cheating detection using eye, voice, and screen, and detailed reports for better hiring decisions. The platform enhances efficiency, candidate experience, and ensures security and integrity in the hiring process.

PolygrAI is a digital polygraph powered by AI technology that provides real-time risk assessment and sentiment analysis. The platform meticulously analyzes facial micro-expressions, body language, vocal attributes, and linguistic cues to detect behavioral fluctuations and signs of deception. By combining well-established psychology practices with advanced AI and computer vision detection, PolygrAI offers users actionable insights for decision-making processes across various applications.

CloudExam AI is an online testing platform developed by Hanke Numerical Union Technology Co., Ltd. It provides stable and efficient AI online testing services, including intelligent grouping, intelligent monitoring, and intelligent evaluation. The platform ensures test fairness by implementing automatic monitoring level regulations and three random strategies. It prioritizes information security by combining software and hardware to secure data and identity. With global cloud deployment and flexible architecture, it supports hundreds of thousands of concurrent users. CloudExam AI offers features like queue interviews, interactive pen testing, data-driven cockpit, AI grouping, AI monitoring, AI evaluation, random question generation, dual-seat testing, facial recognition, real-time recording, abnormal behavior detection, test pledge book, student information verification, photo uploading for answers, inspection system, device detection, scoring template, ranking of results, SMS/email reminders, screen sharing, student fees, and collaboration with selected schools.

Skeptic Reader is a Chrome plugin that helps users detect bias and logical fallacies in real-time while browsing the internet. It uses GPT-4 technology to identify potential biases and fallacies in news articles, social media posts, and other online content. The plugin provides users with counter-arguments and suggestions for further research, helping them to make more informed decisions about the information they consume. Skeptic Reader is designed to promote critical thinking and media literacy, and it is a valuable tool for anyone who wants to navigate the online world with a more discerning eye.

Skeptic Reader is a Chrome plugin that detects biases and logical fallacies in real-time. It's powered by GPT4 and helps users to critically evaluate the information they consume online. The plugin highlights biases and fallacies, provides counter-arguments, and suggests alternative perspectives. It's designed to promote informed skepticism and encourage users to question the information they encounter online.

BS Detector is an AI tool designed to help users determine the credibility of information by analyzing text or images for misleading or false content. Users can input a link, upload a screenshot, or paste text to receive a BS (Bullshit) rating. The tool leverages AI algorithms to assess the accuracy and truthfulness of the provided content, offering users a quick and efficient way to identify potentially deceptive information.

Integrito is an AI detection tool for writing activity analysis, designed to help teachers prevent cheating, students prove their contribution, and institutions promote honesty. It offers a comprehensive text analysis to ensure authenticity, detect suspicious activity, and track the writing process. Integrito empowers users to evaluate contribution and editing time, view the history of the writing process, and unveil contract-cheating and ghost-writing by writing services. The tool aims to enhance critical thinking, foster creativity, and promote high standards in academia by providing plagiarism checking, AI detection, grammar checking, and authorship verification features.

Sourcer AI is an AI-powered fact-checking tool that provides real-time assessments of source credibility and bias in online information. It revolutionizes the evaluation process by using cutting-edge artificial intelligence to uncover reputability ratings and political biases of online sources, helping users combat misinformation and make informed decisions.

Traceable is an intelligent API security platform designed for enterprise-scale security. It offers unmatched API discovery, attack detection, threat hunting, and infinite scalability. The platform provides comprehensive protection against API attacks, fraud, and bot security, along with API testing capabilities. Powered by Traceable's OmniTrace Engine, it ensures unparalleled security outcomes, remediation, and pre-production testing. Security teams trust Traceable for its speed and effectiveness in protecting API infrastructures.

LoginLlama is an AI-powered suspicious login detection tool designed for developers to enhance customer security effortlessly by preventing fraudulent logins. The tool evaluates login attempts based on various ranking factors, historic behavior, AI analysis, request origin, and user agent. It helps combat top login threats like unauthorized access, account takeover, credential stuffing, brute force attacks, phishing attacks, and insider threats. LoginLlama offers easy integration with REST API and official libraries for popular programming languages. It is made with ❤️ in the UK and is 100% independent and self-funded.

BenchLLM is an AI tool designed for AI engineers to evaluate LLM-powered apps by running and evaluating models with a powerful CLI. It allows users to build test suites, choose evaluation strategies, and generate quality reports. The tool supports OpenAI, Langchain, and other APIs out of the box, offering automation, visualization of reports, and monitoring of model performance.

thisorthis.ai is an AI tool that allows users to compare generative AI models and AI model responses. It helps users analyze and evaluate different AI models to make informed decisions. The tool requires JavaScript to be enabled for optimal functionality.

