diff --git a/ACL_TensorFlow/contrib/cv/Rebar_ID2016_for_ACL/LICENSE b/ACL_TensorFlow/contrib/cv/Rebar_ID2016_for_ACL/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..6a40d2cafc5d0f94c3a3e41707922f1d92cb890b
--- /dev/null
+++ b/ACL_TensorFlow/contrib/cv/Rebar_ID2016_for_ACL/LICENSE
@@ -0,0 +1,201 @@
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+   Copyright 2016 Guillaume Chevalier and Yu Zhao
+
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+
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diff --git a/ACL_TensorFlow/contrib/cv/Rebar_ID2016_for_ACL/README.md b/ACL_TensorFlow/contrib/cv/Rebar_ID2016_for_ACL/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..a62a8f8c5e2d9d603244aeddfdceccd3cf8a9d4a
--- /dev/null
+++ b/ACL_TensorFlow/contrib/cv/Rebar_ID2016_for_ACL/README.md
@@ -0,0 +1,52 @@
+## 模型功能
+
+ 对Rebar的tensorflow模型的输出进行解析, 使用固化后outom1021.om模型进行推理。
+
+## 原始模型
+
+参考实现 ：
+```
+https://gitee.com/ascend/ModelZoo-TensorFlow/tree/master/TensorFlow/contrib/cv/Rebar_ID2016_for_TensorFlow
+```
+
+
+原始ckpt模型网络下载地址 :
+```
+链接：https://pan.baidu.com/s/1sWukTQUzzdbqWbjqwkjhHg 
+提取码：1111
+
+```
+原始pb模型网络下载地址 :
+```
+链接：https://pan.baidu.com/s/1cK8-e22Oi4zIl7Sc_U1qmg 
+提取码：1111
+```
+
+
+## om模型
+
+om模型网络下载地址 :
+```
+链接：https://pan.baidu.com/s/14wHs1QrGirvdmoICWnlniQ 
+提取码：1111
+```
+
+使用ATC模型转换工具进行模型转换时可以参考如下指令:
+
+```
+atc --model=./frozen_model1025.pb  --framework=3  --input_shape="Placeholder_1:12,784" --output=./outom1021  --soc_version=Ascend910 
+```
+
+## 使用msame工具推理
+
+参考 https://gitee.com/ascend/tools/tree/master/msame, 获取msame推理工具及使用方法。
+
+## 性能测试
+
+使用msame推理工具，参考如下命令，发起推理性能测试： 
+
+```
+./msame --model "./outom1021.om" --input  "/home/TestUser01/Pycode/rebar_npu/img2bin/bin" --output "./output"  --outfmt TXT --loop 5
+```
+
+
diff --git a/ACL_TensorFlow/contrib/cv/Rebar_ID2016_for_ACL/ckpt2pb.py b/ACL_TensorFlow/contrib/cv/Rebar_ID2016_for_ACL/ckpt2pb.py
new file mode 100644
index 0000000000000000000000000000000000000000..f165fd162fb44076dff89f76c91b6f6501e5e208
--- /dev/null
+++ b/ACL_TensorFlow/contrib/cv/Rebar_ID2016_for_ACL/ckpt2pb.py
@@ -0,0 +1,126 @@
+##  Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from multiprocessing.heap import rebuild_arena
+import tensorflow as tf
+from tensorflow.python.tools import freeze_graph
+from npu_bridge.estimator import npu_ops
+from tensorflow.python.framework import graph_util
+from ops import *
+from data import *
+from utils import *
+from datasets import *
+import rebar 
+import rebar_train
+import numpy as np
+import datasets 
+#添加导入NPU库的头文件
+import npu_bridge 
+from npu_bridge.npu_init import *
+from npu_bridge.estimator.npu import util
+from tensorflow.core.protobuf.rewriter_config_pb2 import RewriterConfig
+from rebar_train import eval
+
+
+config = tf.ConfigProto()
+# keras迁移添加内容
+custom_op = config.graph_options.rewrite_options.custom_optimizers.add()
+custom_op.name = "NpuOptimizer"
+custom_op.parameter_map["use_off_line"].b = True
+config.graph_options.rewrite_options.memory_optimization = RewriterConfig.OFF
+config.graph_options.rewrite_options.remapping = RewriterConfig.OFF  # 必须显示关闭remap
+
+batch_size = 12
+
+#dataset = DataSet("./train.txt", 30, batch_size)
+
+ckpt_path = "./root/rebar/data/output.trial_1/model.ckpt-2000847"
+
+def main():
+    tf.reset_default_graph()
+    inputs1 = tf.placeholder(tf.float32, [None,1,784 ], name="input1")
+    FLAGS = tf.flags.FLAGS
+    hparams = rebar.default_hparams
+    hparams.parse(FLAGS.hparams)
