diff --git a/requirements.txt b/requirements.txt
index b021ba18..5cde46bc 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,2 +1,2 @@
-torch~=1.0
-torchvision
\ No newline at end of file
+torch>=1.1.0
+torchvision>=0.3.0
diff --git a/sotabench.py b/sotabench.py
new file mode 100644
index 00000000..f6a1b24d
--- /dev/null
+++ b/sotabench.py
@@ -0,0 +1,214 @@
+from torchbench.image_classification import ImageNet
+from timm import create_model, list_models
+from timm.data import resolve_data_config, create_transform
+
+NUM_GPU = 1
+BATCH_SIZE = 256 * NUM_GPU
+
+
+def _attrib(paper_model_name='', paper_arxiv_id='', batch_size=BATCH_SIZE):
+    return dict(
+        paper_model_name=paper_model_name,
+        paper_arxiv_id=paper_arxiv_id,
+        batch_size=batch_size)
+
+model_map = dict(
+    #adv_inception_v3=_attrib(paper_model_name='Adversarial Inception V3', paper_arxiv_id=),
+    #densenet121=_attrib(paper_model_name=, paper_arxiv_id=), # same weights as torchvision
+    #densenet161=_attrib(paper_model_name=, paper_arxiv_id=), # same weights as torchvision
+    #densenet169=_attrib(paper_model_name=, paper_arxiv_id=), # same weights as torchvision
+    #densenet201=_attrib(paper_model_name=, paper_arxiv_id=), # same weights as torchvision
+    dpn68=_attrib(
+        paper_model_name='DPN-68', paper_arxiv_id='1707.01629'),
+    dpn68b=_attrib(
+        paper_model_name='DPN-68b', paper_arxiv_id='1707.01629'),
+    dpn92=_attrib(
+        paper_model_name='DPN-92', paper_arxiv_id='1707.01629'),
+    dpn98=_attrib(
+        paper_model_name='DPN-98', paper_arxiv_id='1707.01629'),
+    dpn107=_attrib(
+        paper_model_name='DPN-107', paper_arxiv_id='1707.01629'),
+    dpn131=_attrib(
+        paper_model_name='DPN-131', paper_arxiv_id='1707.01629'),
+    efficientnet_b0=_attrib(
+        paper_model_name='EfficientNet-B0', paper_arxiv_id='1905.11946'),
+    efficientnet_b1=_attrib(
+        paper_model_name='EfficientNet-B1', paper_arxiv_id='1905.11946'),
+    efficientnet_b2=_attrib(
+        paper_model_name='EfficientNet-B2', paper_arxiv_id='1905.11946'),
+    #ens_adv_inception_resnet_v2=_attrib(paper_model_name=, paper_arxiv_id=),
+    fbnetc_100=_attrib(
+        paper_model_name='FBNet-C', paper_arxiv_id='1812.03443'),
+    gluon_inception_v3=_attrib(
+        paper_model_name='Inception V3', paper_arxiv_id='1512.00567'),
+    gluon_resnet18_v1b=_attrib(
+        paper_model_name='ResNet-18', paper_arxiv_id='1812.01187'),
+    gluon_resnet34_v1b=_attrib(
+        paper_model_name='ResNet-34', paper_arxiv_id='1812.01187'),
+    gluon_resnet50_v1b=_attrib(
+        paper_model_name='ResNet-50', paper_arxiv_id='1812.01187'),
+    gluon_resnet50_v1c=_attrib(
+        paper_model_name='ResNet-50-C', paper_arxiv_id='1812.01187'),
+    gluon_resnet50_v1d=_attrib(
+        paper_model_name='ResNet-50-D', paper_arxiv_id='1812.01187'),
+    gluon_resnet50_v1s=_attrib(
+        paper_model_name='ResNet-50-S', paper_arxiv_id='1812.01187'),
+    gluon_resnet101_v1b=_attrib(
+        paper_model_name='ResNet-101', paper_arxiv_id='1812.01187'),
+    gluon_resnet101_v1c=_attrib(
+        paper_model_name='ResNet-101-C', paper_arxiv_id='1812.01187'),
+    gluon_resnet101_v1d=_attrib(
+        paper_model_name='ResNet-101-D', paper_arxiv_id='1812.01187'),
+    gluon_resnet101_v1s=_attrib(
+        paper_model_name='ResNet-101-S', paper_arxiv_id='1812.01187'),
