From 7d2ece020c4ac4ae829d03101d9afeb050dc9851 Mon Sep 17 00:00:00 2001
From: Chi Lo <54722500+chilo-ms@users.noreply.github.com>
Date: Wed, 23 Oct 2024 11:23:23 -0700
Subject: [PATCH] Update README.md

---
 quantization/image_classification/trt/resnet50/README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/quantization/image_classification/trt/resnet50/README.md b/quantization/image_classification/trt/resnet50/README.md
index 5be3b2a75..e116dc227 100644
--- a/quantization/image_classification/trt/resnet50/README.md
+++ b/quantization/image_classification/trt/resnet50/README.md
@@ -1,6 +1,8 @@
 # ONNX PTQ overview
 Following is the end-to-end example using ORT quantization tool to quantize ONNX model, specifially image classification model, and run/evaluate the quantized model with TRT EP.  
 
+## Note
+Implicit quantization is deprecated in TRT 10.1 (using calibration table in TRT EP to set `setDynamicRange`), we suggest to use explicit quantization aka QDQ format.
 ## Environment setup
 ### dataset
 First, prepare the dataset for calibration. TensorRT recommends calibration data size to be at least 500 for CNN and ViT models.