+    print(hparams.values())
+    train_xs, valid_xs, test_xs = datasets.load_data(hparams)
+    print(train_xs.shape)
+    mean_xs = np.mean(train_xs, axis=0)  # Compute mean centering on training
+
+    #training_steps = 2000000
+    model = getattr(rebar, hparams.model)
+    sbn = model(hparams, mean_xs)
+    n_samples = 100
+    
+
+    #c_logits = net.conditioning_logits
+    #p_logits = net.prior_logits
+    #c_logits1, c_logits2 = tf.identity(sbn._create_train_op, name='c_logits1, c_logits2')
+    with tf.Session(config=config) as sess:
+        sbn.initialize(sess)
+        batch_xs = valid_xs[0:24]
+        #c_logits = sbn.partial_eval(batch_xs, n_samples)
+        graph_def = tf.get_default_graph().as_graph_def(add_shapes=True)
+        node_list = [n.name for n in graph_def.node]
+        for node in node_list:
+            print("node_name", node)
+        tf.train.write_graph(sess.graph_def, './pb_model1021', 'model.pb')
+        freeze_graph.freeze_graph(
+            input_graph='./pb_model/model.pb',
+            input_saver='',
+            input_binary=False,
+            input_checkpoint=ckpt_path,
+            output_node_names='Mean_11',
+            restore_op_name='save/restore_all',
+            filename_tensor_name='save/Const:0',
+            output_graph='./pb_model1021/rebar1021.pb',
+            clear_devices=False,
+            initializer_nodes='')
+    print("done")
+
+if __name__ == '__main__':
+    main()
+
+'''
+
+
+def freeze_graph(input_checkpoint,output_graph):
+
+    # checkpoint = tf.train.get_checkpoint_state(model_folder) #检查目录下ckpt文件状态是否可用
+    # input_checkpoint = checkpoint.model_checkpoint_path #得ckpt文件路径
+ 
+    # 指定输出的节点名称,该节点名称必须是原模型中存在的节点
+    # 直接用最后输出的节点，可以在tensorboard中查找到，tensorboard只能在linux中使用
+    output_node_names = "cond_1/Merge"
+    saver = tf.train.import_meta_graph(input_checkpoint + '.meta', clear_devices=True)
+    graph = tf.get_default_graph() # 获得默认的图
+    input_graph_def = graph.as_graph_def()  # 返回一个序列化的图代表当前的图
+ 
+    with tf.Session() as sess:
+        saver.restore(sess, input_checkpoint) #恢复图并得到数据
+        output_graph_def = graph_util.convert_variables_to_constants(  # 模型持久化，将变量值固定
+            sess=sess,
+            input_graph_def=input_graph_def,# 等于:sess.graph_def
+            output_node_names=output_node_names.split(","))# 如果有多个输出节点，以逗号隔开
+ 
+        with tf.gfile.GFile(output_graph, "wb") as f: #保存模型
+            f.write(output_graph_def.SerializeToString()) #序列化输出
+        print("%d ops in the final graph." % len(output_graph_def.node)) #得到当前图有几个操作节点
+
+# input_checkpoint='inceptionv1/model.ckpt-0'
+# out_pb_path='inceptionv1/frozen_model.pb'
+
+input_checkpoint='./output/model.ckpt-280000'
+out_pb_path='frozen_model.pb'
+freeze_graph(input_checkpoint, out_pb_path)
+'''
\ No newline at end of file
diff --git a/ACL_TensorFlow/contrib/cv/Rebar_ID2016_for_ACL/img2bin.py b/ACL_TensorFlow/contrib/cv/Rebar_ID2016_for_ACL/img2bin.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8551eb3954a5a150df7c288aee5ffdbfacc922f
--- /dev/null
+++ b/ACL_TensorFlow/contrib/cv/Rebar_ID2016_for_ACL/img2bin.py
@@ -0,0 +1,316 @@
+# -*- coding: UTF-8 -*-
+#
+#   =======================================================================
+#
+# Copyright (C) 2018, Hisilicon Technologies Co., Ltd. All Rights Reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+#   1 Redistributions of source code must retain the above copyright notice,
+#     this list of conditions and the following disclaimer.
