Add Facebook Research Semi-Supervised and Semi-Weakly Supervised ResNet model weights.

sotabench.py

@@ -167,6 +167,65 @@ model_list = [
     _entry('ig_resnext101_32x48d', 'ResNeXt-101 32x48d (288x288 Mean-Max Pooling)', '1805.00932',
            ttp=True, args=dict(img_size=288), batch_size=BATCH_SIZE // 8),
 
+    ## Facebook SSL weights
+    _entry('ssl_resnet18', 'ResNet-18', '1905.00546',
+           model_desc='Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/'),
+    _entry('ssl_resnet50', 'ResNet-50', '1905.00546',
+           model_desc='Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/'),
+    _entry('ssl_resnext50_32x4d', 'ResNeXt-50 32x4d', '1905.00546',
+           model_desc='Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/'),
+    _entry('ssl_resnext101_32x4d', 'ResNeXt-101 32x4d', '1905.00546',
+           model_desc='Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/'),
+    _entry('ssl_resnext101_32x8d', 'ResNeXt-101 32x8d', '1905.00546',
+           model_desc='Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/'),
+    _entry('ssl_resnext101_32x16d', 'ResNeXt-101 32x16d', '1905.00546',
+           model_desc='Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/'),
+
+    _entry('ssl_resnet50', 'ResNet-50 (288x288 Mean-Max Pooling)', '1905.00546',
+           ttp=True, args=dict(img_size=288),
+           model_desc='Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/'),
+    _entry('ssl_resnext50_32x4d', 'ResNeXt-50 32x4d (288x288 Mean-Max Pooling)', '1905.00546',
+           ttp=True, args=dict(img_size=288),
+           model_desc='Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/'),
+    _entry('ssl_resnext101_32x4d', 'ResNeXt-101 32x4d (288x288 Mean-Max Pooling)', '1905.00546',
+           ttp=True, args=dict(img_size=288),
+           model_desc='Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/'),
+    _entry('ssl_resnext101_32x8d', 'ResNeXt-101 32x8d (288x288 Mean-Max Pooling)', '1905.00546',
+           ttp=True, args=dict(img_size=288),
+           model_desc='Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/'),
+    _entry('ssl_resnext101_32x16d', 'ResNeXt-101 32x16d (288x288 Mean-Max Pooling)', '1905.00546',
+           ttp=True, args=dict(img_size=288), batch_size=BATCH_SIZE // 2,
+           model_desc='Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/'),
+
+    ## Facebook SWSL weights
+    _entry('swsl_resnet18', 'ResNet-18', '1905.00546',
+           model_desc='Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/'),
+    _entry('swsl_resnet50', 'ResNet-50', '1905.00546',
+           model_desc='Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/'),
+    _entry('swsl_resnext50_32x4d', 'ResNeXt-50 32x4d', '1905.00546',
+           model_desc='Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/'),
+    _entry('swsl_resnext101_32x4d', 'ResNeXt-101 32x4d', '1905.00546',
+           model_desc='Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/'),
+    _entry('swsl_resnext101_32x8d', 'ResNeXt-101 32x8d', '1905.00546',
+           model_desc='Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/'),
+    _entry('swsl_resnext101_32x16d', 'ResNeXt-101 32x16d', '1905.00546'),
+
+    _entry('swsl_resnet50', 'ResNet-50 (288x288 Mean-Max Pooling)', '1905.00546',
+           ttp=True, args=dict(img_size=288),
+           model_desc='Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/'),
+    _entry('swsl_resnext50_32x4d', 'ResNeXt-50 32x4d (288x288 Mean-Max Pooling)', '1905.00546',
+           ttp=True, args=dict(img_size=288),
+           model_desc='Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/'),
+    _entry('swsl_resnext101_32x4d', 'ResNeXt-101 32x4d (288x288 Mean-Max Pooling)', '1905.00546',
+           ttp=True, args=dict(img_size=288),
+           model_desc='Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/'),
+    _entry('swsl_resnext101_32x8d', 'ResNeXt-101 32x8d (288x288 Mean-Max Pooling)', '1905.00546',
+           ttp=True, args=dict(img_size=288),
+           model_desc='Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/'),
+    _entry('swsl_resnext101_32x16d', 'ResNeXt-101 32x16d (288x288 Mean-Max Pooling)', '1905.00546',
+           ttp=True, args=dict(img_size=288), batch_size=BATCH_SIZE // 2,
+           model_desc='Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/'),
+
     ## DLA official impl weights (to remove if sotabench added to source)
     _entry('dla34', 'DLA-34', '1707.06484'),
     _entry('dla46_c', 'DLA-46-C', '1707.06484'),

