diff --git a/sotabench.py b/sotabench.py
index c4099505..e2a8fcb0 100644
--- a/sotabench.py
+++ b/sotabench.py
@@ -1,219 +1,153 @@
 from torchbench.image_classification import ImageNet
-from timm import create_model, list_models
+from timm import create_model
 from timm.data import resolve_data_config, create_transform
+from timm.models import TestTimePoolHead
 import os
 
 NUM_GPU = 1
 BATCH_SIZE = 256 * NUM_GPU
 
 
-def _attrib(paper_model_name='', paper_arxiv_id='', batch_size=BATCH_SIZE):
+def _entry(model_name, paper_model_name, paper_arxiv_id, batch_size=BATCH_SIZE, ttp=False, args=dict()):
     return dict(
+        model=model_name,
         paper_model_name=paper_model_name,
         paper_arxiv_id=paper_arxiv_id,
-        batch_size=batch_size)
+        batch_size=batch_size,
+        ttp=ttp,
+        args=args)
 
-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 32x8d', paper_arxiv_id='1805.00932'),
-    ig_resnext101_32x16d=_attrib(
-        paper_model_name='ResNeXt-101 32x16d', paper_arxiv_id='1805.00932'),
-    ig_resnext101_32x32d=_attrib(
-        paper_model_name='ResNeXt-101 32x32d', paper_arxiv_id='1805.00932', batch_size=BATCH_SIZE//2),
-    ig_resnext101_32x48d=_attrib(
-        paper_model_name='ResNeXt-101 32x48d', 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_efficientnet_es=_attrib(
-        paper_model_name='EfficientNet-EdgeTPU-S', paper_arxiv_id='1905.11946'),
-    tf_efficientnet_em=_attrib(
-        paper_model_name='EfficientNet-EdgeTPU-M', paper_arxiv_id='1905.11946'),
-    tf_efficientnet_el=_attrib(
-        paper_model_name='EfficientNet-EdgeTPU-L', paper_arxiv_id='1905.11946', batch_size=BATCH_SIZE//2),
-    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)
+model_list = [
+    #_entry('adv_inception_v3', 'Adversarial Inception V3', ),
+    #_entry('densenet121'), # same weights as torchvision
+    #_entry('densenet161'), # same weights as torchvision
+    #_entry('densenet169'), # same weights as torchvision
+    #_entry('densenet201'), # same weights as torchvision
+    _entry('dpn68', 'DPN-68 (224x224)', '1707.01629'),
+    _entry('dpn68b', 'DPN-68b (224x224)', '1707.01629'),
+    _entry('dpn92', 'DPN-92 (224x224)', '1707.01629'),
+    _entry('dpn98', 'DPN-98 (224x224)', '1707.01629'),
+    _entry('dpn107', 'DPN-107 (224x224)', '1707.01629'),
+    _entry('dpn131', 'DPN-131 (224x224)', '1707.01629'),
+    _entry('dpn68', 'DPN-68 (320x320, Mean-Max Pooling)', '1707.01629', ttp=True, args=dict(img_size=320)),
+    _entry('dpn68b', 'DPN-68b (320x320, Mean-Max Pooling)', '1707.01629', ttp=True, args=dict(img_size=320)),
+    _entry('dpn92', 'DPN-92 (320x320, Mean-Max Pooling)', '1707.01629',
+           ttp=True, args=dict(img_size=320), batch_size=BATCH_SIZE//2),
+    _entry('dpn98', 'DPN-98 (320x320, Mean-Max Pooling)', '1707.01629',
+           ttp=True, args=dict(img_size=320), batch_size=BATCH_SIZE//2),
+    _entry('dpn107', 'DPN-107 (320x320, Mean-Max Pooling)', '1707.01629',
+           ttp=True, args=dict(img_size=320), batch_size=BATCH_SIZE//4),
+    _entry('dpn131', 'DPN-131 (320x320, Mean-Max Pooling)', '1707.01629',
+           ttp=True, args=dict(img_size=320), batch_size=BATCH_SIZE//4),
+    _entry('efficientnet_b0', 'EfficientNet-B0', '1905.11946'),
+    _entry('efficientnet_b1', 'EfficientNet-B1', '1905.11946'),
+    _entry('efficientnet_b2', 'EfficientNet-B2', '1905.11946'),