+    gluon_resnet152_v1b=_attrib(
+        paper_model_name='ResNet-152', paper_arxiv_id='1812.01187'),
+    gluon_resnet152_v1c=_attrib(
+        paper_model_name='ResNet-152-C', paper_arxiv_id='1812.01187'),
+    gluon_resnet152_v1d=_attrib(
+        paper_model_name='ResNet-152-D', paper_arxiv_id='1812.01187'),
+    gluon_resnet152_v1s=_attrib(
+        paper_model_name='ResNet-152-S', paper_arxiv_id='1812.01187'),
+    gluon_resnext50_32x4d=_attrib(
+        paper_model_name='ResNeXt-50 32x4d', paper_arxiv_id='1812.01187'),
+    gluon_resnext101_32x4d=_attrib(
+        paper_model_name='ResNeXt-101 32x4d', paper_arxiv_id='1812.01187'),
+    gluon_resnext101_64x4d=_attrib(
+        paper_model_name='ResNeXt-101 64x4d', paper_arxiv_id='1812.01187'),
+    gluon_senet154=_attrib(
+        paper_model_name='SENet-154', paper_arxiv_id='1812.01187'),
+    gluon_seresnext50_32x4d=_attrib(
+        paper_model_name='SE-ResNeXt-50 32x4d', paper_arxiv_id='1812.01187'),
+    gluon_seresnext101_32x4d=_attrib(
+        paper_model_name='SE-ResNeXt-101 32x4d', paper_arxiv_id='1812.01187'),
+    gluon_seresnext101_64x4d=_attrib(
+        paper_model_name='SE-ResNeXt-101 64x4d', paper_arxiv_id='1812.01187'),
+    gluon_xception65=_attrib(
+        paper_model_name='Modified Aligned Xception', paper_arxiv_id='1802.02611', batch_size=BATCH_SIZE//2),
+    ig_resnext101_32x8d=_attrib(
+        paper_model_name='ResNeXt-101 32×8d', paper_arxiv_id='1805.00932'),
+    ig_resnext101_32x16d=_attrib(
+        paper_model_name='ResNeXt-101 32×16d', paper_arxiv_id='1805.00932'),
+    ig_resnext101_32x32d=_attrib(
+        paper_model_name='ResNeXt-101 32×32d', paper_arxiv_id='1805.00932', batch_size=BATCH_SIZE//2),
+    ig_resnext101_32x48d=_attrib(
+        paper_model_name='ResNeXt-101 32×48d', paper_arxiv_id='1805.00932', batch_size=BATCH_SIZE//4),
+    inception_resnet_v2=_attrib(
+        paper_model_name='Inception ResNet V2', paper_arxiv_id='1602.07261'),
+    #inception_v3=dict(paper_model_name='Inception V3', paper_arxiv_id=),  # same weights as torchvision
+    inception_v4=_attrib(
+        paper_model_name='Inception V4', paper_arxiv_id='1602.07261'),
+    mixnet_l=_attrib(
+        paper_model_name='MixNet-L', paper_arxiv_id='1907.09595'),
+    mixnet_m=_attrib(
+        paper_model_name='MixNet-M', paper_arxiv_id='1907.09595'),
+    mixnet_s=_attrib(
+        paper_model_name='MixNet-S', paper_arxiv_id='1907.09595'),
+    mnasnet_100=_attrib(
+        paper_model_name='MnasNet-B1', paper_arxiv_id='1807.11626'),
+    mobilenetv3_100=_attrib(
+        paper_model_name='MobileNet V3(1.0)', paper_arxiv_id='1905.02244'),
+    nasnetalarge=_attrib(
+        paper_model_name='NASNet-A Large', paper_arxiv_id='1707.07012', batch_size=BATCH_SIZE//4),
+    pnasnet5large=_attrib(
+        paper_model_name='PNASNet-5', paper_arxiv_id='1712.00559', batch_size=BATCH_SIZE//4),
+    resnet18=_attrib(
+        paper_model_name='ResNet-18', paper_arxiv_id='1812.01187'),
+    resnet26=_attrib(
+        paper_model_name='ResNet-26', paper_arxiv_id='1812.01187'),
+    resnet26d=_attrib(
+        paper_model_name='ResNet-26-D', paper_arxiv_id='1812.01187'),
+    resnet34=_attrib(
+        paper_model_name='ResNet-34', paper_arxiv_id='1812.01187'),
+    resnet50=_attrib(
+        paper_model_name='ResNet-50', paper_arxiv_id='1812.01187'),
+    #resnet101=_attrib(paper_model_name=, paper_arxiv_id=),  # same weights as torchvision
