{"data":{"slug":"simoncirstoiu-alice","name":"alice","tagline":"AI-powered YOLO dataset management toolkit","github_url":"https://github.com/simoncirstoiu/alice","owner":"simoncirstoiu","repo":"alice","owner_avatar_url":"https://avatars.githubusercontent.com/u/46245735?v=4","primary_language":"JavaScript","stars":410,"forks":41,"topics":["ai","ai-tools","annotation","computer-vision","dataset","frigate","nvidia-cuda","nvidia-smi","nvr","object-detection","training","yolo"],"archived":false,"github_pushed_at":"2026-04-26T17:51:53+00:00","maintenance_label":"Slowing","stars_delta_30d":40,"url":"https://www.graphcanon.com/tools/simoncirstoiu-alice","markdown_url":"https://www.graphcanon.com/tools/simoncirstoiu-alice.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/simoncirstoiu-alice","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=simoncirstoiu-alice","description":"Analyse · Learn · Ingest · Curate · Export — AI-powered YOLO dataset management toolkit","homepage_url":null,"license":"Other","open_issues":2,"watchers":10,"ai_summary":"A JavaScript-based toolkit for managing datasets with functionalities including analysis, learning, ingestion, curation, and export tailored for object detection tasks using YOLO models. Supports Docker containerization with auto-detection of hardware resources.","readme_excerpt":"### Docker\n\n```bash\npython3 builder.py --no-venv\n```\n\nThe builder generates `docker-compose.yml` automatically, depending on your configured hardware (GPU or CPU). \nEdit the volume paths to match your Frigate setup, then:\n\n```bash\ndocker compose up --build -d\n```\n\nSee [Docker Setup](#docker-setup) below for details.\n\n---\n\n## Requirements\n\nPython 3.8+ required. The builder creates a `.venv` and installs the base dependency (Pillow) automatically. All other dependencies are managed by ALICE and can be installed from the welcome page or Settings → System with one click:\n\n| Package | Purpose |\n|---------|---------|\n| **Pillow** | Image processing, pHash, format conversion (auto-installed by builder) |\n| **NumPy** | Numerical operations for dedup |\n| **inotify** | Filesystem watching on Linux (falls back to polling) |\n| **opencv-python-headless** | Video frame extraction |\n| **ONNX** | ONNX model format for export |\n| **onnxslim** | ONNX model optimization |\n| **onnxruntime** | ONNX Runtime — GPU or CPU variant auto-selected |\n| **PyTorch** | Deep learning framework — CUDA or CPU variant auto-selected |\n| **ultralytics** | YOLO model training & inference |\n\nALICE installs the correct PyTorch variant (CUDA or CPU) based on detected hardware. No manual torch installation needed.\n\n> **Note:** ALICE does **not** install NVIDIA drivers or CUDA. If you want GPU-accelerated training, install the [NVIDIA drivers](https://www.nvidia.com/Download/index.aspx) and [CUDA toolkit](https://developer.nvidia.com/cuda-toolkit) on your system before running ALICE.\n\n---\n\n## Docker Setup\n\nThe builder generates `docker-compose.yml` with GPU support auto-detected from your host:\n\n```bash\npython3 builder.py --no-venv\n```\n\nThis creates `docker-compose.yml` with the NVIDIA GPU block included if a GPU is detected, or CPU-only otherwise.\n\nEdit the volume paths to match your setup:\n\n```yaml\nvolumes:\n  - ./alice.conf:/app/alice.conf\n  - /path/to/datasets:/app/datasets\n  - /path/to/models:/app/models\n  - /path/to/frigate/clips:/app/clips:ro\n  - /path/to/frigate/exports:/app/exports:ro\n  - /path/to/frigate/frigate.db:/app/frigate.db:ro\n```\n\n> **Important:** The `frigate.db` volume must point to the actual **file**, not a directory. If the file doesn't exist on the host at the time of container creation, Docker will create a directory instead and ALICE won't be able to open the database.\n\nThen start:\n\n```bash\ndocker compose up --build -d\n```\n\nOn first run, open ALICE in your browser and install dependencies from the welcome page or Settings → System. Dependencies are persisted in a Docker volume across container restarts.\n\n> **Note:** GPU support in Docker requires [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) on the host. If running the builder on a machine without GPU, the generated compose file will be CPU-only.\n\n---\n\n## License\n\n[PolyForm Noncommercial 1.0.0](https://polyformproject.org/licenses/noncommercial/1.0.0/) — free for personal, non-commercial use. For commercial licensing, contact alice@it-link.net.","github_created_at":"2026-04-19T09:32:31+00:00","created_at":"2026-07-11T12:30:06.083067+00:00","updated_at":"2026-09-24T06:00:45.800435+00:00","categories":[{"slug":"data-retrieval","name":"Data & Retrieval","url":"https://www.graphcanon.com/categories/data-retrieval","markdown_url":"https://www.graphcanon.com/categories/data-retrieval.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/data-retrieval"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"ai-tools","name":"ai-tools"},{"slug":"annotation","name":"annotation"},{"slug":"computer-vision","name":"computer-vision"},{"slug":"dataset","name":"dataset"},{"slug":"frigate","name":"frigate"},{"slug":"nvidia-cuda","name":"nvidia-cuda"},{"slug":"nvidia-smi","name":"nvidia-smi"},{"slug":"object-detection","name":"object-detection"}],"trust":{"provenance":{"is_fork":false,"github_id":1214958134,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-09-24T06:00:44.943Z","maintenance":{"label":"Slowing","score":36,"methodology":"github_public_v1","releases_90d":0,"days_since_push":150,"last_release_at":"2026-04-26T17:51:53Z","stars_delta_30d":40,"open_issues_delta_30d":2},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T12:30:08.835Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-09-24T06:00:45.417Z"},"deploy":{"source":"dockerfile:Dockerfile","self_host":true,"observed_at":"2026-09-24T06:00:45.417Z","managed_saas":false},"languages":{"value":["javascript"],"source":"github.language","observed_at":"2026-09-24T06:00:45.417Z"},"has_docker":{"value":true,"source":"dockerfile:Dockerfile","observed_at":"2026-09-24T06:00:45.417Z"},"license_spdx":{"value":"Other","source":"github.license","observed_at":"2026-09-24T06:00:45.417Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["When you need a toolkit that seamlessly integrates with Docker for dataset management in conjunction with YOLO models.","For use cases requiring automatic hardware resource detection and optimized installation of dependencies like PyTorch, CUDA or CPU variants."],"when_not_to_use":["Do not use if your project does not require integration with the YOLO model for object detection tasks.","Avoid if you prefer tools that handle NVIDIA drivers and CUDA toolkit installations automatically without requiring manual pre-installation by users."],"source":"enrich:decision_facts","observed_at":"2026-07-14T18:05:07.108Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"alice is an AI-assisted tool for handling YOLO object detection datasets using Docker with auto-detection of hardware resources."}]}}