diff --git a/docs/en/tutorials/how_to_convert_model.md b/docs/en/tutorials/how_to_convert_model.md
index b20257cfc9..c84d24dd2d 100644
--- a/docs/en/tutorials/how_to_convert_model.md
+++ b/docs/en/tutorials/how_to_convert_model.md
@@ -54,7 +54,7 @@ python ./tools/deploy.py \
 - `img` : The path of image file that used to convert model.
 - `--test-img` : The path of image file that used to test model. If not specified, it will be set to `None`.
 - `--work-dir` : The path of work directory that used to save logs and models.
-- `--calib-dataset-cfg` : Only valid in int8 mode. Config used for calibration. If not specified, it will be set to `None` and  use "val" dataset in model config for calibration.
+- `--calib-dataset-cfg` : Config used for calibration. If not specified, it will be set to `None`.
 - `--device` : The device used for conversion. If not specified, it will be set to `cpu`.
 - `--log-level` : To set log level which in `'CRITICAL', 'FATAL', 'ERROR', 'WARN', 'WARNING', 'INFO', 'DEBUG', 'NOTSET'`. If not specified, it will be set to `INFO`.
 - `--show` : Whether to show detection outputs.
diff --git a/docs/zh_cn/tutorials/how_to_convert_model.md b/docs/zh_cn/tutorials/how_to_convert_model.md
index 0a1b1a9f52..65925c22d5 100644
--- a/docs/zh_cn/tutorials/how_to_convert_model.md
+++ b/docs/zh_cn/tutorials/how_to_convert_model.md
@@ -55,7 +55,7 @@ python ./tools/deploy.py \
 - `img` : 用于模型转换时使用的图像文件路径。
 - `--test-img` : 用于测试模型的图像文件路径。默认设置成`None`。
 - `--work-dir` : 工作目录，用来保存日志和模型文件。
-- `--calib-dataset-cfg` : 此参数只有int8模式下生效，用于校准数据集配置文件。若在int8模式下未传入参数，则会自动使用模型配置文件中的'val'数据集进行校准。
+- `--calib-dataset-cfg` : 用于校准的配置文件。默认是`None`。
 - `--device` : 用于模型转换的设备。 默认是`cpu`。
 - `--log-level` : 设置日记的等级，选项包括`'CRITICAL'， 'FATAL'， 'ERROR'， 'WARN'， 'WARNING'， 'INFO'， 'DEBUG'， 'NOTSET'`。 默认是`INFO`。
 - `--show` : 是否显示检测的结果。
diff --git a/mmdeploy/codebase/mmcls/deploy/classification_model.py b/mmdeploy/codebase/mmcls/deploy/classification_model.py
index bf6dcbca2a..263d097ecd 100644
--- a/mmdeploy/codebase/mmcls/deploy/classification_model.py
+++ b/mmdeploy/codebase/mmcls/deploy/classification_model.py
@@ -4,12 +4,13 @@
 import mmcv
 import numpy as np
 import torch
+from mmcls.datasets import DATASETS
 from mmcls.models.classifiers.base import BaseClassifier
 from mmcv.utils import Registry
 
 from mmdeploy.codebase.base import BaseBackendModel
 from mmdeploy.utils import (Backend, get_backend, get_codebase_config,
-                            get_root_logger, load_config)
+                            load_config)
 
 
 def __build_backend_model(cls_name: str, registry: Registry, *args, **kwargs):
@@ -150,35 +151,20 @@ def get_classes_from_config(model_cfg: Union[str, mmcv.Config]):
     Returns:
         list[str]: A list of string specifying names of different class.
     """
-    from mmcls.datasets import DATASETS
-
-    module_dict = DATASETS.module_dict
     model_cfg = load_config(model_cfg)[0]
+    module_dict = DATASETS.module_dict
     data_cfg = model_cfg.data
 
-    def _get_class_names(dataset_type: str):
-        dataset = data_cfg.get(dataset_type, None)
-        if (not dataset) or (dataset.type not in module_dict):
-            return None
-
-        module = module_dict[dataset.type]
-        if module.CLASSES is not None:
-            return module.CLASSES
-        return module.get_classes(dataset.get('classes', None))
-
-    class_names = None
-    for dataset_type in ['val', 'test', 'train']:
-        class_names = _get_class_names(dataset_type)
-        if class_names is not None:
-            break
-
-    if class_names is None:
-        logger = get_root_logger()
-        logger.warning(f'Use generated class names, because \
-            it failed to parse CLASSES from config: {data_cfg}')
-        num_classes = model_cfg.model.head.num_classes
-        class_names = [str(i) for i in range(num_classes)]
-    return class_names
+    if 'train' in data_cfg:
+        module = module_dict[data_cfg.train.type]
+    elif 'val' in data_cfg:
+        module = module_dict[data_cfg.val.type]
+    elif 'test' in data_cfg:
+        module = module_dict[data_cfg.test.type]
+    else:
+        raise RuntimeError(f'No dataset config found in: {model_cfg}')
+
+    return module.CLASSES
 
 
 def build_classification_model(model_files: Sequence[str],
diff --git a/mmdeploy/codebase/mmseg/deploy/segmentation.py b/mmdeploy/codebase/mmseg/deploy/segmentation.py
index a3f1728ae2..209723cf6f 100644
--- a/mmdeploy/codebase/mmseg/deploy/segmentation.py
+++ b/mmdeploy/codebase/mmseg/deploy/segmentation.py
@@ -36,7 +36,7 @@ def process_model_config(model_cfg: mmcv.Config,
     # for static exporting
     if input_shape is not None:
         cfg.data.test.pipeline[1]['img_scale'] = tuple(input_shape)
-        cfg.data.test.pipeline[1]['transforms'][0]['keep_ratio'] = False
+    cfg.data.test.pipeline[1]['transforms'][0]['keep_ratio'] = False
     cfg.data.test.pipeline = [LoadImage()] + cfg.data.test.pipeline[1:]
 
     return cfg
diff --git a/tools/deploy.py b/tools/deploy.py
index f18c1d7484..63100c757e 100644
--- a/tools/deploy.py
+++ b/tools/deploy.py
@@ -29,8 +29,7 @@ def parse_args():
     parser.add_argument('--work-dir', help='the dir to save logs and models')
     parser.add_argument(
         '--calib-dataset-cfg',
-        help='dataset config path used to calibrate in int8 mode. If not \
-            specified,it will use "val" dataset in model config instead.',
+        help='dataset config path used to calibrate.',
         default=None)
     parser.add_argument(
         '--device', help='device used for conversion', default='cpu')