Add CoreML and tflite images

v1/README.md

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+# Billiards YOLOv8n Model - Exported Formats (1280x1280)
+
+## Successfully Exported Models
+
+### CoreML
+- **File**: `billiards-yolov8n.mlpackage`
+- **Input Size**: 1280x1280 RGB
+- **Size**: ~6.0 MB
+- **Status**: ✅ Successfully exported
+- **Platform**: iOS/macOS
+- **Description**: CoreML package ready for Apple device inference
+
+### ONNX
+- **File**: `billiards-yolov8n.onnx`
+- **Input Size**: 1280x1280 (1x3x1280x1280 BCHW)
+- **Format**: ONNX opset 19
+- **Size**: 12.2 MB
+- **Status**: ✅ Successfully exported
+- **Platform**: Cross-platform
+- **Description**: Universal format, can be used with ONNXRuntime or converted to other formats
+
+### TFLite (Float32)
+- **File**: `billiards-yolov8n_float32.tflite`
+- **Input Size**: 1280x1280
+- **Size**: 12 MB
+- **Status**: ✅ Successfully exported
+- **Platform**: Android, Edge devices, TensorFlow Lite runtime
+- **Description**: Full precision TFLite model
+
+### TFLite (Float16)
+- **File**: `billiards-yolov8n_float16.tflite`
+- **Input Size**: 1280x1280
+- **Size**: 6.0 MB
+- **Status**: ✅ Successfully exported
+- **Platform**: Android, Edge devices, TensorFlow Lite runtime
+- **Description**: Half precision TFLite model (smaller, slightly less accurate)
+
+## Model Details
+
+- **Base Model**: YOLOv8n (Nano)
+- **Training**: Custom billiards dataset
+- **Input Size**: 1280x1280x3 RGB
+- **Task**: Object detection
+- **Output Shape**: (1, 6, 33600) - 6 classes, 33600 detections at 1280 resolution
+
+## Conversion Notes
+
+### CoreML
+- Converted directly from PyTorch using Ultralytics export
+- Ready for use with Core ML framework on iOS/macOS
+- Optimized for Apple Neural Engine
+
+### ONNX
+- Exported with opset 19 for broad compatibility
+- Can be used as an intermediate format for other conversions
+- Works with ONNXRuntime on all platforms
+
+### TFLite
+- Converted via ONNX → TensorFlow SavedModel → TFLite pipeline using Docker
+- Used `onnx2tf` tool with TensorFlow 2.19
+- Two variants provided:
+  - Float32: Full precision, larger file
+  - Float16: Half precision, 50% smaller, minimal accuracy loss
+
+## Usage Examples
+
+### CoreML (Swift/iOS)
+```swift
+let model = try billiards_yolov8n()
+let input = billiards_yolov8nInput(image: pixelBuffer)
+let output = try model.prediction(input: input)
+```
+
+### ONNX (Python)
+```python
+import onnxruntime as ort
+session = ort.InferenceSession("billiards-yolov8n.onnx")
+output = session.run(None, {"images": input_tensor})
+```
+
+### TFLite (Python)
+```python
+import tensorflow as tf
+interpreter = tf.lite.Interpreter("billiards-yolov8n_float32.tflite")
+interpreter.allocate_tensors()
+# Run inference...
+```
+
+## File Sizes Summary
+
+| Format | File | Size |
+|--------|------|------|
+| PyTorch | billiards-yolov8n.pt | 5.9 MB |
+| CoreML | billiards-yolov8n.mlpackage | ~6.0 MB |
+| ONNX | billiards-yolov8n.onnx | 12.2 MB |
+| TFLite (FP32) | billiards-yolov8n_float32.tflite | 12 MB |
+| TFLite (FP16) | billiards-yolov8n_float16.tflite | 6.0 MB |

v1/billiards-yolov8n.mlpackage/Data/com.apple.CoreML/model.mlmodel

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v1/billiards-yolov8n.mlpackage/Data/com.apple.CoreML/weights/weight.bin

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v1/billiards-yolov8n.mlpackage/Manifest.json

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+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "576D694C-5517-406C-A111-0C8159D2F453": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        },
+        "57BABC12-6BC5-41F6-85A8-007CEECFCE2E": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        }
+    },
+    "rootModelIdentifier": "576D694C-5517-406C-A111-0C8159D2F453"
+}

v1/billiards-yolov8n.onnx

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v1/billiards-yolov8n_float16.tflite

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v1/billiards-yolov8n_float32.tflite

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