funcaptcha-server
A free python server that provides a simple interface to solve FunCaptcha challenges.
Stars: 142
README:
π Based onπ MagicalMadoka/funcaptcha-challenger Image recognition server
Capsolver.com is an AI-powered service that specializes in solving various types of captchas automatically. It supports captchas such as reCAPTCHA V2, reCAPTCHA V3, hCaptcha, FunCaptcha, DataDome, AWS Captcha, Geetest, and Cloudflare Captcha / Challenge 5s, Imperva / Incapsula, among others.
For developers, Capsolver offers API integration options detailed in their documentation, facilitating the integration of captcha solving into applications. They also provide browser extensions for Chrome and Firefox, making it easy to use their service directly within a browser. Different pricing packages are available to accommodate varying needs, ensuring flexibility for users.
- SUPPORTED MODELS:
| animal_rotation_towards_hand | match_count_similarity | match_count_target_detection | match_count_source_detection |
| hopscotch_highsec | icon_connect | 3d_rollball_objects | 3d_rollball_objects_v2 |
| numericalmatch_target_detection | numericalmatch_similarity | coordinatesmatch | penguin |
| shadows | dice_pair | train_coordinates | numericalmatch |
| dicematch | hand_number_puzzle | penguins | frankenhead |
| BrokenJigsawbrokenjigsaw_swap | counting | knotsCrossesCircle | orbit_match_game |
-
https://github.com/MagicalMadoka/funcaptcha-challenger/releases/download/model/version.json
-
http://Deployed API port/support
-
Funcaptcha Other types welcome PR.
- π‘ Installation dependency:
pip install -r requirements.txt- π‘ Run:
python main.py- π‘ Example curl command:
curl --location --request POST 'http://127.0.0.1:8181/createTask' \
--header 'Content-Type: application/json' \
--data-raw '{
"clientKey": "your_key",
"task": {
"type": "FunCaptchaClassification",
"image": "data:image/jpeg;base64,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",
"question": "3d_rollball_objects"
}
}'- π‘ Response Example:
{
"errorCode": "",
"errorId": 0,
"solution": {
"objects": [
4
]
},
"status": "ready",
"taskId": "bb11d056130b5e41f3d870edfa21c6a4"
}- π‘ Identification instructions:
errorId: 0 Recognition successful.
objects: Corresponding recognition results
*Counting from 0οΌ"objects": [4]The recognition result is identified as sequence 4, corresponding to the finger.
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for funcaptcha-server
Similar Open Source Tools
Comfyui-Aix-NodeMap
Comfyui-Aix-NodeMap is a project by the Aix team to organize and annotate the latest nodes in Comfyui. It aims to address the challenge of finding nodes effectively as their number increases. The project is continuously updated every 7 days, with the opportunity for users to provide feedback on any omissions or errors. The team respects developers' opinions and strives to make corrections promptly. The project is part of Aix's vision to make humanity more efficient through open-source contributions, including daily updates on workflow, AI information, and node introductions.
WristAssist
WristAssist is the first app for all WearOS watches that fully brings the classic ChatGPT and DALL-E features to your wrist. It allows users to chat, save chats, edit chats, view galleries, create images, and share images directly from their wrist. The app provides a seamless user experience with easy installation from Google Play and free lifetime updates. Users can refer to the Wiki page for detailed setup instructions. WristAssist is licensed under the terms of the Apache 2.0 license.
awesome-langchain-zh
The awesome-langchain-zh repository is a collection of resources related to LangChain, a framework for building AI applications using large language models (LLMs). The repository includes sections on the LangChain framework itself, other language ports of LangChain, tools for low-code development, services, agents, templates, platforms, open-source projects related to knowledge management and chatbots, as well as learning resources such as notebooks, videos, and articles. It also covers other LLM frameworks and provides additional resources for exploring and working with LLMs. The repository serves as a comprehensive guide for developers and AI enthusiasts interested in leveraging LangChain and LLMs for various applications.
