diff --git a/paddle/operators/detection_map_op.cc b/paddle/operators/detection_map_op.cc
new file mode 100644
index 0000000000000000000000000000000000000000..b59d3bfad96c77e48bf443619978573d02c0e8d9
--- /dev/null
+++ b/paddle/operators/detection_map_op.cc
@@ -0,0 +1,77 @@
+/* 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/detection_map_op.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+class DetectionMAPOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    auto map_dim = framework::make_ddim({1});
+    ctx->SetOutputDim("MAP", map_dim);
+  }
+
+ protected:
+  framework::OpKernelType GetKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<framework::Tensor>("Label")->type()),
+        ctx.device_context());
+  }
+};
+
+class DetectionMAPOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  DetectionMAPOpMaker(framework::OpProto* proto,
+                      framework::OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("Detect", "The detection output.");
+    AddInput("Label", "The label data.");
+    AddOutput("MAP", "The MAP evaluate result of the detection.");
+
+    AddAttr<float>("overlap_threshold", "The overlap threshold.")
+        .SetDefault(.3f);
+    AddAttr<bool>("evaluate_difficult",
+                  "Switch to control whether the difficult data is evaluated.")
+        .SetDefault(true);
+    AddAttr<std::string>("ap_type",
+                         "The AP algorithm type, 'Integral' or '11point'.")
+        .SetDefault("Integral");
+
+    AddComment(R"DOC(
+Detection MAP Operator.
+
+Detection MAP evaluator 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(detection_map, ops::DetectionMAPOp,
+                             ops::DetectionMAPOpMaker);
+REGISTER_OP_CPU_KERNEL(
+    detection_map, ops::DetectionMAPOpKernel<paddle::platform::GPUPlace, float>,
+    ops::DetectionMAPOpKernel<paddle::platform::GPUPlace, double>);
diff --git a/paddle/operators/detection_map_op.cu b/paddle/operators/detection_map_op.cu
new file mode 100644
index 0000000000000000000000000000000000000000..ab9a992c363169588d587965e93dfb3d147678d8
--- /dev/null
+++ b/paddle/operators/detection_map_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/detection_map_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(
+    detection_map, ops::DetectionMAPOpKernel<paddle::platform::GPUPlace, float>,
+    ops::DetectionMAPOpKernel<paddle::platform::GPUPlace, double>);
diff --git a/paddle/operators/detection_map_op.h b/paddle/operators/detection_map_op.h
new file mode 100644
index 0000000000000000000000000000000000000000..3e862abda64eb56361e59d33cf129de0bfc6118b
--- /dev/null
+++ b/paddle/operators/detection_map_op.h
@@ -0,0 +1,316 @@
+/* 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/detection_util.h"
+#include "paddle/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+inline void GetAccumulation(std::vector<std::pair<T, int>> in_pairs,
+                            std::vector<int>* accu_vec) {
+  std::stable_sort(in_pairs.begin(), in_pairs.end(),
+                   math::SortScorePairDescend<int>);
+  accu_vec->clear();
+  size_t sum = 0;
+  for (size_t i = 0; i < in_pairs.size(); ++i) {
+    // auto score = in_pairs[i].first;
+    auto count = in_pairs[i].second;
+    sum += count;
+    accu_vec->push_back(sum);
+  }
+}
+
+template <typename Place, typename T>
+class DetectionMAPOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* input_label = ctx.Input<framework::LoDTensor>("Label");
+    auto* input_detect = ctx.Input<framework::Tensor>("Detect");
+    auto* map_out = ctx.Output<framework::Tensor>("MAP");
+