+#
+#   2 Redistributions in binary form must reproduce the above copyright notice,
+#     this list of conditions and the following disclaimer in the documentation
+#     and/or other materials provided with the distribution.
+#
+#   3 Neither the names of the copyright holders nor the names of the
+#   contributors may be used to endorse or promote products derived from this
+#   software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+# POSSIBILITY OF SUCH DAMAGE.
+#   =======================================================================
+#Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import print_function
+import argparse
+import os
+import sys
+import json
+# import getpass
+
+IMG_EXT = ['.jpg', '.JPG', '.png', '.PNG', '.bmp', '.BMP', '.jpeg', '.JPEG']
+
+try:
+    import cv2 as cv 
+except ImportError:
+    if sys.version_info.major == 2:
+        confirm = raw_input("[info] Begin to install opencv-python, input[Y/N]:")
+    else:
+        confirm = input("[info] Begin to install opencv-python, input[Y/N]:")
+    
+    if confirm == 'Y' or confirm == 'y':
+        print('[info] Starting to install opencv-python...') 
+        if sys.version_info.major == 2:
+            import commands
+            retu, output = commands.getstatusoutput("sudo yum install -y opencv-python")
+            if retu != 0:
+                retu, output = commands.getstatusoutput("sudo apt-get install -y python-opencv")
+                if retu != 0:
+                    print('[ERROR] install opencv-python failed,please check env.')
+                    exit(0)
+        else:
+            retu = os.system('sudo python3 -m pip install opencv-python')
+            if retu != 0:
+                print('[ERROR] install opencv-python failed,please check env.')
+                exit(0)
+        
+        import cv2 as cv
+    else:
+        print("[info] The installation has been cancled.")
+        exit(0)
+    
+try:
+    import numpy as np  
+except ImportError:
+    if sys.version_info.major == 2:
+        os.system('pip2 install numpy')
+    else:
+        os.system('python3 -m pip install numpy')
+    import numpy as np 
+
+def get_args():
+    parser = argparse.ArgumentParser(
+        conflict_handler='resolve',
+        description='''eg1: python2 img2bin.py
+         -i ./images -w 416 -h 416 -f BGR -a NHWC -t uint8 -m [104,117,123] -c [1,1,1] -o ./out 
+         eg2: python2 img2bin.py -i ./test.txt -t uint8 -o ./out''')
+    parser.add_argument('-i', '--input', required=True, type=str, \
+        help='folder of input image or file of other input.')
+    parser.add_argument('-w', '--width', required=True, type=int, \
+        help='resized image width before inference.')
+    parser.add_argument('-h', '--height', required=True, type=int, \
+        help='resized image height before inference.')
+    parser.add_argument('-f', '--output_image_format', default='BGR', type=str, \
+        help='output image format in (BGR/RGB/YUV/GRAY).')
+    parser.add_argument('-a', '--output_format', default='NCHW', type=str, \
+        help='output format in (NCHW/NHWC).')
+    parser.add_argument('-t', '--output_type', required=True, type=str, \
+        help='output type in (float32/uint8/int32/uint32).')
+    parser.add_argument('-m', '--mean', default='[0, 0, 0]', \
+        help='reduce mean for each channel.')   
+    parser.add_argument('-c', '--coefficient', default='[1, 1, 1]', \
+        help='multiplying coefficients for each channel.')
+    parser.add_argument('-o', '--output', default='./', \
+        help='output path.')
+    return parser.parse_args()
+
+
+def eprint(*args, **kwargs):
+    """print error message to stderr
+    """
+    print(*args, file=sys.stderr, **kwargs)
+
+
+def check_args(args):
+    """
+    check console parameters according to restrictions.
+    return: True or False
+    """
+    check_flag = True
+    is_dir = True  
+    if os.path.isdir(args.input):
+        if not os.listdir(args.input):
+            eprint('[ERROR] input image path=%r is empty.' % args.input)
+            check_flag = False
+    elif os.path.isfile(args.input):
+        is_dir = False
+    else:
+        eprint('[ERROR] input path=%r does not exist.' % args.input)
+        check_flag = False
+    if args.output_image_format not in ('BGR','RGB', 'YUV', 'GRAY'):
+        eprint("[ERROR] Convert to %d is not support." % (args.output_image_format))
+        check_flag = False
+    if args.height <= 0 or args.width <= 0:
+        eprint("[ERROR] image height or image width must be greater than 0.")
+        check_flag = False
+    elif args.output_image_format == 'YUV':
+        if args.width % 2 == 1 or args.height % 2 == 1:
+            eprint("[ERROR] when the output color format is YUV, the width and height of the picture must be even.")
