diff --git a/ppocr/utils/save_load.py b/ppocr/utils/save_load.py
index 3647111fddaa848a75873ab689559c63dd6d4814..5d43d656f2f8aca91aa1adbeac95d26381264531 100644
--- a/ppocr/utils/save_load.py
+++ b/ppocr/utils/save_load.py
@@ -90,12 +90,16 @@ def load_model(config, model, optimizer=None, model_type='det'):
         params = paddle.load(checkpoints + '.pdparams')
         state_dict = model.state_dict()
         new_state_dict = {}
+        is_float16 = False
         for key, value in state_dict.items():
             if key not in params:
                 logger.warning("{} not in loaded params {} !".format(
                     key, params.keys()))
                 continue
             pre_value = params[key]
+            if pre_value.dtype == paddle.float16:
+                pre_value = pre_value.astype(paddle.float32)
+                is_float16 = True
             if list(value.shape) == list(pre_value.shape):
                 new_state_dict[key] = pre_value
             else:
@@ -104,6 +108,10 @@ def load_model(config, model, optimizer=None, model_type='det'):
                     format(key, value.shape, pre_value.shape))
         model.set_state_dict(new_state_dict)
 
+        if is_float16:
+            logger.info(
+                "The parameter type is float16, which is converted to float32 when loading"
+            )
         if optimizer is not None:
             if os.path.exists(checkpoints + '.pdopt'):
                 optim_dict = paddle.load(checkpoints + '.pdopt')
@@ -138,17 +146,26 @@ def load_pretrained_params(model, path):
     params = paddle.load(path + '.pdparams')
     state_dict = model.state_dict()
     new_state_dict = {}
+    is_float16 = False
     for k1 in params.keys():
         if k1 not in state_dict.keys():
             logger.warning("The pretrained params {} not in model".format(k1))
         else:
+            if params[k1].dtype == paddle.float16:
+                params[k1] = params[k1].astype(paddle.float32)
+                is_float16 = True
             if list(state_dict[k1].shape) == list(params[k1].shape):
                 new_state_dict[k1] = params[k1]
             else:
                 logger.warning(
                     "The shape of model params {} {} not matched with loaded params {} {} !".
                     format(k1, state_dict[k1].shape, k1, params[k1].shape))
+
     model.set_state_dict(new_state_dict)
+    if is_float16:
+        logger.info(
+            "The parameter type is float16, which is converted to float32 when loading"
+        )
     logger.info("load pretrain successful from {}".format(path))
     return model