+    float overlap_threshold = ctx.Attr<float>("overlap_threshold");
+    float evaluate_difficult = ctx.Attr<bool>("evaluate_difficult");
+    std::string ap_type = ctx.Attr<std::string>("ap_type");
+
+    auto label_lod = input_label->lod();
+    PADDLE_ENFORCE_EQ(label_lod.size(), 1UL,
+                      "Only support one level sequence now.");
+    auto batch_size = label_lod[0].size() - 1;
+
+    std::vector<std::map<int, std::vector<operators::math::BBox<T>>>> gt_bboxes;
+
+    std::vector<
+        std::map<int, std::vector<std::pair<T, operators::math::BBox<T>>>>>
+        detect_bboxes;
+
+    if (platform::is_gpu_place(ctx.GetPlace())) {
+      framework::LoDTensor input_label_cpu;
+      framework::Tensor input_detect_cpu;
+      input_label_cpu.set_lod(input_label->lod());
+      input_label_cpu.Resize(input_label->dims());
+      input_detect_cpu.Resize(input_detect->dims());
+      input_label_cpu.mutable_data<T>(platform::CPUPlace());
+      input_detect_cpu.mutable_data<T>(platform::CPUPlace());
+      framework::CopyFrom(*input_label, platform::CPUPlace(),
+                          ctx.device_context(), &input_label_cpu);
+      framework::CopyFrom(*input_detect, platform::CPUPlace(),
+                          ctx.device_context(), &input_detect_cpu);
+      GetBBoxes(input_label_cpu, input_detect_cpu, gt_bboxes, detect_bboxes);
+    } else {
+      GetBBoxes(*input_label, *input_detect, gt_bboxes, detect_bboxes);
+    }
+
+    std::map<int, int> label_pos_count;
+    std::map<int, std::vector<std::pair<T, int>>> true_pos;
+    std::map<int, std::vector<std::pair<T, int>>> false_pos;
+
+    CalcTrueAndFalsePositive(batch_size, evaluate_difficult, overlap_threshold,
+                             gt_bboxes, detect_bboxes, label_pos_count,
+                             true_pos, false_pos);
+
+    T map = CalcMAP(ap_type, label_pos_count, true_pos, false_pos);
+
+    T* map_data = nullptr;
+    framework::Tensor map_cpu;
+    map_out->mutable_data<T>(ctx.GetPlace());
+    if (platform::is_gpu_place(ctx.GetPlace())) {
+      map_data = map_cpu.mutable_data<T>(map_out->dims(), platform::CPUPlace());
+      map_data[0] = map;
+      framework::CopyFrom(map_cpu, platform::CPUPlace(), ctx.device_context(),
+                          map_out);
+    } else {
+      map_data = map_out->mutable_data<T>(ctx.GetPlace());
+      map_data[0] = map;
+    }
+  }
+
+ protected:
+  void GetBBoxes(
+      const framework::LoDTensor& input_label,
+      const framework::Tensor& input_detect,
+      std::vector<std::map<int, std::vector<operators::math::BBox<T>>>>&
+          gt_bboxes,
+      std::vector<
+          std::map<int, std::vector<std::pair<T, operators::math::BBox<T>>>>>&
+          detect_bboxes) const {
+    const T* label_data = input_label.data<T>();
+    const T* detect_data = input_detect.data<T>();
+
+    auto label_lod = input_label.lod();
+    auto batch_size = label_lod[0].size() - 1;
+    auto label_index = label_lod[0];
+
+    for (size_t n = 0; n < batch_size; ++n) {
+      std::map<int, std::vector<operators::math::BBox<T>>> bboxes;
+      for (int i = label_index[n]; i < label_index[n + 1]; ++i) {
+        std::vector<operators::math::BBox<T>> bbox;
+        math::GetBBoxFromLabelData<T>(label_data + i * 6, 1, bbox);
+        int label = static_cast<int>(label_data[i * 6]);
+        bboxes[label].push_back(bbox[0]);
+      }
+      gt_bboxes.push_back(bboxes);
+    }
+
+    size_t n = 0;
+    size_t detect_box_count = input_detect.dims()[0];
+    for (size_t img_id = 0; img_id < batch_size; ++img_id) {
+      std::map<int, std::vector<std::pair<T, operators::math::BBox<T>>>> bboxes;
