hcaptcha challenger

Report Abuse

Basic Information

hCaptcha Challenger is a repository that implements an automated, multimodal approach to facing hCaptcha challenges using large language models and modular vision models. It is designed to enable AI-vs-AI interactions to interpret and respond to image-based and interactive hCaptcha tasks without relying on Tampermonkey scripts or third‚Äëparty anti‚Äëcaptcha services. The project provides documentation in multiple languages and bundles workflows for dataset collection, model training, CI tasks and model release management. It aims to provide a reproducible stack for researchers and developers who want to build, evaluate and iterate on agents that can classify, detect or interact with captcha interfaces using ONNX models, zero‚Äëshot vision models and multimodal LLM orchestration.

Links

Categorization

App Details

Features
The repository exposes a pluggable resource architecture that supports multiple challenge types and models. Documented capabilities include ResNet ONNX for binary image labeling, YOLOv8 ONNX for point detection and segmentation work, ViT for zero‚Äëshot multi‚Äëchoice, CLIP for self‚Äësupervised tasks and spatial chain‚Äëof‚Äëthought for drag‚Äëand‚Äëdrop scenarios. The project also lists advanced items such as Rank.Strategy, nested model zoo support and an agentic workflow backed by an AIOps multimodal LLM pull request. Operational features include CI workflows named sentinel and collector, Colab notebooks for ResNet and YOLOv8 training, dataset links and a model upload/release process. The codebase is structured to accept new model objects and evaluation pipelines without third‚Äëparty anti‚Äëcaptcha dependencies.
Use Cases
This project helps developers and researchers build reproducible, auditable systems for automating or simulating hCaptcha interactions using modular AI components. It centralizes dataset preparation, model training and CI collection so teams can iterate on models and evaluate agent performance across different challenge types. The pluggable model design lets users swap ONNX and multimodal vision backends, enabling controlled experiments in detection, classification and segmentation. Because it avoids external anti‚Äëcaptcha services and browser user‚Äëscripts, it is suitable for research and integration with browser automation tools like Playwright for end‚Äëto‚Äëend testing. Multilingual documentation and released model artifacts support adoption and community contributions.

Please fill the required fields*