diff --git a/scripts/build_trt_engines.sh b/scripts/build_trt_engines.sh
index 781ec5b..0c7dae1 100755
--- a/scripts/build_trt_engines.sh
+++ b/scripts/build_trt_engines.sh
@@ -6,7 +6,11 @@
 # Profiles must match src/arcface_embedder.hpp and src/scrfd_decoder.hpp:
 #   ArcFace    : min=1x3x112x112 opt=Nx3x112x112 max=Nx3x112x112  (N = embed batch)
 #   SCRFD      : 1x3x640x640 (fixed; we letterbox to this)
-#   TransNetV2 : 1x100x27x48x3 (fixed; scene detector window), input tensor "input"
+#   TransNetV2 : 1x100x27x48x3 (fixed; scene detector window)
+#
+# Input tensor names are read from each ONNX model at runtime rather than
+# hardcoded, since they differ between models (LVFace-B: "data", arcface_r18:
+# "input", arcface_w600k_{r50,mbf}: "input.1").
 #
 # These trtexec-built engines are *not* picked up by the ORT TRT EP cache —
 # ORT uses its own engine format. The point of this script is:
@@ -27,24 +31,43 @@ SCENE_MODEL="${SCENE_MODEL:-$MODELS/transnetv2.onnx}"
 
 run() { echo "+ $*"; "$@"; }
 
-echo "== ArcFace =="
+# The input tensor name is not the same across models — LVFace-B uses "data",
+# arcface_r18 uses "input", and arcface_w600k_{r50,mbf} use "input.1". A
+# hardcoded name makes trtexec fail with "Cannot find input tensor with name
+# ...", so read it from the model instead.
+input_name() {
+    python3 - "$1" <<'PY'
+import sys
+try:
+    import onnxruntime as ort
+except ImportError:
+    sys.exit("onnxruntime is required to read the model's input name")
+sess = ort.InferenceSession(sys.argv[1], providers=["CPUExecutionProvider"])
+print(sess.get_inputs()[0].name)
+PY
+}
+
+ARCFACE_IN="$(input_name "$ARCFACE_MODEL")"
+SCRFD_IN="$(input_name "$SCRFD_MODEL")"
+
+echo "== ArcFace == (input tensor: $ARCFACE_IN)"
 run trtexec \
     --onnx="$ARCFACE_MODEL" \
     --fp16 \
-    --minShapes=input.1:1x3x112x112 \
-    --optShapes=input.1:${EMBED_BATCH}x3x112x112 \
-    --maxShapes=input.1:${EMBED_BATCH}x3x112x112 \
+    --minShapes="$ARCFACE_IN":1x3x112x112 \
+    --optShapes="$ARCFACE_IN":${EMBED_BATCH}x3x112x112 \
+    --maxShapes="$ARCFACE_IN":${EMBED_BATCH}x3x112x112 \
     --saveEngine="$OUT/arcface.$(basename "$ARCFACE_MODEL" .onnx).b${EMBED_BATCH}.fp16.engine" \
     --useCudaGraph
 
 echo
-echo "== SCRFD =="
+echo "== SCRFD == (input tensor: $SCRFD_IN)"
 run trtexec \
     --onnx="$SCRFD_MODEL" \
     --fp16 \
-    --minShapes=input.1:1x3x640x640 \
-    --optShapes=input.1:1x3x640x640 \
-    --maxShapes=input.1:1x3x640x640 \
+    --minShapes="$SCRFD_IN":1x3x640x640 \
+    --optShapes="$SCRFD_IN":1x3x640x640 \
+    --maxShapes="$SCRFD_IN":1x3x640x640 \
     --saveEngine="$OUT/scrfd.$(basename "$SCRFD_MODEL" .onnx).640.fp16.engine" \
     --useCudaGraph
 
@@ -53,12 +76,14 @@ if [[ -f "$SCENE_MODEL" ]]; then
     echo "== TransNetV2 (scene detector) =="
     # Fixed 1x100x27x48x3 window. The raw-TRT scene detector backend loads this
     # engine directly via --scene-detector-engine; the ORT-TRT EP builds its own.
+    SCENE_IN="$(input_name "$SCENE_MODEL")"
+    echo "   (input tensor: $SCENE_IN)"
     run trtexec \
         --onnx="$SCENE_MODEL" \
         --fp16 \
-        --minShapes=input:1x100x27x48x3 \
-        --optShapes=input:1x100x27x48x3 \
-        --maxShapes=input:1x100x27x48x3 \
+        --minShapes="$SCENE_IN":1x100x27x48x3 \
+        --optShapes="$SCENE_IN":1x100x27x48x3 \
+        --maxShapes="$SCENE_IN":1x100x27x48x3 \
         --saveEngine="$OUT/transnetv2.100x27x48.fp16.engine" \
         --useCudaGraph
 else