+      size_t cur_img_id = static_cast<size_t>((detect_data + n * 7)[0]);
+      while (cur_img_id == img_id && n < detect_box_count) {
+        std::vector<T> label;
+        std::vector<T> score;
+        std::vector<operators::math::BBox<T>> bbox;
+        math::GetBBoxFromDetectData<T>(detect_data + n * 7, 1, label, score,
+                                       bbox);
+        bboxes[label[0]].push_back(std::make_pair(score[0], bbox[0]));
+        ++n;
+        cur_img_id = static_cast<size_t>((detect_data + n * 7)[0]);
+      }
+      detect_bboxes.push_back(bboxes);
+    }
+  }
+
+  void CalcTrueAndFalsePositive(
+      size_t batch_size, bool evaluate_difficult, float overlap_threshold,
+      const std::vector<std::map<int, std::vector<operators::math::BBox<T>>>>&
+          gt_bboxes,
+      const std::vector<
+          std::map<int, std::vector<std::pair<T, operators::math::BBox<T>>>>>&
+          detect_bboxes,
+      std::map<int, int>& label_pos_count,
+      std::map<int, std::vector<std::pair<T, int>>>& true_pos,
+      std::map<int, std::vector<std::pair<T, int>>>& false_pos) const {
+    for (size_t n = 0; n < batch_size; ++n) {
+      auto image_gt_bboxes = gt_bboxes[n];
+      for (auto it = image_gt_bboxes.begin(); it != image_gt_bboxes.end();
+           ++it) {
+        size_t count = 0;
+        auto labeled_bboxes = it->second;
+        if (evaluate_difficult) {
+          count = labeled_bboxes.size();
+        } else {
+          for (size_t i = 0; i < labeled_bboxes.size(); ++i)
+            if (!(labeled_bboxes[i].is_difficult)) ++count;
+        }
+        if (count == 0) {
+          continue;
+        }
+        int label = it->first;
+        if (label_pos_count.find(label) == label_pos_count.end()) {
+          label_pos_count[label] = count;
+        } else {
+          label_pos_count[label] += count;
+        }
+      }
+    }
+
+    for (size_t n = 0; n < detect_bboxes.size(); ++n) {
+      auto image_gt_bboxes = gt_bboxes[n];
+      auto detections = detect_bboxes[n];
+
+      if (image_gt_bboxes.size() == 0) {
+        for (auto it = detections.begin(); it != detections.end(); ++it) {
+          auto pred_bboxes = it->second;
+          int label = it->first;
+          for (size_t i = 0; i < pred_bboxes.size(); ++i) {
+            auto score = pred_bboxes[i].first;
+            true_pos[label].push_back(std::make_pair(score, 0));
+            false_pos[label].push_back(std::make_pair(score, 1));
+          }
+        }
+        continue;
+      }
+
+      for (auto it = detections.begin(); it != detections.end(); ++it) {
+        int label = it->first;
+        auto pred_bboxes = it->second;
+        if (image_gt_bboxes.find(label) == image_gt_bboxes.end()) {
+          for (size_t i = 0; i < pred_bboxes.size(); ++i) {
+            auto score = pred_bboxes[i].first;
+            true_pos[label].push_back(std::make_pair(score, 0));
+            false_pos[label].push_back(std::make_pair(score, 1));
+          }
+          continue;
+        }
+
+        auto matched_bboxes = image_gt_bboxes.find(label)->second;
+        std::vector<bool> visited(matched_bboxes.size(), false);
+        // Sort detections in descend order based on scores
+        std::sort(pred_bboxes.begin(), pred_bboxes.end(),
+                  math::SortScorePairDescend<operators::math::BBox<T>>);
+        for (size_t i = 0; i < pred_bboxes.size(); ++i) {
+          float max_overlap = -1.0;
+          size_t max_idx = 0;
+          auto score = pred_bboxes[i].first;
+          for (size_t j = 0; j < matched_bboxes.size(); ++j) {