+    #resnet152=_attrib(paper_model_name=, paper_arxiv_id=),  # same weights as torchvision
+    resnext50_32x4d=_attrib(
+        paper_model_name='ResNeXt-50 32x4d', paper_arxiv_id='1812.01187'),
+    resnext50d_32x4d=_attrib(
+        paper_model_name='ResNeXt-50-D 32x4d', paper_arxiv_id='1812.01187'),
+    #resnext101_32x8d=_attrib(paper_model_name=, paper_arxiv_id=),  # same weights as torchvision
+    semnasnet_100=_attrib(
+        paper_model_name='MnasNet-A1', paper_arxiv_id='1807.11626'),
+    senet154=_attrib(
+        paper_model_name='SENet-154', paper_arxiv_id='1709.01507'),
+    seresnet18=_attrib(
+        paper_model_name='SE-ResNet-18', paper_arxiv_id='1709.01507'),
+    seresnet34=_attrib(
+        paper_model_name='SE-ResNet-34', paper_arxiv_id='1709.01507'),
+    seresnet50=_attrib(
+        paper_model_name='SE-ResNet-50', paper_arxiv_id='1709.01507'),
+    seresnet101=_attrib(
+        paper_model_name='SE-ResNet-101', paper_arxiv_id='1709.01507'),
+    seresnet152=_attrib(
+        paper_model_name='SE-ResNet-152', paper_arxiv_id='1709.01507'),
+    seresnext26_32x4d=_attrib(
+        paper_model_name='SE-ResNeXt-26 32x4d', paper_arxiv_id='1709.01507'),
+    seresnext50_32x4d=_attrib(
+        paper_model_name='SE-ResNeXt-50 32x4d', paper_arxiv_id='1709.01507'),
+    seresnext101_32x4d=_attrib(
+        paper_model_name='SE-ResNeXt-101 32x4d', paper_arxiv_id='1709.01507'),
+    spnasnet_100=_attrib(
+        paper_model_name='Single-Path NAS', paper_arxiv_id='1904.02877'),
+    tf_efficientnet_b0=_attrib(
+        paper_model_name='EfficientNet-B0', paper_arxiv_id='1905.11946'),
+    tf_efficientnet_b1=_attrib(
+        paper_model_name='EfficientNet-B1', paper_arxiv_id='1905.11946'),
+    tf_efficientnet_b2=_attrib(
+        paper_model_name='EfficientNet-B2', paper_arxiv_id='1905.11946'),
+    tf_efficientnet_b3=_attrib(
+        paper_model_name='EfficientNet-B3', paper_arxiv_id='1905.11946', batch_size=BATCH_SIZE//2),
+    tf_efficientnet_b4=_attrib(
+        paper_model_name='EfficientNet-B4', paper_arxiv_id='1905.11946', batch_size=BATCH_SIZE//2),
+    tf_efficientnet_b5=_attrib(
+        paper_model_name='EfficientNet-B5', paper_arxiv_id='1905.11946', batch_size=BATCH_SIZE//4),
+    tf_efficientnet_b6=_attrib(
+        paper_model_name='EfficientNet-B6', paper_arxiv_id='1905.11946', batch_size=BATCH_SIZE//8),
+    tf_efficientnet_b7=_attrib(
+        paper_model_name='EfficientNet-B7', paper_arxiv_id='1905.11946', batch_size=BATCH_SIZE//8),
+    tf_inception_v3=_attrib(
+        paper_model_name='Inception V3', paper_arxiv_id='1512.00567'),
+    tf_mixnet_l=_attrib(
+        paper_model_name='MixNet-L', paper_arxiv_id='1907.09595'),
+    tf_mixnet_m=_attrib(
+        paper_model_name='MixNet-M', paper_arxiv_id='1907.09595'),
+    tf_mixnet_s=_attrib(
+        paper_model_name='MixNet-S', paper_arxiv_id='1907.09595'),
+    #tv_resnet34=_attrib(paper_model_name=, paper_arxiv_id=), # same weights as torchvision
+    #tv_resnet50=_attrib(paper_model_name=, paper_arxiv_id=), # same weights as torchvision
+    #tv_resnext50_32x4d=_attrib(paper_model_name=, paper_arxiv_id=), # same weights as torchvision
+    #wide_resnet50_2=_attrib(paper_model_name=, paper_arxiv_id=),   # same weights as torchvision
+    #wide_resnet101_2=_attrib(paper_model_name=, paper_arxiv_id=),  # same weights as torchvision
+    xception=_attrib(
