Update README.md

README.md

@@ -1,6 +1,8 @@
 # if-curator-headless
 
-fork of [if-curator](https://github.com/ds-sebastian/if_curator) with automatic Frigate face training upload and Docker support.
+Headless fork of [if-curator](https://github.com/ds-sebastian/if_curator) with automatic Frigate face training upload and Docker support.
+
+> *A specialized tool to extract **high-quality, diverse** training images from your Immich library for Frigate's Face Recognition (ArcFace) and Object/State Classification models.*
 
 ## What This Adds
 
@@ -8,11 +10,77 @@ fork of [if-curator](https://github.com/ds-sebastian/if_curator) with automatic
 - **Auto-discover people** — processes all named people from Immich automatically instead of picking one at a time
 - **Frigate upload** — sends curated face crops directly to Frigate's face training API after processing
 - **People filtering** — skip specific people, whitelist only certain people, or set a minimum photo count
-- **Docker** — Dockerfile and compose config with NVIDIA GPU support, ready for TrueNAS
+- **Docker** — pre-built image with NVIDIA GPU support, ready for TrueNAS
 
-All of if-curator's original functionality (smart diversity, quality filtering, face alignment, blur rejection, etc.) is unchanged.
+All of if-curator's original functionality is unchanged.
 
-## New Environment Variables
+---
+
+## Why This Tool?
+
+> **"Diversity matters far more than volume."** — *Frigate Developer Tips*
+
+Training AI models on "bulk" data is often harmful. If you feed the model 50 images from the same 10-second video clip, it learns to recognize the *lighting and background*, not the actual *face* or *object*.
+
+`if-curator` solves this using **AI-powered diversity selection** and **quality filtering**:
+
+| Mode | Embedding Model | Algorithm |
+| :--- | :--- | :--- |
+| **👤 Face** | InsightFace (ArcFace) | K-Medoids Clustering + FPS + Hard Example Weighting |
+| **🐶 Object** | SigLIP (Vision Transformer) | K-Medoids Clustering + FPS |
+
+---
+
+## Features
+
+### Smart Selection
+- **Auto Diversity [Recommended]**: Clusters images by visual similarity, selects representatives from each cluster, then fills with maximally-diverse picks until redundancy starts (capped at 80)
+- **Standard (30 images)**: Balanced set using Smart Diversity
+- **Broad (100 images)**: Extensive set using Smart Diversity
+- **Custom Count**: You choose the limit
+
+### Quality Filtering
+Bad training data hurts ArcFace models. Images are automatically rejected if they are:
+- **Blurry** — Laplacian variance below threshold
+- **Grayscale / IR** — ArcFace is trained on color images only
+- **Over/Underexposed** — Washed-out or too dark to use
+- **Low confidence** — Partial or occluded face detections
+- **Too small** — Faces under 100px (configurable) lack features
+
+### Face Recognition Prep
+- Uses InsightFace embeddings on **face crops** (not full images — avoids wrong-face in group photos)
+- **Hard example prioritization** — unusual angles, sunglasses, and low-confidence detections are biased for selection
+- **Face alignment** via InsightFace landmarks (standard 112×112 ArcFace input)
+- Downloads **full-resolution** originals for final crops (falls back to JPEG preview for HEIC/RAW)
+
+### Object/State Classification Prep
+- Uses **SigLIP** embeddings for semantic diversity
+- **YOLOv9c** to detect and crop specific objects (dogs, cars, etc.)
+- Captures variation in poses, lighting, and backgrounds
+
+> **Note:** Frigate does not support uploading custom images for object classification training via the UI or API. Object mode crops are saved to disk for manual YOLO model training.
+
+### Performance
+- Concurrent thumbnail downloads (8 parallel workers)
+- Batch-capable SigLIP embeddings for GPU efficiency
+- Optional disk-based embedding cache for faster re-runs
+- Multi-person batch mode
+
+---
+
+## Requirements
+
+- **NVIDIA GPU** (recommended) — auto-detects CUDA for faster embedding computation. CPU mode is available via `FORCE_CPU=true` but significantly slower.
+- **Python 3.12+**
+- **[uv](https://astral.sh/uv/)**
+- **Immich Server** (v1.106+)
+- **Frigate** (v0.16+, for face recognition API — optional)
+
+---
+
+## Environment Variables
+
+### New in This Fork
 
 | Variable | Default | What it does |
 | :--- | :--- | :--- |
@@ -25,9 +93,34 @@ All of if-curator's original functionality (smart diversity, quality filtering,
 | `MIN_FACE_COUNT` | `3` | Minimum photos required to process a person |
 | `OBJECT_CLASS` | `dog` | Object type (only for object mode) |
 
-All original if-curator variables (`IMMICH_URL`, `API_KEY`, `FORCE_CPU`, etc.) still work.
+### Original if-curator Variables
+
+| Variable | Default | Description |
+| :--- | :--- | :--- |
+| `IMMICH_URL` | *(prompted)* | Full URL to Immich (e.g. `http://192.168.1.10:2283`) |
+| `API_KEY` | *(prompted)* | Your Immich API Key |
+| `FORCE_CPU` | `false` | Disable GPU acceleration |
+| `MIN_FACE_WIDTH` | `100` | Minimum face crop size (pixels) |
+| `BLUR_THRESHOLD` | `100.0` | Laplacian variance threshold for blur detection |
+| `MIN_CONFIDENCE` | `0.7` | Minimum Immich detection confidence |
+| `MAX_AUTO_IMAGES` | `80` | Safety cap for auto-diversity mode |
+| `FACE_MARGIN` | `0.15` | Crop margin around face (fraction) |
+| `USE_FULL_RESOLUTION` | `true` | Download originals for final crops |
+| `ENABLE_FACE_ALIGNMENT` | `true` | Align faces to ArcFace 112×112 format |
+| `ENABLE_CACHE` | `false` | Cache embeddings to disk for faster re-runs |
+| `CACHE_DIR` | `.if_cache` | Directory for embedding cache |
+
+---
 
 ## Docker
 
-ghcr.io/sudolulo/if-curator-headless:latest
+See [sudolulo/if-curator-headless](https://github.com/sudolulo/if-curator-docker) for the Dockerfile, compose config, and setup instructions.
 
+---
+
+## Local Install
+
+```bash
+git clone https://github.com/sudolulo/if_curator_headless.git
+cd if_curator_headless
+uv sync