+            float overlap =
+                JaccardOverlap(pred_bboxes[i].second, matched_bboxes[j]);
+            if (overlap > max_overlap) {
+              max_overlap = overlap;
+              max_idx = j;
+            }
+          }
+          if (max_overlap > overlap_threshold) {
+            bool match_evaluate_difficult =
+                evaluate_difficult ||
+                (!evaluate_difficult && !matched_bboxes[max_idx].is_difficult);
+            if (match_evaluate_difficult) {
+              if (!visited[max_idx]) {
+                true_pos[label].push_back(std::make_pair(score, 1));
+                false_pos[label].push_back(std::make_pair(score, 0));
+                visited[max_idx] = true;
+              } else {
+                true_pos[label].push_back(std::make_pair(score, 0));
+                false_pos[label].push_back(std::make_pair(score, 1));
+              }
+            }
+          } else {
+            true_pos[label].push_back(std::make_pair(score, 0));
+            false_pos[label].push_back(std::make_pair(score, 1));
+          }
+        }
+      }
+    }
+  }
+
+  T CalcMAP(
+      std::string ap_type, const std::map<int, int>& label_pos_count,
+      const std::map<int, std::vector<std::pair<T, int>>>& true_pos,
+      const std::map<int, std::vector<std::pair<T, int>>>& false_pos) const {
+    T mAP = 0.0;
+    int count = 0;
+    for (auto it = label_pos_count.begin(); it != label_pos_count.end(); ++it) {
+      int label = it->first;
+      int label_num_pos = it->second;
+      if (label_num_pos == 0 || true_pos.find(label) == true_pos.end())
+        continue;
+      auto label_true_pos = true_pos.find(label)->second;
+      auto label_false_pos = false_pos.find(label)->second;
+      // Compute average precision.
+      std::vector<int> tp_sum;
+      GetAccumulation<T>(label_true_pos, &tp_sum);
+      std::vector<int> fp_sum;
+      GetAccumulation<T>(label_false_pos, &fp_sum);
+      std::vector<float> precision, recall;
+      size_t num = tp_sum.size();
+      // Compute Precision.
+      for (size_t i = 0; i < num; ++i) {
+        // CHECK_LE(tpCumSum[i], labelNumPos);
+        precision.push_back(static_cast<float>(tp_sum[i]) /
+                            static_cast<float>(tp_sum[i] + fp_sum[i]));
+        recall.push_back(static_cast<float>(tp_sum[i]) / label_num_pos);
+      }
+      // VOC2007 style
+      if (ap_type == "11point") {
+        std::vector<float> max_precisions(11, 0.0);
+        int start_idx = num - 1;
+        for (int j = 10; j >= 0; --j)
+          for (int i = start_idx; i >= 0; --i) {
+            if (recall[i] < j / 10.) {
+              start_idx = i;
+              if (j > 0) max_precisions[j - 1] = max_precisions[j];
+              break;
+            } else {
+              if (max_precisions[j] < precision[i])
+                max_precisions[j] = precision[i];
+            }
+          }
+        for (int j = 10; j >= 0; --j) mAP += max_precisions[j] / 11;
+        ++count;
+      } else if (ap_type == "Integral") {
+        // Nature integral
+        float average_precisions = 0.;
+        float prev_recall = 0.;
+        for (size_t i = 0; i < num; ++i) {
+          if (fabs(recall[i] - prev_recall) > 1e-6)
+            average_precisions += precision[i] * fabs(recall[i] - prev_recall);
+          prev_recall = recall[i];
+        }
+        mAP += average_precisions;
+        ++count;
+      } else {
+        LOG(FATAL) << "Unkown ap version: " << ap_type;
+      }
+    }
+    if (count != 0) mAP /= count;
+    return mAP * 100;
+  }
+};  // namespace operators
+
+}  // namespace operators
