diff --git a/fluid/PaddleCV/yolov3/data_utils.py b/fluid/PaddleCV/yolov3/data_utils.py
index ac022593119e0008c3f7f3858303cbf5bc717650..c43e040af8d46f0fc57ee9862da4ee749768e7c6 100644
--- a/fluid/PaddleCV/yolov3/data_utils.py
+++ b/fluid/PaddleCV/yolov3/data_utils.py
@@ -124,7 +124,7 @@ class GeneratorEnqueuer(object):
         for thread in self._threads:
             if self._use_multiprocessing:
                 if thread.is_alive():
-                    thread.terminate()
+                    thread.join(timeout)
             else:
                 thread.join(timeout)
         if self._manager:
diff --git a/fluid/PaddleCV/yolov3/image_utils.py b/fluid/PaddleCV/yolov3/image_utils.py
index 6933721e88a413c69769ae27a24821064c450ad6..a35e20be71fe10cce2c4629cb64ea4c5e74cfe27 100644
--- a/fluid/PaddleCV/yolov3/image_utils.py
+++ b/fluid/PaddleCV/yolov3/image_utils.py
@@ -154,7 +154,7 @@ def random_expand(img, gtboxes, max_ratio=4., fill=None, keep_ratio=True, thresh
 
 def shuffle_gtbox(gtbox, gtlabel, gtscore):
     gt = np.concatenate([gtbox, gtlabel[:, np.newaxis], gtscore[:, np.newaxis]], axis=1)
-    idx = np.arange(gt.shape[1])
+    idx = np.arange(gt.shape[0])
     np.random.shuffle(idx)
     gt = gt[idx, :]
     return gt[:, :4], gt[:, 4], gt[:, 5]
diff --git a/fluid/PaddleCV/yolov3/models/yolov3.py b/fluid/PaddleCV/yolov3/models/yolov3.py
index b28c14bdb6afe302eea967f54cf6c3f2624824f2..9ea4ef4433bb647967c4c6ef4160bc6ab491bd61 100644
--- a/fluid/PaddleCV/yolov3/models/yolov3.py
+++ b/fluid/PaddleCV/yolov3/models/yolov3.py
@@ -134,7 +134,6 @@ class YOLOv3(object):
             anchor_mask = cfg.anchor_masks[i]
 
             if self.is_train:
-                ignore_thresh = float(self.ignore_thresh)
                 loss = fluid.layers.yolov3_loss(
                         x=out,
                         gtbox=self.gtbox,
diff --git a/fluid/PaddleCV/yolov3/reader.py b/fluid/PaddleCV/yolov3/reader.py
index 5d37402d004bd3346a76b3fe3ff7fd521571e619..1f3ce3545d8b27c2ea14168cdaf1958bce563703 100644
--- a/fluid/PaddleCV/yolov3/reader.py
+++ b/fluid/PaddleCV/yolov3/reader.py
@@ -250,10 +250,11 @@ dsr = DataSetReader()
 def train(size=416, 
           batch_size=64, 
           shuffle=True, 
+          total_iter=0,
           mixup_iter=0,
           random_sizes=[],
-          num_workers=12,
-          max_queue=36,
+          num_workers=8,
+          max_queue=32,
           use_multiprocessing=True):
     generator = dsr.get_reader('train', size, batch_size, shuffle, int(mixup_iter/num_workers), random_sizes)
 
@@ -266,6 +267,7 @@ def train(size=416,
                 yield data
 
     def reader():
+        cnt = 0
         try:
             enqueuer = GeneratorEnqueuer(
                 infinite_reader(), use_multiprocessing=use_multiprocessing)
@@ -279,6 +281,10 @@ def train(size=416,
                     else:
                         time.sleep(0.02)
                 yield generator_out
+                cnt += 1
+                if cnt >= total_iter:
+                    enqueuer.stop()
+                    return
                 generator_out = None
         finally:
             if enqueuer is not None:
diff --git a/fluid/PaddleCV/yolov3/train.py b/fluid/PaddleCV/yolov3/train.py
index f3ac6e3e3c0d6ede424f7c5cfa1b15fd3642254a..07f74a545c3cbd5047e3304a609d2bafe159a2d7 100644
--- a/fluid/PaddleCV/yolov3/train.py
+++ b/fluid/PaddleCV/yolov3/train.py
@@ -86,8 +86,9 @@ def train():
     if cfg.random_shape:
         random_sizes = [32 * i for i in range(10, 20)]
 
-    mixup_iter = cfg.max_iter - cfg.start_iter - cfg.no_mixup_iter
-    train_reader = reader.train(input_size, batch_size=cfg.batch_size, shuffle=True, mixup_iter=mixup_iter*devices_num, random_sizes=random_sizes, use_multiprocessing=cfg.use_multiprocess)
+    total_iter = cfg.max_iter - cfg.start_iter
+    mixup_iter = total_iter - cfg.no_mixup_iter
+    train_reader = reader.train(input_size, batch_size=cfg.batch_size, shuffle=True, total_iter=total_iter*devices_num, mixup_iter=mixup_iter*devices_num, random_sizes=random_sizes, use_multiprocessing=cfg.use_multiprocess)
     py_reader = model.py_reader
     py_reader.decorate_paddle_reader(train_reader)