Upload CoreML detector package

Add Detector224.mlpackage, conversion config, benchmark/parity evidence, checksums, license, notice, and model card. SwiftPM package: https://github.com/mweinbach/OCRCoreMLDetector

Detector224.mlpackage/Data/com.apple.CoreML/model.mlmodel

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+size 207422

Detector224.mlpackage/Data/com.apple.CoreML/weights/weight.bin

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Detector224.mlpackage/Manifest.json

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+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "34FA8F82-C97F-476C-87BB-78B9BE530514": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        },
+        "E548B89C-6ABA-473C-83A4-7AD37AA2B934": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        }
+    },
+    "rootModelIdentifier": "34FA8F82-C97F-476C-87BB-78B9BE530514"
+}

LICENSE

@@ -0,0 +1,243 @@
+All binary model files are licensed under NVIDIA Open Model License Agreement.
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+   

NOTICE

@@ -0,0 +1,6 @@
+Licensed by NVIDIA Corporation under the NVIDIA Open Model License.
+
+This package includes a CoreML conversion of the detector component from
+NVIDIA Nemotron OCR v2. The converted model weights inherit the NVIDIA Open
+Model License. The Swift wrapper code is provided for loading and running the
+converted CoreML package in Apple apps.

README.md

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+---
+license: other
+license_name: nvidia-open-model-license
+license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
+base_model: nvidia/nemotron-ocr-v2
+library_name: coreml
+tags:
+- coreml
+- ocr
+- text-detection
+- apple-silicon
+- apple-neural-engine
+- swift
+---
+
+# OCR CoreML Detector 224
+
+`Detector224.mlpackage` is a batch-1 CoreML conversion of the detector stage
+from [NVIDIA Nemotron OCR v2](https://huggingface.co/nvidia/nemotron-ocr-v2).
+It is intended for Apple-device OCR pipelines that need a packaged text
+detection model and will implement their own post-processing, recognition, and
+layout stages.
+
+SwiftPM package:
+[github.com/mweinbach/OCRCoreMLDetector](https://github.com/mweinbach/OCRCoreMLDetector)
+
+## What This Is
+
+- Source model: `nvidia/nemotron-ocr-v2`, `v2_english` detector
+- CoreML artifact: `Detector224.mlpackage`
+- Conversion config: `experiments/224_detector_ane_decomposed_int8_768`
+- Input size: `768 x 768`
+- Batch size: `1`
+- Weight quantization: int8 per-channel linear symmetric
+- Compute precision: fp16
+- Minimum deployment target used during conversion: iOS 18
+
+This is not a complete OCR system. The package returns detector tensors only.
+Downstream code still needs thresholding, rotated-box decoding,
+non-maximum suppression, crop/rectify, recognition, and reading-order/layout
+logic.
+
+## Files
+
+| file | purpose |
+|---|---|
+| `Detector224.mlpackage/` | CoreML model package |
+| `conversion_config.yaml` | conversion/benchmark config used to create the artifact |
+| `bench.md` | local CoreML latency results |
+| `parity.json` | PyTorch-vs-CoreML parity summary |
+| `checksums.sha256` | SHA-256 checksums for the CoreML package files |
+| `LICENSE` | NVIDIA Open Model License plus Apache 2.0 source license text from upstream |
+| `NOTICE` | redistribution attribution notice |
+
+## Input Contract
+
+The model expects one CoreML input named `image`:
+
+- shape: `Float32[1, 3, 768, 768]`
+- layout: RGB planar
+- normalization: pixel values in `[0, 1]`
+
+The SwiftPM wrapper linked above includes a helper that converts a `CGImage` to
+this tensor shape.
+
+## Output Contract
+
+The model returns:
+
+| output | shape | meaning |
+|---|---:|---|
+| `prob` | `Float32[1, 192, 192]` | text probability map |
+| `rboxes` | `Float32[1, 192, 192, 5]` | rotated-box geometry |
+| `features` | `Float32[1, 128, 192, 192]` | detector feature map |
+
+## Local Performance
+
+Measured on the bundled sample image after warmup:
+
+| compute units | median prediction latency |
+|---|---:|
+| `ALL` | 13.53 ms |
+| `CPU_AND_GPU` | 13.65 ms |
+| `CPU_AND_NE` | 54.51 ms |
+| `CPU_ONLY` | 298.28 ms |
+
+For lowest single-image latency in the current test environment, use GPU or
+CoreML `ALL`. CPU+ANE is available but slower for this detector.
+
+## Use From Swift
+
+Add the Swift package:
+
+```swift
+.package(url: "https://github.com/mweinbach/OCRCoreMLDetector.git", from: "0.1.0")
+```
+
+Then:
+
+```swift
+import CoreML
+import OCRCoreMLDetector
+
+let detector = try OCRDetector(computeUnits: .cpuAndGPU)
+let prediction = try detector.prediction(for: cgImage)
+
+let prob = prediction.output.prob
+let rboxes = prediction.output.rboxes
+let features = prediction.output.features
+```
+
+## License
+
+The converted model weights inherit the
+[NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).
+The upstream source code and helper scripts are Apache 2.0. See `LICENSE` and
+`NOTICE` for redistribution terms and attribution.

