diff --git a/paddle/operators/prior_box_op.cc b/paddle/operators/prior_box_op.cc
new file mode 100644
index 0000000000000000000000000000000000000000..fe1ccceb0665eb6407ac0203ebf69f7c7a776702
--- /dev/null
+++ b/paddle/operators/prior_box_op.cc
@@ -0,0 +1,167 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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. */
+
+#include "paddle/operators/prior_box_op.h"
+
+namespace paddle {
+namespace operators {
+
+class PriorBoxOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Input"),
+                   "Input(X) of SequenceSliceOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Image"),
+                   "Input(Offset) of SequenceSliceOp should not be null.");
+
+    auto image_dims = ctx->GetInputDim("Image");
+    auto input_dims = ctx->GetInputDim("Input");
+    PADDLE_ENFORCE(image_dims.size() == 4,
+                   "The format of input tensor is NCHW.");
+
+    auto min_sizes = ctx->Attrs().Get<std::vector<int>>("min_sizes");
+    auto max_sizes = ctx->Attrs().Get<std::vector<int>>("max_sizes");
+    auto variances = ctx->Attrs().Get<std::vector<float>>("variances");
+    auto input_aspect_ratio =
+        ctx->Attrs().Get<std::vector<float>>("aspect_ratios");
+    bool flip = ctx->Attrs().Get<bool>("flip");
+
+    PADDLE_ENFORCE_GT(min_sizes.size(), 0, "must provide min_size.");
+    for (size_t i = 0; i < min_sizes.size(); ++i) {
+      PADDLE_ENFORCE_GT(min_sizes[i], 0, "min_sizes[%d] must be positive.", i);
+    }
+
+    std::vector<float> aspect_ratios;
+    expand_aspect_ratios(input_aspect_ratio, flip, aspect_ratios);
+
+    int num_priors = aspect_ratios.size() * min_sizes.size();
+    if (max_sizes.size() > 0) {
+      PADDLE_ENFORCE_EQ(max_sizes.size(), min_sizes.size(),
+                        "The length of min_size and max_size must be equal.");
+      for (size_t i = 0; i < min_sizes.size(); ++i) {
+        PADDLE_ENFORCE_GT(max_sizes[i], min_sizes[i],
+                          "max_size[%d] must be greater than min_size[%d].", i,
+                          i);
+        num_priors += 1;
+      }
+    }
+
+    if (variances.size() > 1) {
+      PADDLE_ENFORCE_EQ(variances.size(), 4,
+                        "Must and only provide 4 variance.");
+      for (size_t i = 0; i < variances.size(); ++i) {
+        PADDLE_ENFORCE_GT(variances[i], 0.0,
+                          "variance[%d] must be greater than 0.", i);
+      }
+    } else if (variances.size() == 1) {
+      PADDLE_ENFORCE_GT(variances[0], 0.0,
+                        "variance[0] must be greater than 0.");
+    }
+
+    const int img_h = ctx->Attrs().Get<int>("img_h");
+    PADDLE_ENFORCE_GT(img_h, 0, "img_h should be larger than 0.");
+    const int img_w = ctx->Attrs().Get<int>("img_w");
+    PADDLE_ENFORCE_GT(img_w, 0, "img_w should be larger than 0.");
+
+    const float step_h = ctx->Attrs().Get<float>("step_h");
+    PADDLE_ENFORCE_GT(step_h, 0.0, "step_h should be larger than 0.");
+    const float step_w = ctx->Attrs().Get<float>("step_w");
+    PADDLE_ENFORCE_GT(step_w, 0.0, "step_w should be larger than 0.");
+
+    const int layer_height = input_dims[3];
+    const int layer_width = input_dims[2];
+
+    std::vector<int64_t> dim_vec(3);
+    // Since all images in a batch has same height and width, we only need to
+    // generate one set of priors which can be shared across all images.
+    dim_vec[0] = 1;
+    // 2 channels. First channel stores the mean of each prior coordinate.
+    // Second channel stores the variance of each prior coordinate.
+    dim_vec[1] = 2;
+    dim_vec[2] = layer_width * layer_height * num_priors * 4;
+    PADDLE_ENFORCE_GT(dim_vec[2], 0,
+                      "output_dim[2] must larger than 0."