+}  // namespace paddle
diff --git a/paddle/operators/math/detection_util.cc b/paddle/operators/math/detection_util.cc
new file mode 100644
index 0000000000000000000000000000000000000000..4131a0cb0ef086301a9938382ae5d837508ea07d
--- /dev/null
+++ b/paddle/operators/math/detection_util.cc
@@ -0,0 +1,22 @@
+/* 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/math/detection_util.h"
+#include "paddle/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+namespace math {}  // namespace math
+}  // namespace operators
+}  // namespace paddle
diff --git a/paddle/operators/math/detection_util.cu b/paddle/operators/math/detection_util.cu
new file mode 100644
index 0000000000000000000000000000000000000000..d2bb992396197cd76c31cee11c804257adc51357
--- /dev/null
+++ b/paddle/operators/math/detection_util.cu
@@ -0,0 +1,23 @@
+/* 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/math/detection_util.h"
+#include "paddle/operators/math/math_function.h"
+#include "paddle/platform/cuda_helper.h"
+
+namespace paddle {
+namespace operators {
+namespace math {}  // namespace math
+}  // namespace operators
+}  // namespace paddle
diff --git a/paddle/operators/math/detection_util.h b/paddle/operators/math/detection_util.h
new file mode 100644
index 0000000000000000000000000000000000000000..2a4dadc545eb2b67e5819e5fe65fc64fcb35e69c
--- /dev/null
+++ b/paddle/operators/math/detection_util.h
@@ -0,0 +1,128 @@
+/* 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/selected_rows.h"
+#include "paddle/platform/device_context.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+template <typename T>
+struct BBox {
+  BBox(T x_min, T y_min, T x_max, T y_max)
+      : x_min(x_min),
+        y_min(y_min),
+        x_max(x_max),
+        y_max(y_max),
+        is_difficult(false) {}
+
+  BBox() {}
+
+  T get_width() const { return x_max - x_min; }
+
+  T get_height() const { return y_max - y_min; }
+
+  T get_center_x() const { return (x_min + x_max) / 2; }
+
+  T get_center_y() const { return (y_min + y_max) / 2; }
+
+  T get_area() const { return get_width() * get_height(); }
+
+  // coordinate of bounding box
+  T x_min;
+  T y_min;
+  T x_max;
+  T y_max;
+  // whether difficult object (e.g. object with heavy occlusion is difficult)
+  bool is_difficult;
+};
+
+template <typename T>
+void GetBBoxFromDetectData(const T* detect_data, const size_t num_bboxes,
+                           std::vector<T>& labels, std::vector<T>& scores,
+                           std::vector<BBox<T>>& bboxes) {
+  size_t out_offset = bboxes.size();
+  labels.resize(out_offset + num_bboxes);
+  scores.resize(out_offset + num_bboxes);
+  bboxes.resize(out_offset + num_bboxes);
+  for (size_t i = 0; i < num_bboxes; ++i) {
+    labels[out_offset + i] = *(detect_data + i * 7 + 1);
+    scores[out_offset + i] = *(detect_data + i * 7 + 2);
+    BBox<T> bbox;
+    bbox.x_min = *(detect_data + i * 7 + 3);
+    bbox.y_min = *(detect_data + i * 7 + 4);
+    bbox.x_max = *(detect_data + i * 7 + 5);
+    bbox.y_max = *(detect_data + i * 7 + 6);
+    bboxes[out_offset + i] = bbox;
+  };
+}
+
+template <typename T>
+void GetBBoxFromLabelData(const T* label_data, const size_t num_bboxes,
+                          std::vector<BBox<T>>& bboxes) {
+  size_t out_offset = bboxes.size();
+  bboxes.resize(bboxes.size() + num_bboxes);
+  for (size_t i = 0; i < num_bboxes; ++i) {
+    BBox<T> bbox;
+    bbox.x_min = *(label_data + i * 6 + 1);
+    bbox.y_min = *(label_data + i * 6 + 2);
+    bbox.x_max = *(label_data + i * 6 + 3);
+    bbox.y_max = *(label_data + i * 6 + 4);
+    T is_difficult = *(label_data + i * 6 + 5);
