awesome-repositories.com
Blog
MCP
awesome-repositories.com

Discover the best open-source repositories with AI-powered search.

ExploreCurated searchesOpen-source alternativesSelf-hosted softwareBlogSitemap
ProjectMCP serverAboutHow we rankPress
LegalPrivacyTerms
© 2026 Bringes Technology SRL·VAT RO45896025·hello@awesome-repositories.com
KichangKim avatar

KichangKim/DeepDanbooru

0
View on GitHub↗
2,892 stars·269 forks·Python·mit·11 views

DeepDanbooru

DeepDanbooru is a deep learning tool for tagging anime-style images with Danbooru-style tags. It uses a pre-trained convolutional neural network to analyze images and predict tags identifying characters, attributes, and artwork details.

The project provides a complete pipeline for training custom tag recognition models. Users can prepare datasets by downloading tag definitions from a remote Danbooru server using authenticated API requests, then store image-tag pairs in a structured SQLite database. The training workflow supports filtering datasets by rating or score criteria, configuring hyperparameters, and running optimization to recognize user-defined tags.

The tool also includes a command-line interface for repeated command execution with configurable intervals, enabling automated continuous workflows.

Features

  • Anime Image Taggers - Analyzes anime-style images and outputs Danbooru-style tags identifying characters and attributes.
  • Custom Model Training - Trains custom tag recognition models using SQLite datasets and configurable hyperparameters.
  • Deep Learning Inference Engines - Runs a pre-trained convolutional neural network to predict tags from image pixel data.
  • Model-Based Tag Predictions - Evaluates images against a trained model to produce predicted tags for each image.
  • Image Tag Training Pipelines - Processes user-provided images and tags into a training workflow with hyperparameter optimization.
  • Training Dataset Preparation - Organizes images and tags into a structured SQLite database for the training pipeline.
  • Remote Data Fetching - Fetches tag definitions and metadata from a remote server using authenticated API requests.
  • SQLite Storage Adapters - Stores image-tag pairs in a structured SQLite database for efficient training data management.
  • Tag Definition Downloads - Downloads Danbooru tag definitions via authenticated API for training and estimation tasks.
  • Rating and Score Filters - Filters raw SQLite datasets by rating and score criteria before training.

Star history

Star history chart for kichangkim/deepdanbooruStar history chart for kichangkim/deepdanbooru

How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.

AI search

Explore more awesome repositories

Describe what you need in plain English — the AI ranks thousands of curated open-source projects by relevance.

Start searching with AI

Open-source alternatives to DeepDanbooru

Similar open-source projects, ranked by how many features they share with DeepDanbooru.
  • nl8590687/asrt_speechrecognitionnl8590687 avatar

    nl8590687/ASRT_SpeechRecognition

    8,375View on GitHub↗

    This project is a Chinese automatic speech recognition framework and deep learning system designed to convert spoken Chinese audio into written text. It functions as a toolkit for training, evaluating, and deploying speech-to-text models, utilizing a specialized pinyin-to-text converter that transforms phonetic sequences into Chinese characters using a probability graph model. The system is distinguished by its deployment flexibility, offering a dockerized recognition server that provides transcription capabilities as a remote API. It supports high-performance streaming through a gRPC speech-

    Pythonasrtchinese-speech-recognitioncnn
    View on GitHub↗8,375
  • dusty-nv/jetson-inferencedusty-nv avatar

    dusty-nv/jetson-inference

    8,734View on GitHub↗

    jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti

    C++caffecomputer-visiondeep-learning
    View on GitHub↗8,734
  • facebook/haxlfacebook avatar

    facebook/Haxl

    4,381View on GitHub↗

    Haxl is a Haskell library and remote service request orchestrator designed for coordinating concurrent data fetching, request batching, and caching across multiple remote service providers. It functions as a framework for retrieving data from external databases and web services while minimizing network round trips. The project distinguishes itself through an applicative-based request batching system that groups multiple individual data requests into single calls to reduce network overhead. It employs an asynchronous parallel request scheduler to execute independent requests concurrently and u

    Haskell
    View on GitHub↗4,381
  • pubkey/rxdbpubkey avatar

    pubkey/rxdb

    23,048View on GitHub↗

    This project is a reactive, offline-first NoSQL database engine designed for JavaScript applications. It provides a robust framework for managing application state by synchronizing data across browsers, mobile devices, and server-side runtimes. By treating local storage as the primary source of truth, it enables applications to remain functional without network connectivity, automatically reconciling changes with remote backends once a connection is restored. The database distinguishes itself through a modular architecture that supports cross-environment synchronization and high-performance d

    TypeScriptangularbrowser-databasecouchdb
    View on GitHub↗23,048
See all 30 alternatives to DeepDanbooru→

Frequently asked questions

What does kichangkim/deepdanbooru do?

DeepDanbooru is a deep learning tool for tagging anime-style images with Danbooru-style tags. It uses a pre-trained convolutional neural network to analyze images and predict tags identifying characters, attributes, and artwork details.

What are the main features of kichangkim/deepdanbooru?

The main features of kichangkim/deepdanbooru are: Anime Image Taggers, Custom Model Training, Deep Learning Inference Engines, Model-Based Tag Predictions, Image Tag Training Pipelines, Training Dataset Preparation, Remote Data Fetching, SQLite Storage Adapters.

What are some open-source alternatives to kichangkim/deepdanbooru?

Open-source alternatives to kichangkim/deepdanbooru include: nl8590687/asrt_speechrecognition — This project is a Chinese automatic speech recognition framework and deep learning system designed to convert spoken… dusty-nv/jetson-inference — jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU… facebook/haxl — Haxl is a Haskell library and remote service request orchestrator designed for coordinating concurrent data fetching,… pubkey/rxdb — This project is a reactive, offline-first NoSQL database engine designed for JavaScript applications. It provides a… advimman/lama — Lama is an image restoration framework and deep learning model designed for image inpainting and object removal. It… caffe2/caffe2 — Caffe2 is a high-performance deep learning framework and C++ machine learning library. It serves as a modular system…