cube-studio
Cube Studio is an open-source all-in-one cloud-native machine learning platform that provides various functionalities such as project group management, network configuration, user management, role management, billing functions, SSO single sign-on, support for multiple computing power types, support for multiple resource groups and clusters, edge cluster support, serverless cluster mode support, database storage support, machine resource management, storage disk management, internationalization capabilities, data map management, data calculation, ETL orchestration, data set management, data annotation, image/audio/text dataset support, feature processing, traditional machine learning algorithms, distributed deep learning frameworks, distributed acceleration frameworks, model evaluation, model format conversion, model registration, model deployment, distributed media processing, custom operators, automatic learning, custom training images, automatic parameter tuning, TensorBoard jobs, internal services, model management, inference services, monitoring, model application management, model marketplace, model development, model fine-tuning, web model deployment, automated annotation, dataset SDK, notebook SDK, pipeline training SDK, inference service SDK, large model distributed training, large model inference, large model fine-tuning, intelligent conversation, private knowledge base, model deployment for WeChat public accounts, enterprise WeChat group chatbot integration, DingTalk group chatbot integration, and more. Cube Studio offers template-based functionality for data import/export, data processing, feature processing, machine learning frameworks, machine learning algorithms, deep learning frameworks, model processing, model serving, monitoring, and more.
goat
GOAT (Great Onchain Agent Toolkit) is an open-source framework designed to simplify the process of making AI agents perform onchain actions by providing a provider-agnostic solution that abstracts away the complexities of interacting with blockchain tools such as wallets, token trading, and smart contracts. It offers a catalog of ready-made blockchain actions for agent developers and allows dApp/smart contract developers to develop plugins for easy access by agents. With compatibility across popular agent frameworks, support for multiple blockchains and wallet providers, and customizable onchain functionalities, GOAT aims to streamline the integration of blockchain capabilities into AI agents.
Awesome-LLMOps
Awesome-LLMOps is a curated list of the best LLMOps tools, providing a comprehensive collection of frameworks and tools for building, deploying, and managing large language models (LLMs) and AI agents. The repository includes a wide range of tools for tasks such as building multimodal AI agents, fine-tuning models, orchestrating applications, evaluating models, and serving models for inference. It covers various aspects of the machine learning operations (MLOps) lifecycle, from training to deployment and observability. The tools listed in this repository cater to the needs of developers, data scientists, and machine learning engineers working with large language models and AI applications.
david-ai
David UI is a free and open-source collection of customizable, production-ready UI components built with Tailwind CSS. It is designed to be developer-friendly and performance-focused, streamlining the creation of modern, visually appealing interfaces to help deliver high-quality user experiences faster.
haystack-core-integrations
This repository contains integrations to extend the capabilities of Haystack version 2.0 and onwards. The code in this repo is maintained by deepset, see each integration's `README` file for details around installation, usage and support.
FlagPerf
FlagPerf is an integrated AI hardware evaluation engine jointly built by the Institute of Intelligence and AI hardware manufacturers. It aims to establish an industry-oriented metric system to evaluate the actual capabilities of AI hardware under software stack combinations (model + framework + compiler). FlagPerf features a multidimensional evaluation metric system that goes beyond just measuring 'whether the chip can support specific model training.' It covers various scenarios and tasks, including computer vision, natural language processing, speech, multimodal, with support for multiple training frameworks and inference engines to connect AI hardware with software ecosystems. It also supports various testing environments to comprehensively assess the performance of domestic AI chips in different scenarios.
cgft-llm
The cgft-llm repository is a collection of video tutorials and documentation for implementing large models. It provides guidance on topics such as fine-tuning llama3 with llama-factory, lightweight deployment and quantization using llama.cpp, speech generation with ChatTTS, introduction to Ollama for large model deployment, deployment tools for vllm and paged attention, and implementing RAG with llama-index. Users can find detailed code documentation and video tutorials for each project in the repository.
Awesome-Segment-Anything
The Segment Anything Model (SAM) is a powerful tool that allows users to segment any object in an image with just a few clicks. This makes it a great tool for a variety of tasks, such as object detection, tracking, and editing. SAM is also very easy to use, making it a great option for both beginners and experienced users.
mnn-llm
MNN-LLM is a high-performance inference engine for large language models (LLMs) on mobile and embedded devices. It provides optimized implementations of popular LLM models, such as ChatGPT, BLOOM, and GPT-3, enabling developers to easily integrate these models into their applications. MNN-LLM is designed to be efficient and lightweight, making it suitable for resource-constrained devices. It supports various deployment options, including mobile apps, web applications, and embedded systems. With MNN-LLM, developers can leverage the power of LLMs to enhance their applications with natural language processing capabilities, such as text generation, question answering, and dialogue generation.