+    if (std::abs(is_difficult - 0.0) < 1e-6)
+      bbox.is_difficult = false;
+    else
+      bbox.is_difficult = true;
+    bboxes[out_offset + i] = bbox;
+  }
+}
+
+template <typename T>
+inline float JaccardOverlap(const BBox<T>& bbox1, const BBox<T>& bbox2) {
+  if (bbox2.x_min > bbox1.x_max || bbox2.x_max < bbox1.x_min ||
+      bbox2.y_min > bbox1.y_max || bbox2.y_max < bbox1.y_min) {
+    return 0.0;
+  } else {
+    float inter_x_min = std::max(bbox1.x_min, bbox2.x_min);
+    float inter_y_min = std::max(bbox1.y_min, bbox2.y_min);
+    float inter_x_max = std::min(bbox1.x_max, bbox2.x_max);
+    float inter_y_max = std::min(bbox1.y_max, bbox2.y_max);
+
+    float inter_width = inter_x_max - inter_x_min;
+    float inter_height = inter_y_max - inter_y_min;
+    float inter_area = inter_width * inter_height;
+
+    float bbox_area1 = bbox1.get_area();
+    float bbox_area2 = bbox2.get_area();
+
+    return inter_area / (bbox_area1 + bbox_area2 - inter_area);
+  }
+}
+
+template <typename T>
+bool SortScorePairDescend(const std::pair<float, T>& pair1,
+                          const std::pair<float, T>& pair2) {
+  return pair1.first > pair2.first;
+}
+
+// template <>
+// bool SortScorePairDescend(const std::pair<float, NormalizedBBox>& pair1,
+//                           const std::pair<float, NormalizedBBox>& pair2) {
+//   return pair1.first > pair2.first;
+// }
+
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle
diff --git a/python/paddle/v2/fluid/tests/test_detection_map_op.py b/python/paddle/v2/fluid/tests/test_detection_map_op.py
new file mode 100644
index 0000000000000000000000000000000000000000..50ce3afbb95064a59b63d9f1606e36d3001a7e08
--- /dev/null
+++ b/python/paddle/v2/fluid/tests/test_detection_map_op.py
@@ -0,0 +1,155 @@
+import unittest
+import numpy as np
+import sys
+import collections
+import math
+from op_test import OpTest
+
+
+class TestDetectionMAPOp(OpTest):
+    def set_data(self):
+        self.init_test_case()
+
+        self.mAP = [self.calc_map(self.tf_pos)]
+        self.label = np.array(self.label).astype('float32')
+        self.detect = np.array(self.detect).astype('float32')
+        self.mAP = np.array(self.mAP).astype('float32')
+
+        self.inputs = {
+            'Label': (self.label, self.label_lod),
+            'Detect': self.detect
+        }
+
+        self.attrs = {
+            'overlap_threshold': self.overlap_threshold,
+            'evaluate_difficult': self.evaluate_difficult,
+            'ap_type': self.ap_type
+        }
+
+        self.outputs = {'MAP': self.mAP}
+
+    def init_test_case(self):
+        self.overlap_threshold = 0.3
+        self.evaluate_difficult = True
+        self.ap_type = "Integral"
+
+        self.label_lod = [[0, 2, 4]]
+        # label xmin ymin xmax ymax difficult
+        self.label = [[1, 0.1, 0.1, 0.3, 0.3, 0], [1, 0.6, 0.6, 0.8, 0.8, 1],
+                      [2, 0.3, 0.3, 0.6, 0.5, 0], [1, 0.7, 0.1, 0.9, 0.3, 0]]
+
+        # image_id label score xmin ymin xmax ymax difficult
+        self.detect = [
+            [0, 1, 0.3, 0.1, 0.0, 0.4, 0.3], [0, 1, 0.7, 0.0, 0.1, 0.2, 0.3],
+            [0, 1, 0.9, 0.7, 0.6, 0.8, 0.8], [1, 2, 0.8, 0.2, 0.1, 0.4, 0.4],
+            [1, 2, 0.1, 0.4, 0.3, 0.7, 0.5], [1, 1, 0.2, 0.8, 0.1, 1.0, 0.3],
+            [1, 3, 0.2, 0.8, 0.1, 1.0, 0.3]
+        ]
+
+        # image_id label score false_pos false_pos
+        # [-1, 1, 3, -1, -1],
+        # [-1, 2, 1, -1, -1]
+        self.tf_pos = [[0, 1, 0.9, 1, 0], [0, 1, 0.7, 1, 0], [0, 1, 0.3, 0, 1],
+                       [1, 1, 0.2, 1, 0], [1, 2, 0.8, 0, 1], [1, 2, 0.1, 1, 0],