+                      "check your data dims");
+    auto output_dim = framework::make_ddim(dim_vec);
+    ctx->SetOutputDim("Out", output_dim);
+  }
+
+ protected:
+  framework::OpKernelType GetKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<framework::LoDTensor>("Image")->type()),
+        ctx.device_context());
+  }
+};
+
+class PriorBoxOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  PriorBoxOpMaker(framework::OpProto* proto,
+                  framework::OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("Input",
+             "(Tensor), "
+             "the input feature data of PriorBoxOp.");
+    AddInput("Image",
+             "(Tensor), "
+             "the input image data of PriorBoxOp.");
+    AddOutput("Out", "(Tensor), the output prior boxes of PriorBoxOp.");
+    AddAttr<std::vector<int>>("min_sizes", "(vector<int>) ",
+                              "List of min sizes of generated prior boxes.");
+    AddAttr<std::vector<int>>("max_sizes", "(vector<int>) ",
+                              "List of max sizes of generated prior boxes.");
+    AddAttr<std::vector<float>>(
+        "aspect_ratios", "(vector<float>) ",
+        "List of aspect ratios of generated prior boxes.")
+        .SetDefault({});
+    AddAttr<std::vector<float>>(
+        "variances", "(vector<float>) ",
+        "List of variances to be encoded in prior boxes.")
+        .SetDefault({0.1});
+    AddAttr<bool>("flip", "(bool) ", "Whether to flip aspect ratios.")
+        .SetDefault(true);
+    AddAttr<bool>("clip", "(bool) ", "Whether to clip out-of-boundary boxes.")
+        .SetDefault(true);
+    AddAttr<int>("img_w", "").SetDefault(0);
+    AddAttr<int>("img_h", "").SetDefault(0);
+    AddAttr<float>("step_w",
+                   "Prior boxes step across width, 0 for auto calculation.")
+        .SetDefault(0.0);
+    AddAttr<float>("step_h",
+                   "Prior boxes step across height, 0 for auto calculation.")
+        .SetDefault(0.0);
+    AddAttr<float>("offset",
+                   "(float) "
+                   "Prior boxes center offset.")
+        .SetDefault(0.5);
+    AddComment(R"DOC(
+Prior box operator
+Generate prior boxes for SSD(Single Shot MultiBox Detector) algorithm.
+Please get more information from the following papers:
+https://arxiv.org/abs/1512.02325.
+)DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_WITHOUT_GRADIENT(prior_box, ops::PriorBoxOp, ops::PriorBoxOpMaker);
+REGISTER_OP_CPU_KERNEL(
+    prior_box, ops::PriorBoxOpKernel<paddle::platform::CPUPlace, float>,
+    ops::PriorBoxOpKernel<paddle::platform::CPUPlace, double>);
diff --git a/paddle/operators/prior_box_op.cu b/paddle/operators/prior_box_op.cu
new file mode 100755
index 0000000000000000000000000000000000000000..d1928462a2d75baa98a85833647fc695cea2a019
--- /dev/null
+++ b/paddle/operators/prior_box_op.cu
@@ -0,0 +1,20 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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. */
+
+#include "paddle/operators/prior_box_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(
+    prior_box, ops::PriorBoxOpKernel<paddle::platform::GPUPlace, float>,
+    ops::PriorBoxOpKernel<paddle::platform::GPUPlace, double>);
diff --git a/paddle/operators/prior_box_op.h b/paddle/operators/prior_box_op.h
new file mode 100644
index 0000000000000000000000000000000000000000..6dabba526504a2680d93da4d70035372b6667f68
--- /dev/null
+++ b/paddle/operators/prior_box_op.h
@@ -0,0 +1,199 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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. */
+
+#pragma once
+#include "paddle/framework/op_registry.h"
+#include "paddle/operators/math/math_function.h"
+// #include "paddle/operators/strided_memcpy.h"
+
+namespace paddle {
+namespace operators {
+
+inline void expand_aspect_ratios(const std::vector<float> input_aspect_ratior,
+                                 bool flip,
+                                 std::vector<float>& output_aspect_ratior) {
+  constexpr float eps = 1e-6;
+  output_aspect_ratior.clear();
+  output_aspect_ratior.push_back(1.);
+  for (size_t i = 0; i < input_aspect_ratior.size(); ++i) {
+    float ar = input_aspect_ratior[i];
+    bool already_exist = false;
