diff --git a/src/image_iter.py b/src/image_iter.py
index 58fba86..a03cf14 100644
--- a/src/image_iter.py
+++ b/src/image_iter.py
@@ -32,6 +32,7 @@ class FaceImageIter(io.DataIter):
                  path_imgrec = None,
                  shuffle=False, aug_list=None, mean = None,
                  rand_mirror = False, cutoff = 0, color_jittering = 0,
+                 images_filter = 0,
                  data_name='data', label_name='softmax_label', **kwargs):
         super(FaceImageIter, self).__init__()
         assert path_imgrec
@@ -46,15 +47,19 @@ class FaceImageIter(io.DataIter):
               print('header0 label', header.label)
               self.header0 = (int(header.label[0]), int(header.label[1]))
               #assert(header.flag==1)
-              self.imgidx = range(1, int(header.label[0]))
+              #self.imgidx = range(1, int(header.label[0]))
+              self.imgidx = []
               self.id2range = {}
               self.seq_identity = range(int(header.label[0]), int(header.label[1]))
               for identity in self.seq_identity:
                 s = self.imgrec.read_idx(identity)
                 header, _ = recordio.unpack(s)
                 a,b = int(header.label[0]), int(header.label[1])
-                self.id2range[identity] = (a,b)
                 count = b-a
+                if count<images_filter:
+                  continue
+                self.id2range[identity] = (a,b)
+                self.imgidx += range(a, b)
               print('id2range', len(self.id2range))
             else:
               self.imgidx = list(self.imgrec.keys)
diff --git a/src/train_softmax.py b/src/train_softmax.py
index 9ec02e4..5716ae7 100644
--- a/src/train_softmax.py
+++ b/src/train_softmax.py
@@ -125,6 +125,7 @@ def parse_args():
   parser.add_argument('--rand-mirror', type=int, default=1, help='if do random mirror in training')
   parser.add_argument('--cutoff', type=int, default=0, help='cut off aug')
   parser.add_argument('--color', type=int, default=0, help='color jittering aug')
+  parser.add_argument('--images-filter', type=int, default=0, help='minimum images per identity filter')
   parser.add_argument('--target', type=str, default='lfw,cfp_fp,agedb_30', help='verification targets')
   parser.add_argument('--ce-loss', default=False, action='store_true', help='if output ce loss')
   args = parser.parse_args()
@@ -272,9 +273,71 @@ def get_symbol(args, arg_params, aux_params):
         gt_one_hot = mx.sym.one_hot(gt_label, depth = args.num_classes, on_value = 1.0, off_value = 0.0)
         body = mx.sym.broadcast_mul(gt_one_hot, diff)
         fc7 = fc7+body
+  elif args.loss_type==6:
+    s = args.margin_s
+    m = args.margin_m
+    assert s>0.0
+    _weight = mx.symbol.L2Normalization(_weight, mode='instance')
+    nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')*s
+    fc7 = mx.sym.FullyConnected(data=nembedding, weight = _weight, no_bias = True, num_hidden=args.num_classes, name='fc7')
+    zy = mx.sym.pick(fc7, gt_label, axis=1)
+    cos_t = zy/s
+    t = mx.sym.arccos(cos_t)
+    intra_loss = t/np.pi
+    intra_loss = mx.sym.mean(intra_loss)
+    #intra_loss = mx.sym.exp(cos_t*-1.0)
+    intra_loss = mx.sym.MakeLoss(intra_loss, name='intra_loss', grad_scale = 1)
+    if m>0.0:
+      t = t+m
+      body = mx.sym.cos(t)
+      new_zy = body*s
+      diff = new_zy - zy
+      diff = mx.sym.expand_dims(diff, 1)
+      gt_one_hot = mx.sym.one_hot(gt_label, depth = args.num_classes, on_value = 1.0, off_value = 0.0)
+      body = mx.sym.broadcast_mul(gt_one_hot, diff)
+      fc7 = fc7+body
+  elif args.loss_type==7:
+    s = args.margin_s
+    m = args.margin_m
+    assert s>0.0
+    _weight = mx.symbol.L2Normalization(_weight, mode='instance')
+    nembedding = mx.symbol.L2Normalization(embedding, mode='instance', name='fc1n')*s