+            check_flag = False     
+    return check_flag, is_dir
+   
+
+def convert_img_2_yuv(input_img):
+    input_img_height = input_img.shape[0]
+    input_img_width = input_img.shape[1]
+    bgr2y_list = np.array([29, 150, 77])
+    bgr2y_data = input_img.reshape(input_img_height * input_img_width, 3)
+    y_data = np.dot(bgr2y_data, bgr2y_list) >> 8
+    bgr2u_list = np.array([131, -87, -44])
+    bgr2v_list = np.array([-21, -110, 131])
+    bgr2uv_matrix = np.transpose(np.append(bgr2u_list, bgr2v_list).reshape((2, 3)))
+    bgr2uv_data = input_img[0::2, 0::2, :].reshape((input_img_height // 2 * input_img_width // 2, 3))
+    yuv_base_data = np.dot(bgr2uv_data, bgr2uv_matrix) >> 8
+    u_data = yuv_base_data[:,0] + 128
+    v_data = yuv_base_data[:,1] + 128
+    u_v_data = np.transpose(np.append(u_data.flatten(), v_data.flatten()).reshape((2, input_img_height //2 * input_img_width // 2)))
+    nv12_data = np.append(y_data.flatten(), u_v_data.flatten()).reshape((input_img_height // 2 * 3, input_img_width)).astype(np.uint8)
+    return nv12_data
+
+
+def convert_img(args, input_img):
+    if args.output_image_format == 'BGR':
+        converted_input_img = input_img
+    elif args.output_image_format == 'RGB':
+        converted_input_img = cv.cvtColor(input_img, cv.COLOR_BGR2RGB)
+    elif args.output_image_format == 'YUV':
+        converted_input_img = convert_img_2_yuv(input_img)
+    elif args.output_image_format == 'GRAY':
+        converted_input_img = cv.cvtColor(input_img, cv.COLOR_BGR2GRAY)
+    return converted_input_img
+        
+
+def resize_img(args, input_img):
+    old_size = input_img.shape[0:2]
+    target_size = [args.height, args.width]
+    ratio = min(float(target_size[i]) / (old_size[i]) for i in range(len(old_size)))
+    new_size = tuple([int(i*ratio) for i in old_size])
+    img_new = cv.resize(input_img,(new_size[1], new_size[0]))
+    pad_w = target_size[1] - new_size[1]
+    pad_h = target_size[0] - new_size[0]
+    top, bottom = pad_h // 2, pad_h - (pad_h // 2)
+    left, right = pad_w // 2, pad_w - (pad_w // 2)
+    resized_img = cv.copyMakeBorder(img_new, top, bottom, left, right, cv.BORDER_CONSTANT, None,(0, 0, 0))
+    return resized_img
+
+
+def change_type(args, input_img):
+    if args.output_type == 'float32':
+        change_type_img = input_img.astype(np.float32)
+    elif args.output_type == 'int32':
+        change_type_img = input_img.astype(np.int32)
+    elif args.output_type == 'uint32':
+        change_type_img = input_img.astype(np.uint32)
+    else:
+        change_type_img = input_img.astype(np.uint8)
+    return change_type_img
+
+
+def mean(args, input_img):
+    if isinstance (args.mean, str):
+        args.mean = json.loads(args.mean)
+    input_img = input_img.astype(np.float32)
+    if args.output_image_format == 'GRAY':
+        input_img[:, :] -= args.mean[0]
+    elif args.output_image_format in ('BGR', 'RGB'):
+        input_img[:, :, 0] -= args.mean[0]
+        input_img[:, :, 1] -= args.mean[1]
+        input_img[:, :, 2] -= args.mean[2]
+    return input_img
+
+
+def coefficient(args, input_img):
+    """
+    Normalize the input image 
+    """
+    if isinstance(args.coefficient, str):
+        args.coefficient = json.loads(args.coefficient)
+    input_img = input_img.astype(np.float32)
+    if args.output_image_format == 'GRAY':
+        input_img[:, :] *= args.coefficient[0]
+    elif args.output_image_format in ('BGR', 'RGB'):
+        input_img[:, :, 0] *= args.coefficient[0]
+        input_img[:, :, 1] *= args.coefficient[1]
+        input_img[:, :, 2] *= args.coefficient[2]
+    return input_img
+
+
+def change_format(args, input_img):
+    if args.output_format == 'NCHW':
+        if args.output_image_format in ('RGB', 'BGR'):
+            change_format_img = input_img.transpose(2,0,1).copy()        
+            return change_format_img
+    return input_img
+
+
