diff --git a/example/ssd/detect/detector.py b/example/ssd/detect/detector.py
index 3a7f89fcadd1..2a64b5e5e9eb 100644
--- a/example/ssd/detect/detector.py
+++ b/example/ssd/detect/detector.py
@@ -15,12 +15,12 @@
 # specific language governing permissions and limitations
 # under the License.
 
-from __future__ import print_function
 import mxnet as mx
 import numpy as np
 from timeit import default_timer as timer
 from dataset.testdb import TestDB
 from dataset.iterator import DetIter
+import logging
 
 class Detector(object):
     """
@@ -43,6 +43,7 @@ class Detector(object):
     ctx : mx.ctx
         device to use, if None, use mx.cpu() as default context
     """
+    CLASS = 0
     def __init__(self, symbol, model_prefix, epoch, data_shape, mean_pixels, \
                  batch_size=1, ctx=None):
         self.ctx = ctx
@@ -51,7 +52,7 @@ def __init__(self, symbol, model_prefix, epoch, data_shape, mean_pixels, \
         load_symbol, args, auxs = mx.model.load_checkpoint(model_prefix, epoch)
         if symbol is None:
             symbol = load_symbol
-        self.mod = mx.mod.Module(symbol, label_names=None, context=ctx)
+        self.mod = mx.mod.Module(symbol, label_names=None, context=self.ctx)
         if not isinstance(data_shape, tuple):
             data_shape = (data_shape, data_shape)
         self.data_shape = data_shape
@@ -81,13 +82,9 @@ def detect(self, det_iter, show_timer=False):
         detections = self.mod.predict(det_iter).asnumpy()
         time_elapsed = timer() - start
         if show_timer:
-            print("Detection time for {} images: {:.4f} sec".format(
+            logging.info("Detection time for {} images: {:.4f} sec".format(
                 num_images, time_elapsed))
-        result = []
-        for i in range(detections.shape[0]):
-            det = detections[i, :, :]
-            res = det[np.where(det[:, 0] >= 0)[0]]
-            result.append(res)
+        result = Detector.filter_positive_detections(detections)
         return result
 
     def im_detect(self, im_list, root_dir=None, extension=None, show_timer=False):
@@ -136,31 +133,52 @@ class names
         height = img.shape[0]
         width = img.shape[1]
         colors = dict()
-        for i in range(dets.shape[0]):
-            cls_id = int(dets[i, 0])
-            if cls_id >= 0:
-                score = dets[i, 1]
-                if score > thresh:
-                    if cls_id not in colors:
-                        colors[cls_id] = (random.random(), random.random(), random.random())
-                    xmin = int(dets[i, 2] * width)
-                    ymin = int(dets[i, 3] * height)
-                    xmax = int(dets[i, 4] * width)
-                    ymax = int(dets[i, 5] * height)
-                    rect = plt.Rectangle((xmin, ymin), xmax - xmin,
-                                         ymax - ymin, fill=False,
-                                         edgecolor=colors[cls_id],
-                                         linewidth=3.5)
-                    plt.gca().add_patch(rect)
-                    class_name = str(cls_id)
-                    if classes and len(classes) > cls_id:
-                        class_name = classes[cls_id]
-                    plt.gca().text(xmin, ymin - 2,
-                                    '{:s} {:.3f}'.format(class_name, score),
-                                    bbox=dict(facecolor=colors[cls_id], alpha=0.5),
+        for det in dets:
+            (klass, score, x0, y0, x1, y1) = det
+            if score < thresh:
+                continue
+            cls_id = int(klass)
+            if cls_id not in colors:
+                colors[cls_id] = (random.random(), random.random(), random.random())
+            xmin = int(x0 * width)
+            ymin = int(y0 * height)
+            xmax = int(x1 * width)
+            ymax = int(y1 * height)
+            rect = plt.Rectangle((xmin, ymin), xmax - xmin,
+                                 ymax - ymin, fill=False,
+                                 edgecolor=colors[cls_id],
+                                 linewidth=3.5)
+            plt.gca().add_patch(rect)
+            class_name = str(cls_id)
+            if classes and len(classes) > cls_id:
+                class_name = classes[cls_id]
+            plt.gca().text(xmin, ymin - 2,
+                            '{:s} {:.3f}'.format(class_name, score),
+                            bbox=dict(facecolor=colors[cls_id], alpha=0.5),
                                     fontsize=12, color='white')
         plt.show()
 
+    @staticmethod
+    def filter_positive_detections(detections):
+        """
+        First column (class id) is -1 for negative detections
+        :param detections:
+        :return:
+        """
+        print(type(detections))
+        assert((type(detections) is mx.nd.NDArray) or (type(detections) is np.ndarray))
+        detections_per_image = []
+        # for each image
+        for i in range(detections.shape[0]):
+            result = []
+            det = detections[i, :, :]
+            for obj in det:
+                if obj[Detector.CLASS] >= 0:
+                    result.append(obj)
+            detections_per_image.append(result)
+        logging.info("%d positive detections", len(result))
+        return detections_per_image
+
     def detect_and_visualize(self, im_list, root_dir=None, extension=None,
                              classes=[], thresh=0.6, show_timer=False):
         """
@@ -187,5 +205,5 @@ def detect_and_visualize(self, im_list, root_dir=None, extension=None,
         assert len(dets) == len(im_list)
         for k, det in enumerate(dets):
             img = cv2.imread(im_list[k])
-            img[:, :, (0, 1, 2)] = img[:, :, (2, 1, 0)]
+            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
             self.visualize_detection(img, det, classes, thresh)