diff --git a/models/cv/classification/efficientnet_b1/ixrt/README.md b/models/cv/classification/efficientnet_b1/ixrt/README.md
index 9d6cbe934c101a963d1170ad0d7c423f246d8022..c943c82133745543c0682e4222d9ab20383d0164 100644
--- a/models/cv/classification/efficientnet_b1/ixrt/README.md
+++ b/models/cv/classification/efficientnet_b1/ixrt/README.md
@@ -15,7 +15,7 @@ EfficientNet B1 is one of the variants in the EfficientNet family of neural netw
 
 ### Prepare Resources
 
-Pretrained model: <https://download.pytorch.org/models/efficientnet_b1-c27df63c.pth>
+Pretrained model: <http://files.deepspark.org.cn:880/deepspark/data/checkpoints/efficientnet_b1.onnx>
 
 Dataset: <https://www.image-net.org/download.php> to download the validation dataset.
 
@@ -34,8 +34,7 @@ pip3 install -r ../../ixrt_common/requirements.txt
 ### Model Conversion
 
 ```bash
-mkdir checkpoints
-python3 ../../ixrt_common/export.py --model-name efficientnet_b1 --weight efficientnet_b1-c27df63c.pth --output checkpoints/efficientnet_b1.onnx
+mkdir -p checkpoints && wget http://files.deepspark.org.cn:880/deepspark/data/checkpoints/efficientnet_b1.onnx checkpoints/efficientnet_b1.onnx
 ```
 
 ## Model Inference
diff --git a/models/cv/classification/ixrt_common/modify_batchsize.py b/models/cv/classification/ixrt_common/modify_batchsize.py
index fbd3be3f213030aaad20ee48a44f206118813c45..c55dd2467c8977ff973f7dca7351cf8a0a02c54e 100644
--- a/models/cv/classification/ixrt_common/modify_batchsize.py
+++ b/models/cv/classification/ixrt_common/modify_batchsize.py
@@ -1,5 +1,6 @@
 import onnx
 import argparse
+import numpy as np
 
 def change_input_dim(model, bsz):
     batch_size = bsz
@@ -23,6 +24,23 @@ def change_input_dim(model, bsz):
             # set batch size of 1
             dim1.dim_value = 1
 
+def change_reshape_batch(model, bsz):
+    batch_size = int(bsz) if not isinstance(bsz, int) else bsz
+    initializer_map = {init.name: init for init in model.graph.initializer}
+    for node in model.graph.node:
+        if node.op_type == 'Reshape' and len(node.input) >= 2:
+            shape_name = node.input[1]
+            if shape_name not in initializer_map:
+                continue
+            init = initializer_map[shape_name]
+            shape_val = np.array(onnx.numpy_helper.to_array(init))
+            if len(shape_val) >= 1 and shape_val[0] > 0 and shape_val[0] != batch_size:
+                old_val = shape_val[0]
+                shape_val[0] = batch_size
+                new_init = onnx.numpy_helper.from_array(shape_val, name=shape_name)
+                init.CopyFrom(new_init)
+                print(f"  Reshape {node.name}: shape[0] {old_val} -> {batch_size}")
+
 def infer_node_shape(model):
     # remove old shape of the node
     for value_info in model.graph.value_info:
@@ -54,8 +72,9 @@ if __name__ == "__main__":
     args = parse_args()
     model = onnx.load(args.origin_model)
     change_input_dim(model, args.batch_size)
+    change_reshape_batch(model, args.batch_size)
 
     if args.strict_mode:
         model = infer_node_shape(model)
 
-    onnx.save(model, args.output_model)
+    onnx.save(model, args.output_model)
\ No newline at end of file