docs: Add Step 0 - Training image creation without the app

- Clarifies no app/Mac/Xcode needed for training data collection
- Documents photography setup for foils (angles, black bg, raking light)
- Documents photography setup for damage and non-foil cards
- Explains the cropping rule and why it matters for model accuracy
- Lists free cropping tools for Windows and cross-platform
- Image quantity guide (min vs recommended per class)
- Explains Scryfall image downloads as a NonFoil supplement

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

README.md

@@ -271,7 +271,83 @@ Also add `PrivacyInfo.xcprivacy` to the app target to satisfy Apple's privacy ma
 
 ## Part 5: Machine Learning Training — Mac Required for Final Step
 
-Training images can be collected on any device. Model training itself requires Create ML on a Mac.
+**You do not need the app, Xcode, or a Mac to collect training images.** All you need is physical cards and a phone camera. The only Mac-required step is the final model training in Create ML (Step 6).
+
+> **The app ships and works without any trained models.** Foil detection defaults to "None" and condition defaults to "NM". You can release a working app first and add models later via OTA update. Do not let missing training data block your first build.
+
+---
+
+### Step 0: How to Create Training Images (No App Required)
+
+This is the complete workflow for preparing training data on any computer.
+
+#### What You Need
+- Physical Magic cards (see shopping lists in Steps 1–3)
+- A phone with a decent camera (iPhone, Android — anything works)
+- A plain background: white card stock or black felt works best
+- A free photo cropping tool:
+  - **Windows:** Photos app (built-in crop) or Paint
+  - **Any platform:** [Squoosh](https://squoosh.app) (browser-based, free, no install)
+  - **Bulk cropping:** [IrfanView](https://www.irfanview.com) (Windows, free) or [XnConvert](https://www.xnview.com/en/xnconvert/) (cross-platform, free)
+
+#### Photography Setup
+
+**For Foil Cards:**
+1. Place the card on a **black background** (black felt or black paper).
+2. Use a single directional light source — a desk lamp or window at 45°.
+3. Take **5 photos of the same card** rotating it slightly between each shot so the light catches the foil at different angles. The AI must learn how the foil *moves*, not just how it looks flat.
+4. Example angles: flat-on, 15° left tilt, 15° right tilt, 15° top tilt, 15° bottom tilt.
+
+**For Non-Foil Cards:**
+1. Place on **white or grey background**.
+2. Even, diffused lighting (avoid strong reflections on the surface).
+3. 1–3 photos per card is sufficient.
+
+**For Condition/Damage:**
+1. Use **raking light** (light source almost parallel to the card surface) — this casts shadows that highlight scratches, dents, and bends far more clearly than direct light.
+2. For edge whitening: photograph against a **black background**.
+3. For chipping: photograph against a **white background**.
+4. Take a close-up — fill the frame with the card.
+
+#### Cropping Rule — Critical
+The app scans **cropped card images only** (no table, no background, no hand visible). Your training images must match this exactly or the model will learn the wrong thing.
+
+After photographing:
+1. Open the photo in your cropping tool.
+2. Crop tightly to the card border — include the full card frame but nothing outside it.
+3. It does not need to be pixel-perfect. Within 5–10px of the edge is fine.
+4. Save as `.jpg` at any reasonable resolution (at least 400×560px).
+
+#### Naming and Sorting
+File names do not matter — only the **folder** they are in matters. Save cropped images directly into the appropriate `IYmtg_Training/` subfolder:
+
+```
+IYmtg_Training/Foil_Data/Etched/   ← drop etched foil photos here
+IYmtg_Training/Foil_Data/Traditional/  ← drop traditional foil photos here
+IYmtg_Training/Condition_Data/Edges/Whitening/  ← drop edge whitening photos here
+```
+
+#### How Many Images Do You Need?
+| Goal | Minimum | Recommended |
+| :--- | :--- | :--- |
+| Test that training works | 10 per class | — |
+| Functional model, limited accuracy | 20 per class | — |
+| Solid production model | 30–50 per class | 50+ per class |
+| High-accuracy model | — | 100+ per class |
+
+More is always better. Variety matters more than quantity — different cards, different lighting, different tilt angles.
+
+#### Using Scryfall Images as a Supplement
+For **NonFoil** training data you can download card images directly from Scryfall instead of photographing them. This is automated — run:
+
+```bash
+pip install requests pillow
+python3 IYmtg_Automation/fetch_set_symbols.py
+```
+
+This downloads and crops set symbol images automatically. For general NonFoil card images, you can query the Scryfall API directly (`https://api.scryfall.com/cards/random`) and download the `normal` image URI. Downloaded Scryfall images are already cropped to the card frame and work well as NonFoil training data. Do not use Scryfall images for foil or damage training — they are flat renders with no foil or physical damage.
+
+---
 
 ### General Data Collection Protocol (Critical)
 The app sends **cropped** images (just the card, no background) to the AI. Your training data must match this.
@@ -283,8 +359,6 @@ The app sends **cropped** images (just the card, no background) to the AI. Your
 3.  **Sort:** Place the cropped image into the corresponding folder in `IYmtg_Training`.
 4.  **Quantity:** Aim for 30-50 images per category for robust results.
 
-> **Note:** The app works without trained models — foil detection defaults to "None" and condition defaults to "NM". You can ship a working app and add ML models later via OTA update.
-
 ### Step 1: The Master Foil Shopping List (Required for FoilEngine)
 Acquire one of each (~$50 total) to train the Foil Classifier. This ensures the app can distinguish complex modern foil types.