diff --git a/example/auto_compression/detection/README.md b/example/auto_compression/detection/README.md
index 1800054c0a0b46baba7a0ad5a9d41e5aa56afa3c..4261fe7076578c82d4c435377e1e44636523ad88 100644
--- a/example/auto_compression/detection/README.md
+++ b/example/auto_compression/detection/README.md
@@ -14,7 +14,6 @@
 ## 1. 简介
 本示例将以目标检测模型PP-YOLOE为例，介绍如何使用PaddleDetection中Inference部署模型进行自动压缩。本示例使用的自动压缩策略为量化蒸馏。
 
-
 ## 2.Benchmark
 
 ### PP-YOLOE
@@ -28,6 +27,18 @@
 - mAP的指标均在COCO val2017数据集中评测得到，IoU=0.5:0.95。
 - PP-YOLOE-l模型在Tesla V100的GPU环境下测试，并且开启TensorRT，batch_size=1，包含NMS，测试脚本是[benchmark demo](https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.4/deploy/python)。
 - PP-YOLOE-s模型在Tesla T4，TensorRT 8.4.1，CUDA 11.2，batch_size=1，不包含NMS，测试脚本是[cpp_infer_ppyoloe](./cpp_infer_ppyoloe)。
+
+### YOLOv8
+
+| 模型  | Base mAP | 离线量化mAP | ACT量化mAP | TRT-FP32 | TRT-FP16 | TRT-INT8 |  配置文件 | 量化模型  |
+| :-------- |:-------- |:--------: | :---------------------: | :----------------: | :----------------: | :---------------: | :----------------------: | :---------------------: |
+| YOLOv8-s | 44.9 |  43.9 | 44.3  |   9.27ms  |   4.65ms   |  **3.78ms**  |  [config](https://github.com/PaddlePaddle/PaddleSlim/blob/develop/example/auto_compression/detection/configs/yolov8_s_qat_dis.yaml) | [Model](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov8_s_500e_coco_trt_nms_quant.tar) |
+
+**注意：**
+- 表格中YOLOv8模型均为带NMS的模型，可直接在TRT中部署，如果需要对齐测试标准，需要测试不带NMS的模型。
+- mAP的指标均在COCO val2017数据集中评测得到，IoU=0.5:0.95。
+- 表格中的性能在Tesla T4的GPU环境下测试，并且开启TensorRT，batch_size=1。
+
 ### SSD on Pascal VOC
 | 模型  | Box AP | ACT量化Box AP | TRT-FP32 | TRT-INT8 |  配置文件 | 量化模型  |
 | :-------- |:-------- | :---------------------: | :----------------: | :---------------: | :----------------------: | :---------------------: |
@@ -99,6 +110,16 @@ python tools/export_model.py \
         trt=True exclude_post_process=True \
 ```
 
+YOLOv8-s模型，包含NMS，具体可参考[YOLOv8模型文档](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8), 然后执行：
+```shell
+python tools/export_model.py \
+        -c configs/yolov8/yolov8_s_500e_coco.yml \
+        -o weights=https://paddledet.bj.bcebos.com/models/yolov8_s_500e_coco.pdparams \
+        trt=True
+```
+
+如快速体验，可直接下载[YOLOv8-s导出模型](https://bj.bcebos.com/v1/paddle-slim-models/act/yolov8_s_500e_coco_trt_nms.tar)
+
 #### 3.4 自动压缩并产出模型
 
 蒸馏量化自动压缩示例通过run.py脚本启动，会使用接口```paddleslim.auto_compression.AutoCompression```对模型进行自动压缩。配置config文件中模型路径、蒸馏、量化、和训练等部分的参数，配置完成后便可对模型进行量化和蒸馏。具体运行命令为：
diff --git a/example/auto_compression/detection/configs/yolov8_reader.yml b/example/auto_compression/detection/configs/yolov8_reader.yml
new file mode 100644
index 0000000000000000000000000000000000000000..54565c78dd12df138a5ea30f72c78ddcef4460cd
--- /dev/null
+++ b/example/auto_compression/detection/configs/yolov8_reader.yml
@@ -0,0 +1,27 @@
+metric: COCO
+num_classes: 80
+
+# Dataset configuration
+TrainDataset:
+  !COCODataSet
+    image_dir: train2017
+    anno_path: annotations/instances_train2017.json
+    dataset_dir: dataset/coco/
+
+EvalDataset:
+  !COCODataSet
+    image_dir: val2017
+    anno_path: annotations/instances_val2017.json
+    dataset_dir: dataset/coco/
+
+worker_num: 0
+
+# preprocess reader in train & test
+EvalReader:
+  sample_transforms:
+    - Decode: {}
+    - Resize: {target_size: [640, 640], keep_ratio: True, interp: 1}
+    - Pad: {size: [640, 640], fill_value: [114., 114., 114.]}
+    - NormalizeImage: {mean: [0., 0., 0.], std: [1., 1., 1.], norm_type: none}
+    - Permute: {}
+  batch_size: 4
diff --git a/example/auto_compression/detection/configs/yolov8_s_qat_dis.yaml b/example/auto_compression/detection/configs/yolov8_s_qat_dis.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..144fba9a1a6f08b0c60b791bcbd5d2e7820ece79
--- /dev/null
+++ b/example/auto_compression/detection/configs/yolov8_s_qat_dis.yaml
@@ -0,0 +1,31 @@
+
+Global:
+  reader_config: configs/yolov8_reader.yml
+  include_nms: True
+  Evaluation: True
+  model_dir: ./yolov8_s_500e_coco_trt_nms/
+  model_filename: model.pdmodel
+  params_filename: model.pdiparams
+
+Distillation:
+  alpha: 1.0
+  loss: soft_label
+
+QuantAware:
+  onnx_format: true
+  activation_quantize_type: 'moving_average_abs_max'
+  quantize_op_types:
+  - conv2d
+  - depthwise_conv2d
+
+TrainConfig:
+  train_iter: 8000
+  eval_iter: 1000
+  learning_rate:  
+    type: CosineAnnealingDecay
+    learning_rate: 0.00003
+    T_max: 10000
+  optimizer_builder:
+    optimizer: 
+      type: SGD
+    weight_decay: 4.0e-05