timm/models/helpers.py

@@ -57,15 +57,17 @@ def resume_checkpoint(model, checkpoint_path):
         raise FileNotFoundError()
 
 
-def load_pretrained(model, default_cfg, num_classes=1000, in_chans=3, filter_fn=None):
+def load_pretrained(model, cfg=None, num_classes=1000, in_chans=3, filter_fn=None):
-    if 'url' not in default_cfg or not default_cfg['url']:
+    if cfg is None:
+        cfg = getattr(model, 'default_cfg')
+    if cfg is None or 'url' not in cfg or not cfg['url']:
         logging.warning("Pretrained model URL is invalid, using random initialization.")
         return
 
-    state_dict = model_zoo.load_url(default_cfg['url'], progress=False)
+    state_dict = model_zoo.load_url(cfg['url'], progress=False)
 
     if in_chans == 1:
-        conv1_name = default_cfg['first_conv']
+        conv1_name = cfg['first_conv']
         logging.info('Converting first conv (%s) from 3 to 1 channel' % conv1_name)
         conv1_weight = state_dict[conv1_name + '.weight']
         state_dict[conv1_name + '.weight'] = conv1_weight.sum(dim=1, keepdim=True)
@@ -73,14 +75,14 @@ def load_pretrained(model, default_cfg, num_classes=1000, in_chans=3, filter_fn=
         assert False, "Invalid in_chans for pretrained weights"
 
     strict = True
-    classifier_name = default_cfg['classifier']
+    classifier_name = cfg['classifier']
-    if num_classes == 1000 and default_cfg['num_classes'] == 1001:
+    if num_classes == 1000 and cfg['num_classes'] == 1001:
         # special case for imagenet trained models with extra background class in pretrained weights
         classifier_weight = state_dict[classifier_name + '.weight']
         state_dict[classifier_name + '.weight'] = classifier_weight[1:]
         classifier_bias = state_dict[classifier_name + '.bias']
         state_dict[classifier_name + '.bias'] = classifier_bias[1:]
-    elif num_classes != default_cfg['num_classes']:
+    elif num_classes != cfg['num_classes']:
         # completely discard fully connected for all other differences between pretrained and created model
         del state_dict[classifier_name + '.weight']
         del state_dict[classifier_name + '.bias']

timm/models/resnet.py

@@ -67,6 +67,30 @@ default_cfgs = {
     'ig_resnext101_32x16d': _cfg(url='https://download.pytorch.org/models/ig_resnext101_32x16-c6f796b0.pth'),
     'ig_resnext101_32x32d': _cfg(url='https://download.pytorch.org/models/ig_resnext101_32x32-e4b90b00.pth'),
     'ig_resnext101_32x48d': _cfg(url='https://download.pytorch.org/models/ig_resnext101_32x48-3e41cc8a.pth'),
+    'ssl_resnet18':  _cfg(
+        url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet18-d92f0530.pth'),
+    'ssl_resnet50':  _cfg(
+        url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnet50-08389792.pth'),
+    'ssl_resnext50_32x4d': _cfg(
+        url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext50_32x4-ddb3e555.pth'),
+    'ssl_resnext101_32x4d': _cfg(
+        url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x4-dc43570a.pth'),
+    'ssl_resnext101_32x8d': _cfg(
+        url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x8-2cfe2f8b.pth'),
+    'ssl_resnext101_32x16d': _cfg(
+        url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_supervised_resnext101_32x16-15fffa57.pth'),
+    'swsl_resnet18': _cfg(
+        url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet18-118f1556.pth'),
+    'swsl_resnet50': _cfg(
+        url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnet50-16a12f1b.pth'),
+    'swsl_resnext50_32x4d': _cfg(
+        url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext50_32x4-72679e44.pth'),
+    'swsl_resnext101_32x4d': _cfg(
+        url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x4-3f87e46b.pth'),
+    'swsl_resnext101_32x8d': _cfg(
+        url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x8-b4712904.pth'),
+    'swsl_resnext101_32x16d': _cfg(
+        url='https://dl.fbaipublicfiles.com/semiweaksupervision/model_files/semi_weakly_supervised_resnext101_32x16-f3559a9c.pth'),
 }
 