+                       [1, 3, 0.2, 0, 1]]
+
+    def calc_map(self, tf_pos):
+        mAP = 0.0
+        count = 0
+
+        class_pos_count = {}
+        true_pos = {}
+        false_pos = {}
+
+        def get_accumulation(pos_list):
+            sorted_list = sorted(pos_list, key=lambda pos: pos[0], reverse=True)
+            sum = 0
+            accu_list = []
+            for (score, count) in sorted_list:
+                sum += count
+                accu_list.append(sum)
+            return accu_list
+
+        label_count = collections.Counter()
+        for (label, xmin, ymin, xmax, ymax, difficult) in self.label:
+            if self.evaluate_difficult:
+                label_count[label] += 1
+            elif not difficult:
+                label_count[label] += 1
+
+        true_pos = collections.defaultdict(list)
+        false_pos = collections.defaultdict(list)
+        for (image_id, label, score, tp, fp) in tf_pos:
+            true_pos[label].append([score, tp])
+            false_pos[label].append([score, fp])
+
+        for (label, label_pos_num) in label_count.items():
+            if label_pos_num == 0 or label not in true_pos:
+                continue
+
+            label_true_pos = true_pos[label]
+            label_false_pos = false_pos[label]
+
+            accu_tp_sum = get_accumulation(label_true_pos)
+            accu_fp_sum = get_accumulation(label_false_pos)
+
+            precision = []
+            recall = []
+
+            for i in range(len(accu_tp_sum)):
+                precision.append(
+                    float(accu_tp_sum[i]) /
+                    float(accu_tp_sum[i] + accu_fp_sum[i]))
+                recall.append(float(accu_tp_sum[i]) / label_pos_num)
+
+            if self.ap_type == "11point":
+                max_precisions = [11.0, 0.0]
+                start_idx = len(accu_tp_sum) - 1
+                for j in range(10, 0, -1):
+                    for i in range(start_idx, 0, -1):
+                        if recall[i] < j / 10.0:
+                            start_idx = i
+                            if j > 0:
+                                max_precisions[j - 1] = max_precisions[j]
+                                break
+                            else:
+                                if max_precisions[j] < accu_precision[i]:
+                                    max_precisions[j] = accu_precision[i]
+                for j in range(10, 0, -1):
+                    mAP += max_precisions[j] / 11
+                count += 1
+            elif self.ap_type == "Integral":
+                average_precisions = 0.0
+                prev_recall = 0.0
+                for i in range(len(accu_tp_sum)):
+                    if math.fabs(recall[i] - prev_recall) > 1e-6:
+                        average_precisions += precision[i] * \
+                            math.fabs(recall[i] - prev_recall)
+                        prev_recall = recall[i]
+
+                mAP += average_precisions
+                count += 1
+
+        if count != 0: mAP /= count
+        return mAP * 100.0
+
+    def setUp(self):
+        self.op_type = "detection_map"
+        self.set_data()
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestDetectionMAPOpSkipDiff(TestDetectionMAPOp):
+    def init_test_case(self):
+        super(TestDetectionMAPOpSkipDiff, self).init_test_case()
+
+        self.evaluate_difficult = False
+
+        self.tf_pos = [[0, 1, 0.7, 1, 0], [0, 1, 0.3, 0, 1], [1, 1, 0.2, 1, 0],
+                       [1, 2, 0.8, 0, 1], [1, 2, 0.1, 1, 0], [1, 3, 0.2, 0, 1]]
+
+
+if __name__ == '__main__':
+    unittest.main()