+    for (size_t j = 0; j < output_aspect_ratior.size(); ++j) {
+      if (fabs(ar - output_aspect_ratior[j]) < eps) {
+        already_exist = true;
+        break;
+      }
+    }
+    if (!already_exist) {
+      output_aspect_ratior.push_back(ar);
+      if (flip) {
+        output_aspect_ratior.push_back(1. / ar);
+      }
+    }
+  }
+}
+
+template <typename Place, typename T>
+class PriorBoxOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* input = ctx.Input<paddle::framework::Tensor>("Input");
+    auto* image = ctx.Input<paddle::framework::Tensor>("Image");
+    auto* out = ctx.Output<paddle::framework::Tensor>("Out");
+
+    auto min_sizes = ctx.Attr<std::vector<int>>("min_sizes");
+    auto max_sizes = ctx.Attr<std::vector<int>>("max_sizes");
+    auto input_aspect_ratio = ctx.Attr<std::vector<float>>("aspect_ratios");
+    auto variances = ctx.Attr<std::vector<float>>("variances");
+    auto flip = ctx.Attr<bool>("flip");
+    auto clip = ctx.Attr<bool>("clip");
+
+    std::vector<float> aspect_ratios;
+    expand_aspect_ratios(input_aspect_ratio, flip, aspect_ratios);
+
+    auto img_w = ctx.Attr<int>("img_w");
+    auto img_h = ctx.Attr<int>("img_h");
+    auto step_w = ctx.Attr<float>("step_w");
+    auto step_h = ctx.Attr<float>("step_h");
+    auto offset = ctx.Attr<float>("offset");
+
+    int img_width, img_height;
+    if (img_h == 0 || img_w == 0) {
+      img_width = image->dims()[2];
+      img_height = image->dims()[3];
+    } else {
+      img_width = img_w;
+      img_height = img_h;
+    }
+
+    const int layer_width = input->dims()[2];
+    const int layer_height = input->dims()[3];
+
+    float step_width, step_height;
+    if (step_w == 0 || step_h == 0) {
+      step_width = static_cast<float>(img_width) / layer_width;
+      step_height = static_cast<float>(img_height) / layer_height;
+    } else {
+      step_width = step_w;
+      step_height = step_h;
+    }
+
+    int num_priors = aspect_ratios.size() * min_sizes.size();
+    if (max_sizes.size() > 0) {
+      num_priors += max_sizes.size();
+    }
+
+    int dim = layer_height * layer_width * num_priors * 4;
+
+    T* output_data = nullptr;
+    framework::Tensor output_cpu;
+    out->mutable_data<T>(ctx.GetPlace());
+    if (platform::is_gpu_place(ctx.GetPlace())) {
+      output_data =
+          output_cpu.mutable_data<T>(out->dims(), platform::CPUPlace());
+    } else {
+      output_data = out->mutable_data<T>(ctx.GetPlace());
+    }
+
+    int idx = 0;
+    for (int h = 0; h < layer_height; ++h) {
+      for (int w = 0; w < layer_width; ++w) {
+        float center_x = (w + offset) * step_width;
+        float center_y = (h + offset) * step_height;
+        float box_width, box_height;
+        for (size_t s = 0; s < min_sizes.size(); ++s) {
+          int min_size = min_sizes[s];
+          // first prior: aspect_ratio = 1, size = min_size
+          box_width = box_height = min_size;
+          // xmin
+          output_data[idx++] = (center_x - box_width / 2.) / img_width;
+          // ymin
+          output_data[idx++] = (center_y - box_height / 2.) / img_height;
+          // xmax
+          output_data[idx++] = (center_x + box_width / 2.) / img_width;
+          // ymax
+          output_data[idx++] = (center_y + box_height / 2.) / img_height;
+
+          if (max_sizes.size() > 0) {
+            int max_size = max_sizes[s];
+            // second prior: aspect_ratio = 1,
+            // size = sqrt(min_size * max_size)
+            box_width = box_height = sqrt(min_size * max_size);
+            // xmin
+            output_data[idx++] = (center_x - box_width / 2.) / img_width;
+            // ymin
+            output_data[idx++] = (center_y - box_height / 2.) / img_height;
+            // xmax
+            output_data[idx++] = (center_x + box_width / 2.) / img_width;
+            // ymax
+            output_data[idx++] = (center_y + box_height / 2.) / img_height;
+          }
+
+          // rest of priors
+          for (size_t r = 0; r < aspect_ratios.size(); ++r) {
+            float ar = aspect_ratios[r];
+            if (fabs(ar - 1.) < 1e-6) {
+              continue;
+            }
+            box_width = min_size * sqrt(ar);
+            box_height = min_size / sqrt(ar);
