diff --git a/deploy/slim/quantization/export_model.py b/deploy/slim/quantization/export_model.py
index d0d08b300066044d3088f669045e0536006c3140..5f4d91a8eea2d4f63334be571620aa0e1c00fabd 100644
--- a/deploy/slim/quantization/export_model.py
+++ b/deploy/slim/quantization/export_model.py
@@ -51,6 +51,7 @@ from paddleslim.quant import quant_aware, convert
 from paddle.fluid.layer_helper import LayerHelper
 from eval_utils.eval_det_utils import eval_det_run
 from eval_utils.eval_rec_utils import eval_rec_run
+from eval_utils.eval_cls_utils import eval_cls_run
 
 
 def main():
@@ -105,6 +106,8 @@ def main():
 
     if alg_type == 'det':
         final_metrics = eval_det_run(exe, config, quant_info_dict, "eval")
+    elif alg_type == 'cls':
+        final_metrics = eval_cls_run(exe, quant_info_dict)
     else:
         final_metrics = eval_rec_run(exe, config, quant_info_dict, "eval")
     print(final_metrics)
diff --git a/deploy/slim/quantization/quant.py b/deploy/slim/quantization/quant.py
index b1003ca9c564d01af8eb547e31b366f5833c2a07..e75e84be86867274368b6a0427b3d02d2524e020 100755
--- a/deploy/slim/quantization/quant.py
+++ b/deploy/slim/quantization/quant.py
@@ -178,9 +178,12 @@ def main():
     if train_alg_type == 'det':
         program.train_eval_det_run(
             config, exe, train_info_dict, eval_info_dict, is_slim="quant")
-    else:
+    elif train_alg_type == 'rec':
         program.train_eval_rec_run(
             config, exe, train_info_dict, eval_info_dict, is_slim="quant")
+    else:
+        program.train_eval_cls_run(
+            config, exe, train_info_dict, eval_info_dict, is_slim="quant")
 
 
 if __name__ == '__main__':
diff --git a/ppocr/modeling/architectures/cls_model.py b/ppocr/modeling/architectures/cls_model.py
index ad3ad0e7cf4010a14c70a700ed02d02ee1f1323b..30f3661b4c5ef4370b542f46dfaee2fdc1fc53e5 100755
--- a/ppocr/modeling/architectures/cls_model.py
+++ b/ppocr/modeling/architectures/cls_model.py
@@ -65,6 +65,7 @@ class ClsModel(object):
             labels = None
             loader = None
             image = fluid.data(name='image', shape=image_shape, dtype='float32')
+        image.stop_gradient = False
         return image, labels, loader
 
     def __call__(self, mode):