Langtrace AI is an open-source observability tool powered by Scale3 Labs that helps monitor, evaluate, and improve LLM (Large Language Model) applications. It collects and analyzes traces and metrics to provide insights into the ML pipeline, ensuring security through SOC 2 Type II certification. Langtrace supports popular LLMs, frameworks, and vector databases, offering end-to-end observability and the ability to build and deploy AI applications with confidence.

Arize AI is an AI Observability & LLM Evaluation Platform that helps you monitor, troubleshoot, and evaluate your machine learning models. With Arize, you can catch model issues, troubleshoot root causes, and continuously improve performance. Arize is used by top AI companies to surface, resolve, and improve their models.

Evidently AI is an open-source machine learning (ML) monitoring and observability platform that helps data scientists and ML engineers evaluate, test, and monitor ML models from validation to production. It provides a centralized hub for ML in production, including data quality monitoring, data drift monitoring, ML model performance monitoring, and NLP and LLM monitoring. Evidently AI's features include customizable reports, structured checks for data and models, and a Python library for ML monitoring. It is designed to be easy to use, with a simple setup process and a user-friendly interface. Evidently AI is used by over 2,500 data scientists and ML engineers worldwide, and it has been featured in publications such as Forbes, VentureBeat, and TechCrunch.

The Lakera PINT Benchmark provides a neutral evaluation method for prompt injection detection systems, offering a dataset of English inputs with prompt injections, jailbreaks, benign inputs, user-agent chats, and public document excerpts. The dataset is designed to be challenging and representative, with plans for future enhancements. The benchmark aims to be unbiased and accurate, welcoming contributions to improve prompt injection detection. Users can evaluate prompt injection detection systems using the provided Jupyter Notebook. The dataset structure is specified in YAML format, allowing users to prepare their datasets for benchmarking. Evaluation examples and resources are provided to assist users in evaluating prompt injection detection models and tools.

This repository contains materials for the paper 'How Far Have We Gone in Vulnerability Detection Using Large Language Model'. It provides a tool for evaluating vulnerability detection models using datasets such as d2a, ctf, magma, big-vul, and devign. Users can query the model 'Llama-2-7b-chat-hf' and store results in a SQLite database for analysis. The tool supports binary and multiple classification tasks with concurrency settings. Additionally, users can evaluate the results and generate a CSV file with metrics for each dataset and prompt type.

**deep** doctection is a Python library that orchestrates document extraction and document layout analysis tasks using deep learning models. It does not implement models but enables you to build pipelines using highly acknowledged libraries for object detection, OCR and selected NLP tasks and provides an integrated framework for fine-tuning, evaluating and running models. For more specific text processing tasks use one of the many other great NLP libraries. **deep** doctection focuses on applications and is made for those who want to solve real world problems related to document extraction from PDFs or scans in various image formats. **deep** doctection provides model wrappers of supported libraries for various tasks to be integrated into pipelines. Its core function does not depend on any specific deep learning library. Selected models for the following tasks are currently supported: * Document layout analysis including table recognition in Tensorflow with **Tensorpack**, or PyTorch with **Detectron2**, * OCR with support of **Tesseract**, **DocTr** (Tensorflow and PyTorch implementations available) and a wrapper to an API for a commercial solution, * Text mining for native PDFs with **pdfplumber**, * Language detection with **fastText**, * Deskewing and rotating images with **jdeskew**. * Document and token classification with all LayoutLM models provided by the **Transformer library**. (Yes, you can use any LayoutLM-model with any of the provided OCR-or pdfplumber tools straight away!). * Table detection and table structure recognition with **table-transformer**. * There is a small dataset for token classification available and a lot of new tutorials to show, how to train and evaluate this dataset using LayoutLMv1, LayoutLMv2, LayoutXLM and LayoutLMv3. * Comprehensive configuration of **analyzer** like choosing different models, output parsing, OCR selection. Check this notebook or the docs for more infos. * Document layout analysis and table recognition now runs with **Torchscript** (CPU) as well and **Detectron2** is not required anymore for basic inference. * [**new**] More angle predictors for determining the rotation of a document based on **Tesseract** and **DocTr** (not contained in the built-in Analyzer). * [**new**] Token classification with **LiLT** via **transformers**. We have added a model wrapper for token classification with LiLT and added a some LiLT models to the model catalog that seem to look promising, especially if you want to train a model on non-english data. The training script for LayoutLM can be used for LiLT as well and we will be providing a notebook on how to train a model on a custom dataset soon. **deep** doctection provides on top of that methods for pre-processing inputs to models like cropping or resizing and to post-process results, like validating duplicate outputs, relating words to detected layout segments or ordering words into contiguous text. You will get an output in JSON format that you can customize even further by yourself. Have a look at the **introduction notebook** in the notebook repo for an easy start. Check the **release notes** for recent updates. **deep** doctection or its support libraries provide pre-trained models that are in most of the cases available at the **Hugging Face Model Hub** or that will be automatically downloaded once requested. For instance, you can find pre-trained object detection models from the Tensorpack or Detectron2 framework for coarse layout analysis, table cell detection and table recognition. Training is a substantial part to get pipelines ready on some specific domain, let it be document layout analysis, document classification or NER. **deep** doctection provides training scripts for models that are based on trainers developed from the library that hosts the model code. Moreover, **deep** doctection hosts code to some well established datasets like **Publaynet** that makes it easy to experiment. It also contains mappings from widely used data formats like COCO and it has a dataset framework (akin to **datasets** so that setting up training on a custom dataset becomes very easy. **This notebook** shows you how to do this. **deep** doctection comes equipped with a framework that allows you to evaluate predictions of a single or multiple models in a pipeline against some ground truth. Check again **here** how it is done. Having set up a pipeline it takes you a few lines of code to instantiate the pipeline and after a for loop all pages will be processed through the pipeline.