+            // xmin
+            output_data[idx++] = (center_x - box_width / 2.) / img_width;
+            // ymin
+            output_data[idx++] = (center_y - box_height / 2.) / img_height;
+            // xmax
+            output_data[idx++] = (center_x + box_width / 2.) / img_width;
+            // ymax
+            output_data[idx++] = (center_y + box_height / 2.) / img_height;
+          }
+        }
+      }
+    }
+
+    // clip the prior's coordidate such that it is within [0, 1]
+    if (clip) {
+      for (int d = 0; d < dim; ++d) {
+        output_data[d] = std::min<T>(std::max<T>(output_data[d], 0.), 1.);
+      }
+    }
+
+    // set the variance.
+    auto output_stride = framework::stride(out->dims());
+    output_data += output_stride[1];
+    if (variances.size() == 1) {
+      for (int i = 0; i < dim; ++i) {
+        output_data[i] = variances[0];
+      }
+    } else {
+      int count = 0;
+      for (int h = 0; h < layer_height; ++h) {
+        for (int w = 0; w < layer_width; ++w) {
+          for (int i = 0; i < num_priors; ++i) {
+            for (int j = 0; j < 4; ++j) {
+              output_data[count] = variances[j];
+              ++count;
+            }
+          }
+        }
+      }
+    }
+    if (platform::is_gpu_place(ctx.GetPlace())) {
+      framework::CopyFrom(output_cpu, platform::CPUPlace(),
+                          ctx.device_context(), out);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
diff --git a/python/paddle/v2/fluid/tests/test_prior_box_op.py b/python/paddle/v2/fluid/tests/test_prior_box_op.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f8218895299e2a0dceeb7cb2ad72a65d6629680
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/test_prior_box_op.py
@@ -0,0 +1,179 @@
+import unittest
+import numpy as np
+import sys
+import math
+from op_test import OpTest
+
+
+class TestPriorBoxOp(OpTest):
+    def set_data(self):
+        self.init_test_params()
+        self.init_test_input()
+        self.init_test_output()
+        self.inputs = {'Input': self.input, 'Image': self.image}
+
+        self.attrs = {
+            'min_sizes': self.min_sizes,
+            'max_sizes': self.max_sizes,
+            'aspect_ratios': self.aspect_ratios,
+            'variances': self.variances,
+            'flip': self.flip,
+            'clip': self.clip,
+            'step_w': self.step_w,
+            'step_h': self.step_h,
+            'img_w': self.image_w,
+            'img_h': self.image_h,
+            'offset': self.offset
+        }
+
+        self.outputs = {'Out': self.output}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        return
+
+    def setUp(self):
+        self.op_type = "prior_box"
+        self.set_data()
+
+    def init_test_params(self):
+        self.layer_w = 4
+        self.layer_h = 4
+
+        self.image_w = 20
+        self.image_h = 20
+
+        self.step_w = float(self.image_w) / float(self.layer_w)
+        self.step_h = float(self.image_h) / float(self.layer_h)
+
+        self.input_channels = 2
+        self.image_channels = 3
+        self.batch_size = 10
+
+        self.min_sizes = [2, 4]
+        self.min_sizes = np.array(self.min_sizes).astype('int64')
+        self.max_sizes = [5, 10]
+        self.max_sizes = np.array(self.max_sizes).astype('int64')
+        self.aspect_ratios = [2.0, 3.0]
+        self.flip = True
+        self.real_aspect_ratios = [1, 2.0, 1.0 / 2.0, 3.0, 1.0 / 3.0]
+        self.aspect_ratios = np.array(
+            self.aspect_ratios, dtype=np.float).flatten()
+        self.variances = [0.1, 0.1, 0.2, 0.2]
+        self.variances = np.array(self.variances, dtype=np.float).flatten()
+
+        self.clip = True
+
+        self.num_priors = len(self.real_aspect_ratios) * len(self.min_sizes)
+        if len(self.max_sizes) > 1:
+            self.num_priors += len(self.max_sizes)
+        self.offset = 0.5
+
+    def init_test_input(self):
+        self.image = np.random.random(
+            (self.batch_size, self.image_channels, self.image_w,
+             self.image_h)).astype('float32')
+
+        self.input = np.random.random(
+            (self.batch_size, self.input_channels, self.layer_w,
+             self.layer_h)).astype('float32')
+
