--- a
+++ b/BioSeqNet/resnest/gluon/resnest.py
@@ -0,0 +1,55 @@
+##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+## Created by: Hang Zhang
+## Email: zhanghang0704@gmail.com
+## Copyright (c) 2020
+##
+## LICENSE file in the root directory of this source tree 
+##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+"""ResNeSt implemented in Gluon."""
+
+__all__ = ['resnest50', 'resnest101',
+           'resnest200', 'resnest269']
+
+from .resnet import ResNet, Bottleneck
+from mxnet import cpu
+
+def resnest50(pretrained=False, root='~/.mxnet/models', ctx=cpu(0), **kwargs):
+    model = ResNet(Bottleneck, [3, 4, 6, 3],
+                      radix=2, cardinality=1, bottleneck_width=64,
+                      deep_stem=True, avg_down=True,
+                      avd=True, avd_first=False,
+                      use_splat=True, dropblock_prob=0.1,
+                      name_prefix='resnest_', **kwargs)
+    if pretrained:
+        from .model_store import get_model_file
+        model.load_parameters(get_model_file('resnest50', root=root), ctx=ctx)
+    return model
+
+def resnest101(pretrained=False, root='~/.mxnet/models', ctx=cpu(0), **kwargs):
+    model = ResNet(Bottleneck, [3, 4, 23, 3],
+                      radix=2, cardinality=1, bottleneck_width=64,
+                      deep_stem=True, avg_down=True, stem_width=64,
+                      avd=True, avd_first=False, use_splat=True, dropblock_prob=0.1,
+                      name_prefix='resnest_', **kwargs)
+    if pretrained:
+        from .model_store import get_model_file
+        model.load_parameters(get_model_file('resnest101', root=root), ctx=ctx)
+    return model
+
+def resnest200(pretrained=False, root='~/.mxnet/models', ctx=cpu(0), **kwargs):
+    model = ResNet(Bottleneck, [3, 24, 36, 3], deep_stem=True, avg_down=True, stem_width=64,
+                      avd=True, use_splat=True, dropblock_prob=0.1, final_drop=0.2,
+                      name_prefix='resnest_', **kwargs)
+    if pretrained:
+        from .model_store import get_model_file
+        model.load_parameters(get_model_file('resnest200', root=root), ctx=ctx)
+    return model
+
+def resnest269(pretrained=False, root='~/.mxnet/models', ctx=cpu(0), **kwargs):
+    model = ResNet(Bottleneck, [3, 30, 48, 8], deep_stem=True, avg_down=True, stem_width=64,
+                      avd=True, use_splat=True, dropblock_prob=0.1, final_drop=0.2,
+                      name_prefix='resnest_', **kwargs)
+    if pretrained:
+        from .model_store import get_model_file
+        model.load_parameters(get_model_file('resnest269', root=root), ctx=ctx)
+    return model