AutoNode is a self-operating computer system designed to automate web interactions and data extraction processes. It leverages advanced technologies like OCR (Optical Character Recognition), YOLO (You Only Look Once) models for object detection, and a custom site-graph to navigate and interact with web pages programmatically. Users can define objectives, create site-graphs, and utilize AutoNode via API to automate tasks on websites. The tool also supports training custom YOLO models for object detection and OCR for text recognition on web pages. AutoNode can be used for tasks such as extracting product details, automating web interactions, and more.

The 100 Days in AI repository provides a comprehensive roadmap for individuals to learn Artificial Intelligence over a period of 100 days. It covers topics ranging from basic programming in Python to advanced concepts in AI, including machine learning, deep learning, and specialized AI topics. The repository includes daily tasks, resources, and exercises to ensure a structured learning experience. By following this roadmap, users can gain a solid understanding of AI and be prepared to work on real-world AI projects.

Pixeltable is a Python library designed for ML Engineers and Data Scientists to focus on exploration, modeling, and app development without the need to handle data plumbing. It provides a declarative interface for working with text, images, embeddings, and video, enabling users to store, transform, index, and iterate on data within a single table interface. Pixeltable is persistent, acting as a database unlike in-memory Python libraries such as Pandas. It offers features like data storage and versioning, combined data and model lineage, indexing, orchestration of multimodal workloads, incremental updates, and automatic production-ready code generation. The tool emphasizes transparency, reproducibility, cost-saving through incremental data changes, and seamless integration with existing Python code and libraries.

This repository is a curated list of papers focused on the detection of Large Language Models (LLMs)-generated content. It includes the latest research papers covering detection methods, datasets, attacks, and more. The repository is regularly updated to include the most recent papers in the field.

An Open-Source Engineering Guide for Prompt-in-context-learning from EgoAlpha Lab. 📝 Papers | ⚡️ Playground | 🛠 Prompt Engineering | 🌍 ChatGPT Prompt | ⛳ LLMs Usage Guide > **⭐️ Shining ⭐️:** This is fresh, daily-updated resources for in-context learning and prompt engineering. As Artificial General Intelligence (AGI) is approaching, let’s take action and become a super learner so as to position ourselves at the forefront of this exciting era and strive for personal and professional greatness. The resources include: _🎉Papers🎉_: The latest papers about _In-Context Learning_ , _Prompt Engineering_ , _Agent_ , and _Foundation Models_. _🎉Playground🎉_: Large language models（LLMs）that enable prompt experimentation. _🎉Prompt Engineering🎉_: Prompt techniques for leveraging large language models. _🎉ChatGPT Prompt🎉_: Prompt examples that can be applied in our work and daily lives. _🎉LLMs Usage Guide🎉_: The method for quickly getting started with large language models by using LangChain. In the future, there will likely be two types of people on Earth (perhaps even on Mars, but that's a question for Musk): - Those who enhance their abilities through the use of AIGC; - Those whose jobs are replaced by AI automation. 💎EgoAlpha: Hello! human👤, are you ready?

ZenGuard AI enables AI developers to integrate production-level, low-code LLM (Large Language Model) guardrails into their generative AI applications effortlessly. With ZenGuard AI, ensure your application operates within trusted boundaries, is protected from prompt injections, and maintains user privacy without compromising on performance.