 
@@ -621,80 +645,218 @@ def tv_resnext50_32x4d(pretrained=False, num_classes=1000, in_chans=3, **kwargs)
 
 
 @register_model
-def ig_resnext101_32x8d(pretrained=True, num_classes=1000, in_chans=3, **kwargs):
+def ig_resnext101_32x8d(pretrained=True, **kwargs):
     """Constructs a ResNeXt-101 32x8 model pre-trained on weakly-supervised data
     and finetuned on ImageNet from Figure 5 in
     `"Exploring the Limits of Weakly Supervised Pretraining" <https://arxiv.org/abs/1805.00932>`_
     Weights from https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/
-    Args:
+    """
-        pretrained (bool): load pretrained weights
+    model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=8, **kwargs)
-        num_classes (int): number of classes for classifier (default: 1000 for pretrained)
+    model.default_cfg = default_cfgs['ig_resnext101_32x8d']
-        in_chans (int): number of input planes (default: 3 for pretrained / color)
-    """
-    default_cfg = default_cfgs['ig_resnext101_32x8d']
-    model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=8,
-                   num_classes=1000, in_chans=3, **kwargs)
-    model.default_cfg = default_cfg
     if pretrained:
-        load_pretrained(model, default_cfg, num_classes, in_chans)
+        load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3))
     return model
 
 
 @register_model
-def ig_resnext101_32x16d(pretrained=True, num_classes=1000, in_chans=3, **kwargs):
+def ig_resnext101_32x16d(pretrained=True, **kwargs):
     """Constructs a ResNeXt-101 32x16 model pre-trained on weakly-supervised data
     and finetuned on ImageNet from Figure 5 in
     `"Exploring the Limits of Weakly Supervised Pretraining" <https://arxiv.org/abs/1805.00932>`_
     Weights from https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/
-    Args:
+    """
-        pretrained (bool): load pretrained weights
+    model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=16, **kwargs)
-        num_classes (int): number of classes for classifier (default: 1000 for pretrained)
+    model.default_cfg = default_cfgs['ig_resnext101_32x16d']
-        in_chans (int): number of input planes (default: 3 for pretrained / color)
-    """
-    default_cfg = default_cfgs['ig_resnext101_32x16d']
-    model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=16,
-                   num_classes=1000, in_chans=3, **kwargs)
-    model.default_cfg = default_cfg
     if pretrained:
-        load_pretrained(model, default_cfg, num_classes, in_chans)
+        load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3))
     return model
 
 
 @register_model
-def ig_resnext101_32x32d(pretrained=True, num_classes=1000, in_chans=3, **kwargs):
+def ig_resnext101_32x32d(pretrained=True, **kwargs):
     """Constructs a ResNeXt-101 32x32 model pre-trained on weakly-supervised data
     and finetuned on ImageNet from Figure 5 in
     `"Exploring the Limits of Weakly Supervised Pretraining" <https://arxiv.org/abs/1805.00932>`_
     Weights from https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/
-    Args:
+    """
-        pretrained (bool): load pretrained weights
+    model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=32, **kwargs)
-        num_classes (int): number of classes for classifier (default: 1000 for pretrained)
+    model.default_cfg = default_cfgs['ig_resnext101_32x32d']
-        in_chans (int): number of input planes (default: 3 for pretrained / color)
-    """
-    default_cfg = default_cfgs['ig_resnext101_32x32d']
-    model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=32,
-                   num_classes=1000, in_chans=3, **kwargs)
-    model.default_cfg = default_cfg
     if pretrained:
-        load_pretrained(model, default_cfg, num_classes, in_chans)
+        load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3))
     return model
 