+    def init_test_output(self):
+        dim = self.layer_w * self.layer_h * self.num_priors * 4
+        out_dim = (1, 2, dim)
+        output = np.zeros(out_dim).astype('float32')
+
+        idx = 0
+        for h in range(self.layer_h):
+            for w in range(self.layer_w):
+                center_x = (w + self.offset) * self.step_w
+                center_y = (h + self.offset) * self.step_h
+                for s in range(len(self.min_sizes)):
+                    min_size = self.min_sizes[s]
+                    # first prior: aspect_ratio = 1, size = min_size
+                    box_width = box_height = min_size
+                    # xmin
+                    output[0, 0, idx] = (
+                        center_x - box_width / 2.) / self.image_w
+                    idx += 1
+                    # ymin
+                    output[0, 0, idx] = (
+                        center_y - box_height / 2.) / self.image_h
+                    idx += 1
+                    # xmax
+                    output[0, 0, idx] = (
+                        center_x + box_width / 2.) / self.image_w
+                    idx += 1
+                    # ymax
+                    output[0, 0, idx] = (
+                        center_y + box_height / 2.) / self.image_h
+                    idx += 1
+
+                    if len(self.max_sizes) > 0:
+                        max_size = self.max_sizes[s]
+                        # second prior: aspect_ratio = 1,
+                        # size = sqrt(min_size * max_size)
+                        box_width = box_height = math.sqrt(min_size * max_size)
+                        # xmin
+                        output[0, 0, idx] = (
+                            center_x - box_width / 2.) / self.image_w
+                        idx += 1
+                        # ymin
+                        output[0, 0, idx] = (
+                            center_y - box_height / 2.) / self.image_h
+                        idx += 1
+                        # xmax
+                        output[0, 0, idx] = (
+                            center_x + box_width / 2.) / self.image_w
+                        idx += 1
+                        # ymax
+                        output[0, 0, idx] = (
+                            center_y + box_height / 2.) / self.image_h
+                        idx += 1
+
+                    # rest of priors
+                    for r in range(len(self.real_aspect_ratios)):
+                        ar = self.real_aspect_ratios[r]
+                        if math.fabs(ar - 1.) < 1e-6:
+                            continue
+                        box_width = min_size * math.sqrt(ar)
+                        box_height = min_size / math.sqrt(ar)
+                        # xmin
+                        output[0, 0, idx] = (
+                            center_x - box_width / 2.) / self.image_w
+                        idx += 1
+                        # ymin
+                        output[0, 0, idx] = (
+                            center_y - box_height / 2.) / self.image_h
+                        idx += 1
+                        # xmax
+                        output[0, 0, idx] = (
+                            center_x + box_width / 2.) / self.image_w
+                        idx += 1
+                        # ymax
+                        output[0, 0, idx] = (
+                            center_y + box_height / 2.) / self.image_h
+                        idx += 1
+        # clip the prior's coordidate such that it is within[0, 1]
+        if self.clip:
+            for d in range(dim):
+                output[0, 0, d] = min(max(output[0, 0, d], 0), 1)
+        # set the variance.
+        if len(self.variances) == 1:
+            for i in range(dim):
+                output[0, 1, i] = self.variances[0]
+        else:
+            count = 0
+            for h in range(self.layer_h):
+                for w in range(self.layer_w):
+                    for i in range(self.num_priors):
+                        for j in range(4):
+                            output[0, 1, count] = self.variances[j]
+                            count += 1
+        self.output = output.astype('float32')
+
+
+if __name__ == '__main__':
+    unittest.main()