diff --git a/PaddleCV/PaddleDetection/slim/prune/README.md b/PaddleCV/PaddleDetection/slim/prune/README.md
index b883cd09ed974b29b659db31198ddf8775d7ae29..22ebbd625c63094089c7bcbc483aa3c006dbe724 100644
--- a/PaddleCV/PaddleDetection/slim/prune/README.md
+++ b/PaddleCV/PaddleDetection/slim/prune/README.md
@@ -7,7 +7,8 @@
 该示例使用PaddleSlim提供的[卷积通道剪裁压缩策略](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/tutorial.md#2-%E5%8D%B7%E7%A7%AF%E6%A0%B8%E5%89%AA%E8%A3%81%E5%8E%9F%E7%90%86)对检测库中的模型进行压缩。
 在阅读该示例前，建议您先了解以下内容：
 
-- <a href="../..README_cn.md">检测库的常规训练方法</a>
+- <a href="../../README_cn.md">检测库的常规训练方法</a>
+- [检测模型数据准备](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleDetection/docs/INSTALL_cn.md#%E6%95%B0%E6%8D%AE%E9%9B%86)
 - [PaddleSlim使用文档](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md)
 
 
@@ -109,6 +110,7 @@ python compress.py \
     -s yolov3_mobilenet_v1_slim.yaml \
     -c ../../configs/yolov3_mobilenet_v1_voc.yml \
     -o max_iters=258 \
+    YoloTrainFeed.batch_size=64 \
     -d "../../dataset/voc"
 ```
 
@@ -117,7 +119,7 @@ python compress.py \
 如果要调整训练卡数，需要调整配置文件`yolov3_mobilenet_v1_voc.yml`中的以下参数：
 
 - **max_iters:** 一个`epoch`中batch的数量，需要设置为`total_num / batch_size`, 其中`total_num`为训练样本总数量，`batch_size`为多卡上总的batch size.
-- **YoloTrainFeed.batch_size:** 单张卡上的batch size, 受限于显存大小。
+- **YoloTrainFeed.batch_size:** 当使用DataLoader时，表示单张卡上的batch size; 当使用普通reader时，则表示多卡上的总的`batch_size`。`batch_size`受限于显存大小。
 - **LeaningRate.base_lr:** 根据多卡的总`batch_size`调整`base_lr`，两者大小正相关，可以简单的按比例进行调整。
 - **LearningRate.schedulers.PiecewiseDecay.milestones：**请根据batch size的变化对其调整。
 - **LearningRate.schedulers.PiecewiseDecay.LinearWarmup.steps：** 请根据batch size的变化对其进行调整。
@@ -130,7 +132,7 @@ python compress.py \
     -s yolov3_mobilenet_v1_slim.yaml \
     -c ../../configs/yolov3_mobilenet_v1_voc.yml \
     -o max_iters=258 \
-    -o YoloTrainFeed.batch_size = 16 \
+    YoloTrainFeed.batch_size=64 \
     -d "../../dataset/voc"
 ```
 
@@ -140,9 +142,9 @@ python compress.py \
     -s yolov3_mobilenet_v1_slim.yaml \
     -c ../../configs/yolov3_mobilenet_v1_voc.yml \
     -o max_iters=516 \
-    -o LeaningRate.base_lr=0.005 \ # 0.001 /2
-    -o YoloTrainFeed.batch_size = 16 \
-    -o LearningRate.schedulers='[!PiecewiseDecay {gamma: 0.1, milestones: [110000, 124000]}, !LinearWarmup {start_factor: 0., steps: 2000}]' \
+    LeaningRate.base_lr=0.005 \
+    YoloTrainFeed.batch_size=32 \
+    LearningRate.schedulers='[!PiecewiseDecay {gamma: 0.1, milestones: [110000, 124000]}, !LinearWarmup {start_factor: 0., steps: 2000}]' \
     -d "../../dataset/voc"
 ```
 
@@ -189,11 +191,9 @@ python compress.py \
 
 ### MobileNetV1-YOLO-V3
 
-| FLOPS |top1_acc/top5_acc| model_size |Paddle Fluid inference time(ms)| Paddle Lite inference time(ms)|
+| FLOPS |Box AP| model_size |Paddle Fluid inference time(ms)| Paddle Lite inference time(ms)|
 |---|---|---|---|---|
-|baseline|- |- |- |-|
-|-10%|- |- |- |-|
-|-30%|- |- |- |-|
-|-50%|- |- |- |-|
+|baseline|76.2 |93M |- |-|
+|-50%|69.48 |51M |- |-|
 
 ## FAQ
diff --git a/PaddleCV/PaddleDetection/slim/prune/compress.py b/PaddleCV/PaddleDetection/slim/prune/compress.py
index 2d012e46f1dc624697006c06d75cceafccd1c3a1..fc8228d58948df964d96597fbf15092dff6ce4a6 100644
--- a/PaddleCV/PaddleDetection/slim/prune/compress.py
+++ b/PaddleCV/PaddleDetection/slim/prune/compress.py
@@ -148,7 +148,7 @@ def main():
             optimizer.minimize(loss)
 
 
-    train_reader = create_reader(train_feed, cfg.max_iters * devices_num,
+    train_reader = create_reader(train_feed, cfg.max_iters,
                                  FLAGS.dataset_dir)
     train_loader.set_sample_list_generator(train_reader, place)
 
@@ -207,7 +207,6 @@ def main():
             best_box_ap_list.append(box_ap_stats[0])
         elif box_ap_stats[0] > best_box_ap_list[0]:
             best_box_ap_list[0] = box_ap_stats[0]
-            checkpoint.save(exe, train_prog, os.path.join(save_dir,"best_model"))
         logger.info("Best test box ap: {}".format(
             best_box_ap_list[0]))
         return best_box_ap_list[0]