The STM32 AI model zoo is a collection of reference machine learning models optimized to run on STM32 microcontrollers. It provides a large collection of application-oriented models ready for re-training, scripts for easy retraining from user datasets, pre-trained models on reference datasets, and application code examples generated from user AI models. The project offers training scripts for transfer learning or training custom models from scratch. It includes performances on reference STM32 MCU and MPU for float and quantized models. The project is organized by application, providing step-by-step guides for training and deploying models.

LLM Hallucination Index - RAG Special is a comprehensive evaluation of large language models (LLMs) focusing on context length and open vs. closed-source attributes. The index explores the impact of context length on model performance and tests the assumption that closed-source LLMs outperform open-source ones. It also investigates the effectiveness of prompting techniques like Chain-of-Note across different context lengths. The evaluation includes 22 models from various brands, analyzing major trends and declaring overall winners based on short, medium, and long context insights. Methodologies involve rigorous testing with different context lengths and prompting techniques to assess models' abilities in handling extensive texts and detecting hallucinations.

OpenRedTeaming is a repository focused on red teaming for generative models, specifically large language models (LLMs). The repository provides a comprehensive survey on potential attacks on GenAI and robust safeguards. It covers attack strategies, evaluation metrics, benchmarks, and defensive approaches. The repository also implements over 30 auto red teaming methods. It includes surveys, taxonomies, attack strategies, and risks related to LLMs. The goal is to understand vulnerabilities and develop defenses against adversarial attacks on large language models.

InternVL scales up the ViT to _**6B parameters**_ and aligns it with LLM. It is a vision-language foundation model that can perform various tasks, including: **Visual Perception** - Linear-Probe Image Classification - Semantic Segmentation - Zero-Shot Image Classification - Multilingual Zero-Shot Image Classification - Zero-Shot Video Classification **Cross-Modal Retrieval** - English Zero-Shot Image-Text Retrieval - Chinese Zero-Shot Image-Text Retrieval - Multilingual Zero-Shot Image-Text Retrieval on XTD **Multimodal Dialogue** - Zero-Shot Image Captioning - Multimodal Benchmarks with Frozen LLM - Multimodal Benchmarks with Trainable LLM - Tiny LVLM InternVL has been shown to achieve state-of-the-art results on a variety of benchmarks. For example, on the MMMU image classification benchmark, InternVL achieves a top-1 accuracy of 51.6%, which is higher than GPT-4V and Gemini Pro. On the DocVQA question answering benchmark, InternVL achieves a score of 82.2%, which is also higher than GPT-4V and Gemini Pro. InternVL is open-sourced and available on Hugging Face. It can be used for a variety of applications, including image classification, object detection, semantic segmentation, image captioning, and question answering.

This repository provides a curated list of papers, datasets, and resources related to the detection and mitigation of hallucinations in large language models (LLMs). Hallucinations refer to the generation of factually incorrect or nonsensical text by LLMs, which can be a significant challenge for their use in real-world applications. The resources in this repository aim to help researchers and practitioners better understand and address this issue.

Enthusiastic about truth in academic papers, critical and analytical.

AI content analysis expert for detecting AI authorship.

Eloquent analyst of media biases and agendas.

Detect logical fallacies and lapses in critical thinking to help avoid misinformation in the style of Socrates

I will score your Mind Blowing Startup Ideas, helping your to evaluate faster.

I'll help you evaluate your writing to make sure it's engaging, informative, and flows well. Uses principles from "Made to Stick"

The main objective of a personalized ChatGPT for reading laboratory tests is to evaluate laboratory test results and create a spreadsheet with the evaluation results and possible solutions.

As a Search Quality Rater, you will help evaluate search engine quality around the world.

Business Model Canvas Creator - Build and evaluate your business model

Guide to unbiasedly evaluate different job opportunities

I automatically create and evaluate phone scripts, presenting a final draft.

Helps you evaluate whether or not tech platforms match UNESCO's Internet for Trust Guidelines for the Governance of Digital Platforms

Analyzes AFL draft data to evaluate clubs' historical performance

Generate, Evaluate, and Improve your Academic Programs

Searches the web's verified fact-checkers to evaluate facts and claims

🔬💊 Navigate the high-risk, high-reward world of biotech and pharma investing! Discover breakthrough therapies 🧬📈, understand drug development 🧪📊, and evaluate investment opportunities 🚀💰. Invest wisely in innovation! 💡🌐 Not a financial advisor. 🚫💼

Guides B2B startups in a structured customer segment evaluation process. Stop guessing! Ideate, Evaluate & Make data-driven decision.

I provide a structured way of using AI to support teaching and learning. I use the the CHOICE method (i.e., Clarify, Harness, Originate, Iterate, Communicate, Evaluate) to ensure that your use of AI can help you meet your educational goals.

Evaluates your long-term market position based on value offered and uniqueness against competitors.