bench.md

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+# Benchmark — `model.mlpackage`
+
+- input `image` shape = [1, 3, 768, 768]
+
+| compute unit | mean | median | p95 | p99 | min | max | stdev |
+|---|---:|---:|---:|---:|---:|---:|---:|
+| `ALL` | 13.58 ms | 13.53 ms | 13.93 ms | 13.96 ms | 13.24 ms | 13.97 ms | 0.22 ms |
+| `CPU_AND_GPU` | 13.68 ms | 13.65 ms | 13.90 ms | 13.92 ms | 13.45 ms | 13.93 ms | 0.14 ms |
+| `CPU_AND_NE` | 54.54 ms | 54.51 ms | 54.69 ms | 54.72 ms | 54.40 ms | 54.73 ms | 0.10 ms |
+| `CPU_ONLY` | 300.02 ms | 298.28 ms | 307.53 ms | 307.64 ms | 295.91 ms | 307.67 ms | 4.10 ms |
+
+**Fastest:** `ALL` at 13.53 ms median.

checksums.sha256

@@ -0,0 +1,3 @@
+d771fbfe0d09de4f59ee08d2b14922542b416818a830e23a17315279a90af378  Detector224.mlpackage/Manifest.json
+9a90489494e9d6880c8ee3d98e63f114b1c8bf7c0fb31a5b92cecfcea0d59546  Detector224.mlpackage/Data/com.apple.CoreML/model.mlmodel
+92585a57d37ec157d773b67bfbdc497d1037b947353edeaa97c54300d896c295  Detector224.mlpackage/Data/com.apple.CoreML/weights/weight.bin

conversion_config.yaml

@@ -0,0 +1,20 @@
+name: detector_ane_decomposed_int8_768
+description: ANE-decomposed detector at 768 px, int8 per-channel weight quantization.
+model: { module: detector, checkpoint: v2_english }
+detector_input:
+  batch_size: 1
+  infer_length: 768
+  ane_decomposed: true
+conversion:
+  compute_precision: fp16
+  compute_units: CPU_AND_NE
+  minimum_deployment_target: iOS18
+  quantize_nbits: 8
+  quantize_granularity: per_channel
+  quantize_mode: linear_symmetric
+compare:
+  atol: 2.0e+2
+  rtol: 5.0e-1
+  num_samples: 2
+  seed: 0
+  include_real_image: true

parity.json

@@ -0,0 +1,93 @@
+{
+  "passes": true,
+  "outputs": [
+    {
+      "name": "prob",
+      "shape": [
+        1,
+        192,
+        192
+      ],
+      "dtype": "float32",
+      "max_abs_diff": 0.14600548148155212,
+      "mean_abs_diff": 0.0017581588815068648,
+      "cosine_similarity": 0.9994850579560237,
+      "passes": true,
+      "finiteness_mismatches": 0
+    },
+    {
+      "name": "rboxes",
+      "shape": [
+        1,
+        192,
+        192,
+        5
+      ],
+      "dtype": "float32",
+      "max_abs_diff": 98.17948150634766,
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