 
 @register_model
-def ig_resnext101_32x48d(pretrained=True, num_classes=1000, in_chans=3, **kwargs):
+def ig_resnext101_32x48d(pretrained=True, **kwargs):
     """Constructs a ResNeXt-101 32x48 model pre-trained on weakly-supervised data
     and finetuned on ImageNet from Figure 5 in
     `"Exploring the Limits of Weakly Supervised Pretraining" <https://arxiv.org/abs/1805.00932>`_
     Weights from https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/
-    Args:
+    """
-        pretrained (bool): load pretrained weights
+    model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=48, **kwargs)
-        num_classes (int): number of classes for classifier (default: 1000 for pretrained)
+    model.default_cfg = default_cfgs['ig_resnext101_32x48d']
-        in_chans (int): number of input planes (default: 3 for pretrained / color)
-    """
-    default_cfg = default_cfgs['ig_resnext101_32x48d']
-    model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=48,
-                   num_classes=1000, in_chans=3, **kwargs)
-    model.default_cfg = default_cfg
     if pretrained:
-        load_pretrained(model, default_cfg, num_classes, in_chans)
+        load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3))
+    return model
+
+
+@register_model
+def ssl_resnet18(pretrained=True, **kwargs):
+    """Constructs a semi-supervised ResNet-18 model pre-trained on YFCC100M dataset and finetuned on ImageNet
+    `"Billion-scale Semi-Supervised Learning for Image Classification" <https://arxiv.org/abs/1905.00546>`_
+    Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/
+    """
+    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
+    model.default_cfg = default_cfgs['ssl_resnet18']
+    if pretrained:
+        load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3))
+    return model
+
+
+@register_model
+def ssl_resnet50(pretrained=True, **kwargs):
+    """Constructs a semi-supervised ResNet-50 model pre-trained on YFCC100M dataset and finetuned on ImageNet
+    `"Billion-scale Semi-Supervised Learning for Image Classification" <https://arxiv.org/abs/1905.00546>`_
+    Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/
+    """
+    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
+    model.default_cfg = default_cfgs['ssl_resnet50']
+    if pretrained:
+        load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3))
+    return model
+
+
+@register_model
+def ssl_resnext50_32x4d(pretrained=True, **kwargs):
+    """Constructs a semi-supervised ResNeXt-50 32x4 model pre-trained on YFCC100M dataset and finetuned on ImageNet
+    `"Billion-scale Semi-Supervised Learning for Image Classification" <https://arxiv.org/abs/1905.00546>`_
+    Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/
+    """
+    model = ResNet(Bottleneck, [3, 4, 6, 3], cardinality=32, base_width=4, **kwargs)
+    model.default_cfg = default_cfgs['ssl_resnext50_32x4d']
+    if pretrained:
+        load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3))
+    return model
+
+
+@register_model
+def ssl_resnext101_32x4d(pretrained=True, **kwargs):
+    """Constructs a semi-supervised ResNeXt-101 32x4 model pre-trained on YFCC100M dataset and finetuned on ImageNet
+    `"Billion-scale Semi-Supervised Learning for Image Classification" <https://arxiv.org/abs/1905.00546>`_
+    Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/
+    """
+    model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=4, **kwargs)
+    model.default_cfg = default_cfgs['ssl_resnext101_32x4d']
+    if pretrained:
+        load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3))
+    return model
+
+
+@register_model
+def ssl_resnext101_32x8d(pretrained=True, **kwargs):
+    """Constructs a semi-supervised ResNeXt-101 32x8 model pre-trained on YFCC100M dataset and finetuned on ImageNet
+    `"Billion-scale Semi-Supervised Learning for Image Classification" <https://arxiv.org/abs/1905.00546>`_
+    Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/
+    """
+    model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=8, **kwargs)
+    model.default_cfg = default_cfgs['ssl_resnext101_32x8d']
+    if pretrained:
+        load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3))
+    return model
+
+
+@register_model
+def ssl_resnext101_32x16d(pretrained=True, **kwargs):
+    """Constructs a semi-supervised ResNeXt-101 32x16 model pre-trained on YFCC100M dataset and finetuned on ImageNet
