--- a
+++ b/BioSeqNet/resnest/torch/resnest.py
@@ -0,0 +1,93 @@
+##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+## Created by: Hang Zhang
+## Email: zhanghang0704@gmail.com
+## Copyright (c) 2020
+##
+## LICENSE file in the root directory of this source tree 
+##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+"""ResNeSt models"""
+
+import torch
+from .resnet import ResNet, Bottleneck
+
+__all__ = ['resnest50', 'resnest101', 'resnest200', 'resnest269', 'resnest14', 'resnest26']
+
+_url_format = 'https://hangzh.s3.amazonaws.com/encoding/models/{}-{}.pth'
+
+_model_sha256 = {name: checksum for checksum, name in [
+    ('528c19ca', 'resnest50'),
+    ('22405ba7', 'resnest101'),
+    ('75117900', 'resnest200'),
+    ('0cc87c48', 'resnest269'),
+    ]}
+
+def short_hash(name):
+    if name not in _model_sha256:
+        raise ValueError('Pretrained model for {name} is not available.'.format(name=name))
+    return _model_sha256[name][:8]
+
+resnest_model_urls = {name: _url_format.format(name, short_hash(name)) for
+    name in _model_sha256.keys()
+}
+
+def resnest14(pretrained=False, root='~/.encoding/models', **kwargs):
+    model = ResNet(Bottleneck, [1, 1, 1, 1],
+                   radix=2, groups=1, bottleneck_width=64,
+                   deep_stem=True, stem_width=32, avg_down=True,
+                   avd=True, avd_first=False, **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.hub.load_state_dict_from_url(
+            resnest_model_urls['resnest14'], progress=True, check_hash=True))
+    return model
+
+def resnest26(pretrained=False, root='~/.encoding/models', **kwargs):
+    model = ResNet(Bottleneck, [2, 2, 2, 2],
+                   radix=2, groups=1, bottleneck_width=64,
+                   deep_stem=True, stem_width=32, avg_down=True,
+                   avd=True, avd_first=False, **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.hub.load_state_dict_from_url(
+            resnest_model_urls['resnest26'], progress=True, check_hash=True))
+    return model
+
+
+
+def resnest50(pretrained=False, root='~/.encoding/models', **kwargs):
+    model = ResNet(Bottleneck, [3, 4, 6, 3],
+                   radix=2, groups=1, bottleneck_width=64,
+                   deep_stem=True, stem_width=32, avg_down=True,
+                   avd=True, avd_first=False, **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.hub.load_state_dict_from_url(
+            resnest_model_urls['resnest50'], progress=True, check_hash=True))
+    return model
+
+def resnest101(pretrained=False, root='~/.encoding/models', **kwargs):
+    model = ResNet(Bottleneck, [3, 4, 23, 3],
+                   radix=2, groups=1, bottleneck_width=64,
+                   deep_stem=True, stem_width=64, avg_down=True,
+                   avd=True, avd_first=False, **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.hub.load_state_dict_from_url(
+            resnest_model_urls['resnest101'], progress=True, check_hash=True))
+    return model
+
+def resnest200(pretrained=False, root='~/.encoding/models', **kwargs):
+    model = ResNet(Bottleneck, [3, 24, 36, 3],
+                   radix=2, groups=1, bottleneck_width=64,
+                   deep_stem=True, stem_width=64, avg_down=True,
+                   avd=True, avd_first=False, **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.hub.load_state_dict_from_url(
+            resnest_model_urls['resnest200'], progress=True, check_hash=True))
+    return model
+
+def resnest269(pretrained=False, root='~/.encoding/models', **kwargs):
+    model = ResNet(Bottleneck, [3, 30, 48, 8],
+                   radix=2, groups=1, bottleneck_width=64,
+                   deep_stem=True, stem_width=64, avg_down=True,
+                   avd=True, avd_first=False, **kwargs)
+    if pretrained:
+        model.load_state_dict(torch.hub.load_state_dict_from_url(
+            resnest_model_urls['resnest269'], progress=True, check_hash=True))
+    return model