Alice 0.6.3

[0.6.3] - 25-04-2026

• Added bulk video frames export under Video section.
• Fixed image name clipping.
• Fixed builder venv setup.

Alice 0.6.2

v0.6.2 - Various bug fixes and improvements.

Alice 0.6.1

FP16 default off and respected from UI, also added Discord Community link in Help menu.

Alice 0.6.0

Switch license to PolyForm Noncommercial 1.0.0

Alice 0.5.8

GPU/CPU auto-detection, dynamic dependencies, other improvements.

Alice 0.5.7

v0.5.7 — Fix welcome/onboarding for future development

Alice 0.5.6

Added wget as requirements for docker.

Alice 0.5.5

Fixed

• Added empty alice.conf for docker compatibility.

Alice 0.5.4

Fixed

• F-string nested quotes compatibility for Python < 3.12 (Docker fix)

Alice 0.5.3

ALICE

Analyse · Learn · Ingest · Curate · Export

All-in-one AI-powered image annotation, training, and dataset management toolkit.

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Why?

I needed a tool to train a YOLO model for my cameras, using my own images, with the specific angles and scenarios around my house.
I couldn't find anything on the internet (or if it existed, I was probably too drunk to find it), so I built my own utility to fit my needs.
If you find it useful — enjoy. If not, well... cry me a river! :)

Quick Start

# Build and set up virtual environment
python3 builder.py

# Run
./alice.py

The builder assembles alice.py from source modules, creates a .venv with base dependencies, and patches the shebang so ./alice.py uses the venv automatically.

On first run, alice.conf is generated with sensible defaults. Models and datasets directories are created next to alice.py. Open http://localhost:8080 and you're ready to go — download a YOLO model from Settings, configure your Frigate paths if needed, and start working.

# Options
./alice.py --port 9090                  # custom port
./alice.py --conf /path/to/alice.conf   # custom config path

Docker

docker compose up -d

Edit docker-compose.yml to map your Frigate media, datasets, and models directories. See Docker Setup below.

Features

Viewer — Dataset Mode

Browse and annotate YOLO bounding boxes with a full canvas editor — draw, resize, move, delete, undo. Filter by split (train / val / empty) or by class. Gallery grid view, keyboard shortcuts, right-click context menus.

Gallery & Dataset Stats

Gallery grid view with thumbnail previews, split badges, and box counts. Dataset statistics panel shows total images, train/val distribution, annotation coverage, and class distribution.

Duplicate Detection

Perceptual hashing (pHash) with DCT-based 64-bit hashes. Multiprocessing-accelerated computation. Side-by-side comparison. Box-similarity dedup per camera. NMS cleanup for overlapping same-class boxes.

Viewer — Live Mode

Browse Frigate NVR event snapshots in real-time. Filter by camera and time window. Transfer snapshots directly into your training dataset with automatic WebP → JPG conversion.

Viewer — Video Mode

Frame-by-frame analysis of Frigate video exports. Seekbar, step controls, adjustable playback FPS. Automated frame scanner with AI detection for batch export.

AI Analysis

Run YOLO inference on any image across all three modes. Merge detected boxes into annotations (dedup by IoU > 0.5). Preview mode shows detections without saving. Live detection auto-runs as you navigate.

Training Pipeline

Five-step pipeline — each step toggleable, runnable individually or as a sequence:

Step	What it does
1. Export	Extract snapshots from Frigate SQLite DB → 90/10 train/val split
2. Dedup	Remove duplicates via pHash, box similarity, and NMS cleanup
3. Annotate	Auto-label all images using a teacher model
4. Train	Fine-tune student model with real-time metrics (loss, mAP50, mAP50-95)
5. Export ONNX	Convert to ONNX for deployment (FP16, dynamic batch)

All steps log to the Logs tab with COMPLETED / FAILED / STOPPED status.

Settings

All configuration in alice.conf — editable from the web UI or directly in the file. Built-in dependency checker with one-click install. Model downloader for YOLO11 and YOLOv8 variants.

Paths	AI Models
Training	System & Dependencies

Requirements

Python 3.8+ required. The builder creates a .venv and installs the base dependency (Pillow) automatically. All other dependencies are optional and can be installed from the Settings page in the web UI with one click:

Package	Purpose
Pillow	Image processing, pHash, format conversion (auto-installed by builder)
ultralytics	YOLO model training & inference
opencv-python-headless	Video frame extraction
numpy	Numerical operations for dedup
inotify	Filesystem watching on Linux (falls back to polling)

Note: ALICE does not install NVIDIA drivers or CUDA. If you want GPU-accelerated training and inference, install the NVIDIA drivers and CUDA toolkit on your system before running ALICE.

Docker Setup

services:
  alice:
    build: .
    container_name: alice
    ports:
      - "8080:8080"
    volumes:
      - ./alice.conf:/app/alice.conf
      - /path/to/datasets:/datasets
      - /path/to/models:/models
      - /path/to/frigate/media/clips:/clips:ro
      - /path/to/frigate/media/exports:/exports:ro
      - /path/to/frigate/config/frigate.db:/frigate.db:ro
    restart: unless-stopped
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

Note: The deploy.resources section requires NVIDIA Container Toolkit on the host. Remove it if running CPU-only.

Edit the volume paths to match your setup, then:

docker compose up -d

Your alice.conf paths should reference the container-side mount points (/datasets, /models, /clips, /exports, /frigate.db).

Dataset Structure

Alice expects standard YOLO format:

dataset/
├── images/
│   ├── train/*.jpg
│   └── val/*.jpg
├── labels/
│   ├── train/*.txt
│   └── val/*.txt
└── dataset.yaml          ← auto-generated by trainer

Building from Source

Alice is developed as modular source files assembled into a single alice.py:

python3 builder.py                     # → alice.py + .venv/
python3 builder.py -o /path/to/out.py  # custom output path
python3 builder.py --no-venv           # skip venv creation
python3 builder.py --check             # verify all modules and assets exist
python3 builder.py --list              # show resolved dependency order
python3 builder.py --strict            # abort on name conflicts

The builder:

1. Reads the import graph from from .module import statements in each src/*.py
2. Topologically sorts modules by dependency order
3. Strips all relative imports (redundant in the flat monolith namespace)
4. Injects CSS, JS, and HTML assets from src/assets/ into placeholder tokens
5. Writes a single self-contained alice.py
6. Creates a .venv with Pillow and patches the shebang

Re-running builder.py rebuilds alice.py but skips venv creation if .venv already exists.

Source modules live in src/ — see CONTRIBUTING.md for development workflow.

Keyboard Shortcuts

Key	Action
← →	Navigate images
D	Delete selected box
P	Set to Person class
A	AI Analyse (save)
M	Copy/Move dialog
E	Toggle panel
G	Gallery view
J	Jump to image #
Ctrl+Z	Undo
Ctrl+Scroll	Zoom
Space	Play/pause (video)

License

CC BY-NC 4.0 — free for personal, non-commercial use. For commercial licensing, contact alice@it-link.net.

Author

Simon Cirstoiu