+    `"Billion-scale Semi-Supervised Learning for Image Classification" <https://arxiv.org/abs/1905.00546>`_
+    Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/
+    """
+    model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=16, **kwargs)
+    model.default_cfg = default_cfgs['ssl_resnext101_32x16d']
+    if pretrained:
+        load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3))
+    return model
+
+
+@register_model
+def swsl_resnet18(pretrained=True, **kwargs):
+    """Constructs a semi-weakly supervised Resnet-18 model pre-trained on 1B weakly supervised
+       image dataset and finetuned on ImageNet.
+       `"Billion-scale Semi-Supervised Learning for Image Classification" <https://arxiv.org/abs/1905.00546>`_
+       Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/
+    """
+    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
+    model.default_cfg = default_cfgs['swsl_resnet18']
+    if pretrained:
+        load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3))
+    return model
+
+
+@register_model
+def swsl_resnet50(pretrained=True, **kwargs):
+    """Constructs a semi-weakly supervised ResNet-50 model pre-trained on 1B weakly supervised
+       image dataset and finetuned on ImageNet.
+       `"Billion-scale Semi-Supervised Learning for Image Classification" <https://arxiv.org/abs/1905.00546>`_
+       Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/
+    """
+    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
+    model.default_cfg = default_cfgs['swsl_resnet50']
+    if pretrained:
+        load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3))
+    return model
+
+
+@register_model
+def swsl_resnext50_32x4d(pretrained=True, **kwargs):
+    """Constructs a semi-weakly supervised ResNeXt-50 32x4 model pre-trained on 1B weakly supervised
+       image dataset and finetuned on ImageNet.
+       `"Billion-scale Semi-Supervised Learning for Image Classification" <https://arxiv.org/abs/1905.00546>`_
+       Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/
+    """
+    model = ResNet(Bottleneck, [3, 4, 6, 3], cardinality=32, base_width=4, **kwargs)
+    model.default_cfg = default_cfgs['swsl_resnext50_32x4d']
+    if pretrained:
+        load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3))
+    return model
+
+
+@register_model
+def swsl_resnext101_32x4d(pretrained=True, **kwargs):
+    """Constructs a semi-weakly supervised ResNeXt-101 32x4 model pre-trained on 1B weakly supervised
+       image dataset and finetuned on ImageNet.
+       `"Billion-scale Semi-Supervised Learning for Image Classification" <https://arxiv.org/abs/1905.00546>`_
+       Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/
+    """
+    model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=4, **kwargs)
+    model.default_cfg = default_cfgs['swsl_resnext101_32x4d']
+    if pretrained:
+        load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3))
+    return model
+
+
+@register_model
+def swsl_resnext101_32x8d(pretrained=True, **kwargs):
+    """Constructs a semi-weakly supervised ResNeXt-101 32x8 model pre-trained on 1B weakly supervised
+       image dataset and finetuned on ImageNet.
+       `"Billion-scale Semi-Supervised Learning for Image Classification" <https://arxiv.org/abs/1905.00546>`_
+       Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/
+    """
+    model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=8, **kwargs)
+    model.default_cfg = default_cfgs['swsl_resnext101_32x8d']
+    if pretrained:
+        load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3))
+    return model
+
+
+@register_model
+def swsl_resnext101_32x16d(pretrained=True, **kwargs):
+    """Constructs a semi-weakly supervised ResNeXt-101 32x16 model pre-trained on 1B weakly supervised
+       image dataset and finetuned on ImageNet.
+       `"Billion-scale Semi-Supervised Learning for Image Classification" <https://arxiv.org/abs/1905.00546>`_
+       Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/
+    """
+    model = ResNet(Bottleneck, [3, 4, 23, 3], cardinality=32, base_width=16, **kwargs)
+    model.default_cfg = default_cfgs['swsl_resnext101_32x16d']
+    if pretrained:
+        load_pretrained(model, num_classes=kwargs.get('num_classes', 0), in_chans=kwargs.get('in_chans', 3))
     return model