gen-cards: regenerate Use-it block
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README.md
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@@ -34,7 +34,7 @@ needs no NMS, YOLOX is the classic `score = obj · cls` + **per-class NMS** pipe
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<!-- gen-cards:use-it begin id=yolox-s (managed by scripts/gen-cards — edit cards.json / QuickStart.swift, not this block) -->
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## Use it
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**New to Core AI? [Start with CoreAIKit 0.7.
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⚡ **One line** — run the kit's task op on this model
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(`import CoreAIOps`; no session, no model plumbing, downloads on first use):
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let boxes = try await CoreAI.detect(inImageAt: url, options: .model("yolox-s"))
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```
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Every op, one shape — [Cookbook](https://github.com/john-rocky/coreai-kit/blob/0.7.
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▶️ **Run it (source)** — the [DetectCamera runner](https://github.com/john-rocky/coreai-kit/tree/0.7.
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(real-time object detection on the zero-copy camera path):
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```bash
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git clone --branch 0.7.
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export DEVELOPER_DIR=/Applications/Xcode-27.0.0-RC.app/Contents/Developer
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open -a /Applications/Xcode-27.0.0-RC.app coreai-kit/Examples/DetectCamera/DetectCamera.xcodeproj
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# → Run, then pick "YOLOX" in the model picker
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// detections: [Detection] — label, score, normalized box (top-left origin)
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```
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The take-home is [`Examples/DetectCamera/Sources/QuickStart.swift`](https://github.com/john-rocky/coreai-kit/blob/0.7.
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— this exact code as one typed function, no UI; the CLI is an argument shell over it, and
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the GUI runs the same detector per camera frame on a zero-copy pixel-buffer fast path.
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YOLOX is a dense detector — `KitDetector` runs the obj·cls threshold + per-class NMS
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**Integration checklist**
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- SPM: `https://github.com/john-rocky/coreai-kit` (exact **0.7.
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- Info.plist: `NSCameraUsageDescription` — only for the live camera; the snippet needs none
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- Entitlements: none needed
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- First run downloads the model — ~36 MB (Mac) / ~36 MB (iPhone) — then it loads from the
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<!-- gen-cards:use-it begin id=yolox-s (managed by scripts/gen-cards — edit cards.json / QuickStart.swift, not this block) -->
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## Use it
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**New to Core AI? [Start with CoreAIKit 0.7.3](https://github.com/john-rocky/coreai-kit#readme).** Follow its requirements and first-run steps for `qwen3-0.6b`, then open the same release's [ChatDemo](https://github.com/john-rocky/coreai-kit/tree/0.7.3/Examples/ChatDemo). The README records the tested OS/SDK and download size; model and device coverage is stated per example.
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⚡ **One line** — run the kit's task op on this model
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(`import CoreAIOps`; no session, no model plumbing, downloads on first use):
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let boxes = try await CoreAI.detect(inImageAt: url, options: .model("yolox-s"))
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```
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Every op, one shape — [Cookbook](https://github.com/john-rocky/coreai-kit/blob/0.7.3/docs/COOKBOOK.md).
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▶️ **Run it (source)** — the [DetectCamera runner](https://github.com/john-rocky/coreai-kit/tree/0.7.3/Examples/DetectCamera)
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(real-time object detection on the zero-copy camera path):
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```bash
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git clone --branch 0.7.3 --depth 1 https://github.com/john-rocky/coreai-kit
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export DEVELOPER_DIR=/Applications/Xcode-27.0.0-RC.app/Contents/Developer
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open -a /Applications/Xcode-27.0.0-RC.app coreai-kit/Examples/DetectCamera/DetectCamera.xcodeproj
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# → Run, then pick "YOLOX" in the model picker
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// detections: [Detection] — label, score, normalized box (top-left origin)
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```
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The take-home is [`Examples/DetectCamera/Sources/QuickStart.swift`](https://github.com/john-rocky/coreai-kit/blob/0.7.3/Examples/DetectCamera/Sources/QuickStart.swift)
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— this exact code as one typed function, no UI; the CLI is an argument shell over it, and
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the GUI runs the same detector per camera frame on a zero-copy pixel-buffer fast path.
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YOLOX is a dense detector — `KitDetector` runs the obj·cls threshold + per-class NMS
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**Integration checklist**
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- SPM: `https://github.com/john-rocky/coreai-kit` (exact **0.7.3**) → product **CoreAIKitVision**
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- Info.plist: `NSCameraUsageDescription` — only for the live camera; the snippet needs none
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- Entitlements: none needed
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- First run downloads the model — ~36 MB (Mac) / ~36 MB (iPhone) — then it loads from the
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