diff --git a/tutorials/train/detection/test.py b/tutorials/train/detection/test.py
deleted file mode 100644
index cf6d4a9294a70f120f5febb41b2950d2fdf8449b..0000000000000000000000000000000000000000
--- a/tutorials/train/detection/test.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import os
-os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-from paddlex.det import transforms
-import paddlex as pdx
-
-# 定义训练和验证时的transforms
-train_transforms = transforms.ComposedRCNNTransforms(
-    mode='train', min_max_size=[600, 1000])
-eval_transforms = transforms.ComposedRCNNTransforms(
-    mode='eval', min_max_size=[600, 1000])
-
-# 定义训练所用的数据集
-train_dataset = pdx.datasets.CocoDetection(
-    data_dir='jinnan2_round1_train_20190305/restricted/',
-    ann_file='jinnan2_round1_train_20190305/train.json',
-    transforms=train_transforms,
-    shuffle=True,
-    num_workers=2)
-# 训练集中加入无目标背景图片
-train_dataset.add_negative_samples(
-    'jinnan2_round1_train_20190305/normal_train_back/')
-
-# 定义验证所用的数据集
-eval_dataset = pdx.datasets.CocoDetection(
-    data_dir='jinnan2_round1_train_20190305/restricted/',
-    ann_file='jinnan2_round1_train_20190305/val.json',
-    transforms=eval_transforms,
-    num_workers=2)
-
-# 初始化模型，并进行训练
-model = pdx.det.FasterRCNN(num_classes=len(train_dataset.labels) + 1)
-model.train(
-    num_epochs=17,
-    train_dataset=train_dataset,
-    eval_dataset=eval_dataset,
-    train_batch_size=8,
-    learning_rate=0.01,
-    lr_decay_epochs=[13, 16],
-    save_dir='./output')