+    #_entry('ens_adv_inception_resnet_v2', 'Ensemble Adversarial Inception V3'),
+    _entry('fbnetc_100', 'FBNet-C', '1812.03443'),
+    _entry('gluon_inception_v3', 'Inception V3', '1512.00567'),
+    _entry('gluon_resnet18_v1b', 'ResNet-18', '1812.01187'),
+    _entry('gluon_resnet34_v1b', 'ResNet-34', '1812.01187'),
+    _entry('gluon_resnet50_v1b', 'ResNet-50', '1812.01187'),
+    _entry('gluon_resnet50_v1c', 'ResNet-50-C', '1812.01187'),
+    _entry('gluon_resnet50_v1d', 'ResNet-50-D', '1812.01187'),
+    _entry('gluon_resnet50_v1s', 'ResNet-50-S', '1812.01187'),
+    _entry('gluon_resnet101_v1b', 'ResNet-101', '1812.01187'),
+    _entry('gluon_resnet101_v1c', 'ResNet-101-C', '1812.01187'),
+    _entry('gluon_resnet101_v1d', 'ResNet-101-D', '1812.01187'),
+    _entry('gluon_resnet101_v1s', 'ResNet-101-S', '1812.01187'),
+    _entry('gluon_resnet152_v1b', 'ResNet-152', '1812.01187'),
+    _entry('gluon_resnet152_v1c', 'ResNet-152-C', '1812.01187'),
+    _entry('gluon_resnet152_v1d', 'ResNet-152-D', '1812.01187'),
+    _entry('gluon_resnet152_v1s', 'ResNet-152-S', '1812.01187'),
+    _entry('gluon_resnext50_32x4d', 'ResNeXt-50 32x4d', '1812.01187'),
+    _entry('gluon_resnext101_32x4d', 'ResNeXt-101 32x4d', '1812.01187'),
+    _entry('gluon_resnext101_64x4d', 'ResNeXt-101 64x4d', '1812.01187'),
+    _entry('gluon_senet154', 'SENet-154', '1812.01187'),
+    _entry('gluon_seresnext50_32x4d', 'SE-ResNeXt-50 32x4d', '1812.01187'),
+    _entry('gluon_seresnext101_32x4d', 'SE-ResNeXt-101 32x4d', '1812.01187'),
+    _entry('gluon_seresnext101_64x4d', 'SE-ResNeXt-101 64x4d', '1812.01187'),
+    _entry('gluon_xception65', 'Modified Aligned Xception', '1802.02611', batch_size=BATCH_SIZE//2),
+    _entry('ig_resnext101_32x8d', 'ResNeXt-101 32x8d', '1805.00932'),
+    _entry('ig_resnext101_32x16d', 'ResNeXt-101 32x16d', '1805.00932'),
+    _entry('ig_resnext101_32x32d', 'ResNeXt-101 32x32d', '1805.00932', batch_size=BATCH_SIZE//2),
+    _entry('ig_resnext101_32x48d', 'ResNeXt-101 32x48d', '1805.00932', batch_size=BATCH_SIZE//4),
+    _entry('inception_resnet_v2', 'Inception ResNet V2', '1602.07261'),
+    #_entry('inception_v3', paper_model_name='Inception V3', ),  # same weights as torchvision
+    _entry('inception_v4', 'Inception V4', '1602.07261'),
+    _entry('mixnet_l', 'MixNet-L', '1907.09595'),
+    _entry('mixnet_m', 'MixNet-M', '1907.09595'),
+    _entry('mixnet_s', 'MixNet-S', '1907.09595'),
+    _entry('mnasnet_100', 'MnasNet-B1', '1807.11626'),
+    _entry('mobilenetv3_100', 'MobileNet V3(1.0)', '1905.02244'),
+    _entry('nasnetalarge', 'NASNet-A Large', '1707.07012', batch_size=BATCH_SIZE//4),
+    _entry('pnasnet5large', 'PNASNet-5', '1712.00559', batch_size=BATCH_SIZE//4),
+    _entry('resnet18', 'ResNet-18', '1812.01187'),
+    _entry('resnet26', 'ResNet-26', '1812.01187'),
+    _entry('resnet26d', 'ResNet-26-D', '1812.01187'),
+    _entry('resnet34', 'ResNet-34', '1812.01187'),
+    _entry('resnet50', 'ResNet-50', '1812.01187'),
+    #_entry('resnet101', , ),  # same weights as torchvision
+    #_entry('resnet152', , ),  # same weights as torchvision
+    _entry('resnext50_32x4d', 'ResNeXt-50 32x4d', '1812.01187'),
+    _entry('resnext50d_32x4d', 'ResNeXt-50-D 32x4d', '1812.01187'),
+    #_entry('resnext101_32x8d', ),  # same weights as torchvision
+    _entry('semnasnet_100', 'MnasNet-A1', '1807.11626'),
+    _entry('senet154', 'SENet-154', '1709.01507'),
