diff --git a/official/cv/c3d/README.md b/official/cv/c3d/README.md
index 1b8738b40aea9baeae9827173da5221e6cd19ebb..81159d121fb48de70f963659b3a83eca7f374408 100644
--- a/official/cv/c3d/README.md
+++ b/official/cv/c3d/README.md
@@ -425,11 +425,13 @@ eval result: top_1 79.381%
 ### [Export MindIR](#contents)
 
 ```shell
-python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
+python export.py --ckpt_file [CKPT_PATH] --mindir_file_name [FILE_NAME] --file_format [FILE_FORMAT] --num_classes [NUM_CLASSES] --batch_size [BATCH_SIZE]
 ```
 
-The ckpt_file parameter is required,
-`file_format` should be in ["AIR", "MINDIR"]
+- `ckpt_file` parameter is mandotory.
+- `file_format` should be in ["AIR", "MINDIR"].
+- `NUM_CLASSES` Number of total classes in the dataset, 51 for HMDB51 and 101 for UCF101.
+- `BATCH_SIZE` Since currently mindir does not support dynamic shapes, this network only supports inference with batch_size of 1.
 
 ### Infer on Ascend310
 
diff --git a/official/cv/c3d/default_config.yaml b/official/cv/c3d/default_config.yaml
index d547a2826abf031732e3f9c883c9c50fa77092a8..4690d074605ff122c22ed19dffe2d9f7c05f3dd9 100644
--- a/official/cv/c3d/default_config.yaml
+++ b/official/cv/c3d/default_config.yaml
@@ -58,5 +58,5 @@ mindir_file_name:  'C3D'               # Save file path
 file_format:       'MINDIR'              # Save file format
 
 # 310 infer
-pre_result_path:   './pre_result_path'          # Mindspore ckpt file path
-post_result_path:  './result_Files'               # Save file path
+pre_result_path:   './preprocess_Result'          # save preprocess result file path
+post_result_path:  './result_Files'               # Save postprocess result file path
diff --git a/official/cv/c3d/preprocess.py b/official/cv/c3d/preprocess.py
index ab501930e9abe184854a70e565117a6a19b50db1..8a8de2150c4a8fd42e3f848efb73a2ab25c137f3 100644
--- a/official/cv/c3d/preprocess.py
+++ b/official/cv/c3d/preprocess.py
@@ -31,7 +31,6 @@ def gen_bin(data_dir):
 
     image_path = os.path.join(data_dir, "image")
     label_path = os.path.join(data_dir, "label_bs" + str(config.batch_size) + ".npy")
-    os.makedirs(image_path)
     label_list = []
 
     for index, (input_data, label) in enumerate(dataset):