+def mkdir_output(args):
+    if not os.path.exists(args.output):
+        os.makedirs(args.output)
+    return
+
+
+def process(args, file_path):
+    if file_path.endswith(".txt"):
+        if sys.version_info.major == 2:
+            import ConfigParser as configparser
+        else:
+            import configparser
+
+        config = configparser.ConfigParser()
+        config.read(file_path)
+        if sys.version_info.major == 2:
+            input_node = json.loads(config.get('baseconf', 'input_node'))
+            shape = json.loads(config.get('baseconf', 'shape'))
+        else:
+            input_node = json.loads(config['baseconf']['input_node'])
+            shape = json.loads(config['baseconf']['shape'])
+        input_node_np = np.array(input_node)
+        change_type_img_info = change_type(args, input_node_np)
+        img_info = np.reshape(change_type_img_info, shape)
+        out_path = os.path.join(args.output, os.path.splitext(os.path.split(file_path)[1])[0] + ".bin")
+        mkdir_output(args)
+        img_info.tofile(out_path) 
+    else:
+        input_img = cv.imread(file_path) 
+        resized_img1 = resize_img(args, input_img) 
+        converted_img = convert_img(args, resized_img1) 
+        if args.output_image_format == "YUV":
+            change_format_img = converted_img
+        else:
+            mean_img = mean(args, converted_img)
+            coefficient_img = coefficient(args, mean_img)
+            change_type_img = change_type(args, coefficient_img)
+            change_format_img = change_format(args, change_type_img)
+        out_path = os.path.join(args.output, os.path.splitext(os.path.split(file_path)[1])[0] + ".bin")
+        mkdir_output(args)
+        change_format_img.tofile(out_path)
+        
+
+def main():
+    """main function to receive params them change data to bin.
+    """
+    args = get_args()
+    ret, is_dir = check_args(args)
+    if ret:
+        if is_dir:
+            files_name = os.listdir(args.input)
+            for file_name in files_name:
+                if os.path.splitext(file_name)[1] in IMG_EXT:
+                    file_path = os.path.join(args.input, file_name)
+                    process(args, file_path)  
+            print("[info] bin file generated successfully.")
+        else:
+            if os.path.splitext(args.input)[1] in IMG_EXT or os.path.splitext(args.input)[1] == "txt":
+                process(args, args.input)    
+                print("[info] bin file generated successfully.")
+            else:
+                eprint("[ERROR] input file must be image or end with '.txt'.")
+
+if __name__ == '__main__':
+    main()
+
+
+
+
diff --git a/ACL_TensorFlow/contrib/cv/Rebar_ID2016_for_ACL/modelzoo_level.txt b/ACL_TensorFlow/contrib/cv/Rebar_ID2016_for_ACL/modelzoo_level.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4f4eeb699cb9bfd53ef6361efe5807c90ff799c8
--- /dev/null
+++ b/ACL_TensorFlow/contrib/cv/Rebar_ID2016_for_ACL/modelzoo_level.txt
@@ -0,0 +1,6 @@
+ModelConvert:OK 
+QuantStatus:NOK 
+FuncStatus:OK
+PrecisionStatus:OK
+AutoTune:NOK
+PerfStatus:NOK
diff --git a/ACL_TensorFlow/contrib/cv/Rebar_ID2016_for_ACL/msame b/ACL_TensorFlow/contrib/cv/Rebar_ID2016_for_ACL/msame
new file mode 100644
index 0000000000000000000000000000000000000000..5647e05a02c561a05fa71c512cf58872ffc6e366
Binary files /dev/null and b/ACL_TensorFlow/contrib/cv/Rebar_ID2016_for_ACL/msame differ
diff --git a/ACL_TensorFlow/contrib/cv/Rebar_ID2016_for_ACL/pb2om.sh b/ACL_TensorFlow/contrib/cv/Rebar_ID2016_for_ACL/pb2om.sh
new file mode 100644
index 0000000000000000000000000000000000000000..5f89fee3539db3d013a6e559872a05d73951d961
--- /dev/null
+++ b/ACL_TensorFlow/contrib/cv/Rebar_ID2016_for_ACL/pb2om.sh
@@ -0,0 +1 @@
+atc --model=./frozen_model1025.pb  --framework=3  --input_shape="Placeholder_1:12,784" --output=./outom1021  --soc_version=Ascend910 
\ No newline at end of file