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feat: Create dynamic in-app training status guide

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Implements a new UI to show recommended image counts for ML training.
Uses color-coded indicators (orange/green/blue) for Functional, Solid,
and High-Accuracy thresholds across all 28 training categories
(Foil, Stamp, and Condition models). Critical damage types (Inking,
Rips, Water Damage) carry higher recommended counts to minimise false
positives on NM grades. Accessible via a "?" toolbar button in Library.
Bumps app version to 1.1.0.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Mike Wichers
2026-03-05 13:21:45 -05:00
parent bb4ad9eb7e
commit 5da5614a10
5 changed files with 179 additions and 2 deletions
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IYmtg_App_iOS/Features/Help/Models/TrainingStatus.swift Normal file
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// IYmtg_App_iOS/Features/Help/Models/TrainingStatus.swift
import SwiftUI
struct TrainingCategory: Identifiable {
let id = UUID()
let name: String
let group: String
let functionalCount: Int
let solidCount: Int
let highAccuracyCount: Int
}
class TrainingGuideViewModel: ObservableObject {
@Published var categories: [TrainingCategory] = [
// MARK: Foil Model (Image Classification)
// 13 classes — false negatives cause wrong foil label; all classes need balanced data.
.init(name: "NonFoil", group: "Foil Model", functionalCount: 15, solidCount: 50, highAccuracyCount: 100),
.init(name: "Traditional", group: "Foil Model", functionalCount: 15, solidCount: 50, highAccuracyCount: 100),
.init(name: "Etched", group: "Foil Model", functionalCount: 15, solidCount: 50, highAccuracyCount: 100),
.init(name: "PreModern", group: "Foil Model", functionalCount: 15, solidCount: 50, highAccuracyCount: 100),
.init(name: "Textured", group: "Foil Model", functionalCount: 15, solidCount: 50, highAccuracyCount: 100),
.init(name: "Galaxy", group: "Foil Model", functionalCount: 15, solidCount: 50, highAccuracyCount: 100),
.init(name: "Surge", group: "Foil Model", functionalCount: 15, solidCount: 50, highAccuracyCount: 100),
.init(name: "Oil Slick", group: "Foil Model", functionalCount: 15, solidCount: 50, highAccuracyCount: 100),
.init(name: "Step & Compleat", group: "Foil Model", functionalCount: 15, solidCount: 50, highAccuracyCount: 100),
.init(name: "Halo", group: "Foil Model", functionalCount: 15, solidCount: 50, highAccuracyCount: 100),
.init(name: "Confetti", group: "Foil Model", functionalCount: 15, solidCount: 50, highAccuracyCount: 100),
.init(name: "Neon Ink", group: "Foil Model", functionalCount: 15, solidCount: 50, highAccuracyCount: 100),
.init(name: "Fracture", group: "Foil Model", functionalCount: 15, solidCount: 50, highAccuracyCount: 100),
// MARK: Stamp Model (Image Classification)
// Detects promo stamp presence. Binary classification — easy to train quickly.
.init(name: "Stamped (Promo)", group: "Stamp Model", functionalCount: 15, solidCount: 50, highAccuracyCount: 100),
.init(name: "Clean (No Stamp)", group: "Stamp Model", functionalCount: 15, solidCount: 50, highAccuracyCount: 100),
// MARK: Condition Model (Object Detection — requires bounding-box annotations in Create ML)
// Higher minimums than image classification because each image needs manual annotation.
// Surface defects
.init(name: "Light Scratches", group: "Condition Model", functionalCount: 20, solidCount: 50, highAccuracyCount: 100),
.init(name: "Clouding", group: "Condition Model", functionalCount: 20, solidCount: 50, highAccuracyCount: 100),
.init(name: "Dirt", group: "Condition Model", functionalCount: 20, solidCount: 50, highAccuracyCount: 100),
.init(name: "Dents", group: "Condition Model", functionalCount: 20, solidCount: 50, highAccuracyCount: 100),
// Edge defects
.init(name: "Whitening (Edges)", group: "Condition Model", functionalCount: 20, solidCount: 50, highAccuracyCount: 100),
.init(name: "Chipping (Edges)", group: "Condition Model", functionalCount: 20, solidCount: 50, highAccuracyCount: 100),
.init(name: "Corner Wear", group: "Condition Model", functionalCount: 20, solidCount: 50, highAccuracyCount: 100),
// Structure defects
.init(name: "Creases", group: "Condition Model", functionalCount: 20, solidCount: 50, highAccuracyCount: 100),
.init(name: "Shuffle Bend", group: "Condition Model", functionalCount: 20, solidCount: 50, highAccuracyCount: 100),
.init(name: "Binder Dents", group: "Condition Model", functionalCount: 20, solidCount: 50, highAccuracyCount: 100),
// Critical damage — a single false positive will downgrade a NM card to Damaged.
// Train these more aggressively to minimise false positives.
.init(name: "Water Damage", group: "Condition Model", functionalCount: 20, solidCount: 75, highAccuracyCount: 150),
.init(name: "Inking", group: "Condition Model", functionalCount: 20, solidCount: 75, highAccuracyCount: 150),
.init(name: "Rips", group: "Condition Model", functionalCount: 20, solidCount: 75, highAccuracyCount: 150),
]
var groups: [String] {
var seen = Set<String>()
return categories.compactMap { seen.insert($0.group).inserted ? $0.group : nil }
}
func categories(for group: String) -> [TrainingCategory] {
categories.filter { $0.group == group }
}
}