+    fc7 = mx.sym.FullyConnected(data=nembedding, weight = _weight, no_bias = True, num_hidden=args.num_classes, name='fc7')
+    zy = mx.sym.pick(fc7, gt_label, axis=1)
+    cos_t = zy/s
+    t = mx.sym.arccos(cos_t)
+    intra_loss = t/np.pi
+    intra_loss = mx.sym.MakeLoss(intra_loss, name='intra_loss', grad_scale = 0.1)
+    #idx = mx.sym.arange(0, args.num_classes)
+    #idx = mx.sym.random.shuffle(idx)
+    #idx = mx.sym.slice(data=idx, begin=0, end=100)
+    #counter_weight = mx.sym.take(_weight, indices=idx, axis=1)
+    #counter_weight = mx.sym.pick(_weight, gt_label, axis=1)
+    counter_weight = mx.sym.take(_weight, gt_label, axis=1)
+    counter_cos = mx.sym.dot(counter_weight, _weight, transpose_a=True)
+    counter_angle = mx.sym.arccos(counter_cos)
+    inter_loss = counter_angle/np.pi #[0,1]
+    inter_loss = inter_loss*-1.0 # [-1,0]
+    inter_loss = inter_loss+1.0 # [0,1]
+    inter_loss = mx.sym.mean(inter_loss)
+    inter_loss = mx.sym.MakeLoss(inter_loss, name='inter_loss', grad_scale = 0.1)
+    if m>0.0:
+      t = t+m
+      body = mx.sym.cos(t)
+      new_zy = body*s
+      diff = new_zy - zy
+      diff = mx.sym.expand_dims(diff, 1)
+      gt_one_hot = mx.sym.one_hot(gt_label, depth = args.num_classes, on_value = 1.0, off_value = 0.0)
+      body = mx.sym.broadcast_mul(gt_one_hot, diff)
+      fc7 = fc7+body
   out_list = [mx.symbol.BlockGrad(embedding)]
   softmax = mx.symbol.SoftmaxOutput(data=fc7, label = gt_label, name='softmax', normalization='valid')
   out_list.append(softmax)
+  if args.loss_type==6:
+    out_list.append(intra_loss)
+  if args.loss_type==7:
+    out_list.append(inter_loss)
+    #out_list.append(intra_loss)
   if args.ce_loss:
     #ce_loss = mx.symbol.softmax_cross_entropy(data=fc7, label = gt_label, name='ce_loss')/args.per_batch_size
     body = mx.symbol.SoftmaxActivation(data=fc7)
@@ -346,6 +409,9 @@ def train_net(args):
       arg_params = None
       aux_params = None
       sym, arg_params, aux_params = get_symbol(args, arg_params, aux_params)
+      if args.network[0]=='s':
+        data_shape_dict = {'data' : (args.per_batch_size,)+data_shape}
+        spherenet.init_weights(sym, data_shape_dict, args.num_layers)
     else:
       vec = args.pretrained.split(',')
       print('loading', vec)
@@ -369,6 +435,7 @@ def train_net(args):
         mean                 = mean,
         cutoff               = args.cutoff,
         color_jittering      = args.color,
+        images_filter        = args.images_filter,
     )
 
     metric1 = AccMetric()
@@ -446,20 +513,33 @@ def train_net(args):
         save_step[0]+=1
         msave = save_step[0]
         do_save = False
+        is_highest = False
         if len(acc_list)>0:
-          lfw_score = acc_list[0]
-          if lfw_score>highest_acc[0]:
-            highest_acc[0] = lfw_score
-            if lfw_score>=0.998:
-              do_save = True
+          #lfw_score = acc_list[0]
+          #if lfw_score>highest_acc[0]:
+          #  highest_acc[0] = lfw_score
+          #  if lfw_score>=0.998:
+          #    do_save = True
+          score = sum(acc_list)
           if acc_list[-1]>=highest_acc[-1]:
+            if acc_list[-1]>highest_acc[-1]:
+              is_highest = True
+            else:
+              if score>=highest_acc[0]:
+                is_highest = True
+                highest_acc[0] = score
             highest_acc[-1] = acc_list[-1]
-            if lfw_score>=0.99:
-              do_save = True
+            #if lfw_score>=0.99:
+            #  do_save = True
+        if is_highest:
+          do_save = True
         if args.ckpt==0:
           do_save = False
-        elif args.ckpt>1:
+        elif args.ckpt==2:
           do_save = True
+        elif args.ckpt==3:
+          msave = 1
+
         if do_save:
           print('saving', msave)
           arg, aux = model.get_params()