diff --git a/paddle/fluid/operators/detection/yolo_box_op.cu b/paddle/fluid/operators/detection/yolo_box_op.cu
index bc563107f8824b834216923f422f903f726be78f..fbe934c7eac8961e04f92cff89254446ff9d45b2 100644
--- a/paddle/fluid/operators/detection/yolo_box_op.cu
+++ b/paddle/fluid/operators/detection/yolo_box_op.cu
@@ -36,7 +36,7 @@ __global__ void KeYoloBoxFw(const T* input, const int* imgsize, T* boxes,
     int k = (tid % grid_num) / w;
     int l = tid % w;
 
-    int an_stride = an_num * grid_num;
+    int an_stride = (5 + class_num) * grid_num;
     int img_height = imgsize[2 * i];
     int img_width = imgsize[2 * i + 1];
 
@@ -56,7 +56,7 @@ __global__ void KeYoloBoxFw(const T* input, const int* imgsize, T* boxes,
 
     int label_idx =
         GetEntryIndex(i, j, k * w + l, an_num, an_stride, grid_num, 5);
-    int score_idx = (i * box_num + j * stride + k * w + l) * class_num;
+    int score_idx = (i * box_num + j * grid_num + k * w + l) * class_num;
     CalcLabelScore<T>(scores, input, label_idx, score_idx, class_num, conf,
                       grid_num);
   }
@@ -99,12 +99,12 @@ class YoloBoxOpCUDAKernel : public framework::OpKernel<T> {
     set_zero(dev_ctx, boxes, static_cast<T>(0));
     set_zero(dev_ctx, scores, static_cast<T>(0));
 
-    int grid_dim = (n * box_num + 512 - 1) / 512;
-    grid_dim = grid_dim > 8 ? 8 : grid_dim;
+    int grid_dim = (n * box_num + 4 - 1) / 4;
+    grid_dim = grid_dim > 2 ? 2 : grid_dim;
 
-    KeYoloBoxFw<T><<<grid_dim, 512, 0, ctx.cuda_device_context().stream()>>>(
+    KeYoloBoxFw<T><<<grid_dim, 4, 0, ctx.cuda_device_context().stream()>>>(
 	input_data, imgsize_data, boxes_data, scores_data, conf_thresh, 
-	anchors_data, h, w, an_num, class_num, box_num, input_size);	
+	anchors_data, h, w, an_num, class_num, n * box_num, input_size);	
   }
 };