+        paper_model_name='Xception', paper_arxiv_id='1610.02357'),
+)
+
+model_names = list_models(pretrained=True)
+
+for model_name in model_names:
+    if model_name not in model_map:
+        print('Skipping %s' % model_name)
+        continue
+
+    # create model from name
+    model = create_model(model_name, pretrained=True)
+    param_count = sum([m.numel() for m in model.parameters()])
+    print('Model %s created, param count: %d' % (model_name, param_count))
+
+    # get appropriate transform for model's default pretrained config
+    data_config = resolve_data_config(dict(), model=model, verbose=True)
+    input_transform = create_transform(**data_config)
+
+    # Run the benchmark
+    ImageNet.benchmark(
+        model=model,
+        paper_model_name=model_map[model_name]['paper_model_name'],
+        paper_arxiv_id=model_map[model_name]['paper_arxiv_id'],
+        input_transform=input_transform,
+        batch_size=model_map[model_name]['batch_size'],
+        num_gpu=NUM_GPU,
+        #data_root=DATA_ROOT
+    )
+
+
diff --git a/timm/data/__init__.py b/timm/data/__init__.py
index 74872aac..61ad83ea 100644
--- a/timm/data/__init__.py
+++ b/timm/data/__init__.py
@@ -2,5 +2,5 @@ from .constants import *
 from .config import resolve_data_config
 from .dataset import Dataset, DatasetTar
 from .transforms import *
-from .loader import create_loader
+from .loader import create_loader, create_transform
 from .mixup import mixup_target, FastCollateMixup
diff --git a/timm/data/loader.py b/timm/data/loader.py
index 1198d5e5..db902a06 100644
--- a/timm/data/loader.py
+++ b/timm/data/loader.py
@@ -86,25 +86,17 @@ class PrefetchLoader:
             self.loader.collate_fn.mixup_enabled = x
 
 
-def create_loader(
-        dataset,
+def create_transform(
         input_size,
-        batch_size,
         is_training=False,
-        use_prefetcher=True,
-        rand_erase_prob=0.,
-        rand_erase_mode='const',
+        use_prefetcher=False,
         color_jitter=0.4,
         interpolation='bilinear',
         mean=IMAGENET_DEFAULT_MEAN,
         std=IMAGENET_DEFAULT_STD,
-        num_workers=1,
-        distributed=False,
         crop_pct=None,
-        collate_fn=None,
-        fp16=False,
-        tf_preprocessing=False,
-):
+        tf_preprocessing=False):
+
     if isinstance(input_size, tuple):
         img_size = input_size[-2:]
     else:
@@ -132,7 +124,39 @@ def create_loader(
                 std=std,
                 crop_pct=crop_pct)
 
-    dataset.transform = transform
+    return transform
+
+
+def create_loader(
+        dataset,
+        input_size,
+        batch_size,
+        is_training=False,
+        use_prefetcher=True,
+        rand_erase_prob=0.,
+        rand_erase_mode='const',
+        color_jitter=0.4,
+        interpolation='bilinear',
+        mean=IMAGENET_DEFAULT_MEAN,
+        std=IMAGENET_DEFAULT_STD,
+        num_workers=1,
+        distributed=False,
+        crop_pct=None,
+        collate_fn=None,
+        fp16=False,
+        tf_preprocessing=False,
+):
+    dataset.transform = create_transform(
+        input_size,
+        is_training=is_training,
+        use_prefetcher=use_prefetcher,
+        color_jitter=color_jitter,
+        interpolation=interpolation,
+        mean=mean,
+        std=std,
+        crop_pct=crop_pct,
+        tf_preprocessing=tf_preprocessing,
+    )
 
     sampler = None
     if distributed: