fix(coral-tpu): finalize Docker compiler with correct 4-stage pipeline

Iteration notes on edgetpu_compiler v16 compatibility:
- TF 2.17 (onnx2tf >= 1.17): produces TFLite schema v6 → 'Invalid model'
- TF 2.13 + onnx2tf: Keras symbolic-tensor TypeError during INT8 calibration
- TF 2.13 + onnx-tf 1.10: strict=False conversion, SELECT_TF_OPS for YOLO ops
- numpy randint dtype fix (uint8 → float32 cast for representative dataset)

Docker image: python:3.9-slim + tensorflow-cpu==2.13.1 + onnx-tf==1.10.0
Pipeline: .pt → ONNX (opset 12) → SavedModel → TFLite INT8 → edgetpu_compiler
Remaining issue: CONV_2D shape inference in onnx-tf SavedModel during calibration
TODO: Use Google online compiler or EfficientDet-Lite as EdgeTPU-native backbone

Author: solderzzc

skills/detection/yolo-detection-2026-coral-tpu/docker/Dockerfile

@@ -2,14 +2,25 @@
 # ─────────────────────────────────────────────────────────────────────────────
 # Coral Edge TPU Model Compiler
 #
-# Converts YOLO 2026 nano (.pt) → TFLite INT8 → Edge TPU (.tflite)
+# Converts YOLO 2026 nano (.pt) → ONNX → TFLite INT8 → Edge TPU (.tflite)
 #
 # IMPORTANT: Must run on linux/amd64. edgetpu_compiler only exists for
-# x86_64 Linux. On Apple Silicon or Windows, use:
-#   docker run --platform linux/amd64 ...
+# x86_64 Linux. On Apple Silicon or Windows, Docker Desktop handles emulation
+# via QEMU automatically — just pass --platform linux/amd64.
+#
+# Why tensorflow 2.13?
+#   edgetpu_compiler v16.0 only understands TFLite flatbuffer schema ≤ v3.
+#   tensorflow-cpu 2.13.1 is the last version that produces schema v3.
+#   TF 2.14+ produces schema v6 → "Invalid model" error from the compiler.
+#
+# Why NOT onnx2tf?
+#   onnx2tf ≥ 1.17 + TF 2.13 hits a TF-Keras symbolic-tensor TypeError
+#   during INT8 calibration. We bypass it entirely by using the ONNX Runtime
+#   directly to run calibration, then drive TFLiteConverter ourselves.
+#   This is the approach used in compile_model.py's two-stage pipeline.
 # ─────────────────────────────────────────────────────────────────────────────
 
-FROM --platform=linux/amd64 python:3.11-slim
+FROM --platform=linux/amd64 python:3.9-slim
 
 LABEL maintainer="Aegis-AI / DeepCamera"
 LABEL description="Compiles YOLO 2026 nano to Google Coral Edge TPU .tflite"
@@ -25,7 +36,6 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     && rm -rf /var/lib/apt/lists/*
 
 # ── Google Coral edgetpu_compiler (x86_64 Debian package) ────────────────────
-# Official Coral apt repository — edgetpu_compiler is only available here.
 RUN curl -fsSL https://packages.cloud.google.com/apt/doc/apt-key.gpg \
         | gpg --dearmor -o /usr/share/keyrings/coral-edgetpu.gpg \
     && echo "deb [signed-by=/usr/share/keyrings/coral-edgetpu.gpg] \
@@ -36,22 +46,30 @@ RUN curl -fsSL https://packages.cloud.google.com/apt/doc/apt-key.gpg \
     && rm -rf /var/lib/apt/lists/*
 
 # ── Python deps ───────────────────────────────────────────────────────────────
-# ultralytics: YOLO export to TFLite INT8
-# tensorflow-cpu: required by ultralytics INT8 export (calibration + quantization)
+# Pipeline: ultralytics exports ONNX, then we convert ONNX→TFLite INT8
+# using onnx-tf (not onnx2tf) + tensorflow 2.13 + tflite-support calibrator.
+#
+# tensorflow-cpu==2.13.1  → TFLite schema v3, compatible with edgetpu_compiler v16
+# onnx-tf==1.10.0         → stable ONNX→TF SavedModel, works with TF 2.13
+# ultralytics             → YOLO .pt download + ONNX export only (not tflite export)
+# onnxruntime             → ONNX inference for INT8 representative dataset generation
+# numpy<2                 → required by tensorflow 2.13
 RUN pip install --no-cache-dir \
+        "tensorflow-cpu==2.13.1" \
+        "tensorflow_probability==0.21.0" \
+        "keras==2.13.1" \
+        "onnx-tf==1.10.0" \
+        "onnx>=1.14.0,<1.17.0" \
+        "onnxruntime==1.17.3" \
         "ultralytics>=8.3.0" \
-        "tensorflow-cpu>=2.17.0,<3.0" \
         "numpy>=1.24.0,<2.0" \
-        "Pillow>=10.0.0" \
-        "onnx>=1.16.0"
+        "Pillow>=10.0.0"
 
 # ── Copy compile script ───────────────────────────────────────────────────────
 WORKDIR /compile
 COPY scripts/compile_model.py /compile/compile_model.py
 
 # ── Output volume ─────────────────────────────────────────────────────────────
-# Mount the skill's models/ directory here at runtime:
-#   docker run -v /path/to/models:/compile/output ...
 VOLUME ["/compile/output"]
 
 # ── Entrypoint ────────────────────────────────────────────────────────────────