+    _entry('seresnet18', 'SE-ResNet-18', '1709.01507'),
+    _entry('seresnet34', 'SE-ResNet-34', '1709.01507'),
+    _entry('seresnet50', 'SE-ResNet-50', '1709.01507'),
+    _entry('seresnet101', 'SE-ResNet-101', '1709.01507'),
+    _entry('seresnet152', 'SE-ResNet-152', '1709.01507'),
+    _entry('seresnext26_32x4d', 'SE-ResNeXt-26 32x4d', '1709.01507'),
+    _entry('seresnext50_32x4d', 'SE-ResNeXt-50 32x4d', '1709.01507'),
+    _entry('seresnext101_32x4d', 'SE-ResNeXt-101 32x4d', '1709.01507'),
+    _entry('spnasnet_100', 'Single-Path NAS', '1904.02877'),
+    _entry('tf_efficientnet_b0', 'EfficientNet-B0 (AutoAugment)', '1905.11946'),
+    _entry('tf_efficientnet_b1', 'EfficientNet-B1 (AutoAugment)', '1905.11946'),
+    _entry('tf_efficientnet_b2', 'EfficientNet-B2 (AutoAugment)', '1905.11946'),
+    _entry('tf_efficientnet_b3', 'EfficientNet-B3 (AutoAugment)', '1905.11946', batch_size=BATCH_SIZE//2),
+    _entry('tf_efficientnet_b4', 'EfficientNet-B4 (AutoAugment)', '1905.11946', batch_size=BATCH_SIZE//2),
+    _entry('tf_efficientnet_b5', 'EfficientNet-B5 (AutoAugment)', '1905.11946', batch_size=BATCH_SIZE//4),
+    _entry('tf_efficientnet_b6', 'EfficientNet-B6 (AutoAugment)', '1905.11946', batch_size=BATCH_SIZE//8),
+    _entry('tf_efficientnet_b7', 'EfficientNet-B7 (AutoAugment)', '1905.11946', batch_size=BATCH_SIZE//8),
+    _entry('tf_efficientnet_es', 'EfficientNet-EdgeTPU-S', '1905.11946'),
+    _entry('tf_efficientnet_em', 'EfficientNet-EdgeTPU-M', '1905.11946'),
+    _entry('tf_efficientnet_el', 'EfficientNet-EdgeTPU-L', '1905.11946', batch_size=BATCH_SIZE//2),
+    _entry('tf_inception_v3', 'Inception V3', '1512.00567'),
+    _entry('tf_mixnet_l', 'MixNet-L', '1907.09595'),
+    _entry('tf_mixnet_m', 'MixNet-M', '1907.09595'),
+    _entry('tf_mixnet_s', 'MixNet-S', '1907.09595'),
+    #_entry('tv_resnet34', , ), # same weights as torchvision
+    #_entry('tv_resnet50', , ), # same weights as torchvision
+    #_entry('tv_resnext50_32x4d', , ), # same weights as torchvision
+    #_entry('wide_resnet50_2' , ),   # same weights as torchvision
+    #_entry('wide_resnet101_2', , ),  # same weights as torchvision
+    _entry('xception', 'Xception', '1610.02357'),
+]
 
-for model_name in model_names:
-    if model_name not in model_map:
-        print('Skipping %s' % model_name)
-        continue
 
+for m in model_list:
+    model_name = m['model']
     # 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))
+    print('Model %s, %s created. Param count: %d' % (model_name, m['paper_model_name'], param_count))
 
     # get appropriate transform for model's default pretrained config
-    data_config = resolve_data_config(dict(), model=model, verbose=True)
+    data_config = resolve_data_config(m['args'], model=model, verbose=True)
+    if m['ttp']:
+        model = TestTimePoolHead(model, model.default_cfg['pool_size'])
+        data_config['crop_pct'] = 1.0
     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'],
+        paper_model_name=m['paper_model_name'],
+        paper_arxiv_id=m['paper_arxiv_id'],
         input_transform=input_transform,
-        batch_size=model_map[model_name]['batch_size'],
+        batch_size=m['batch_size'],
         num_gpu=NUM_GPU,
         data_root=os.environ.get('IMAGENET_DIR', './imagenet')
     )
diff --git a/timm/models/helpers.py b/timm/models/helpers.py
index a37f5062..aba0a864 100644
--- a/timm/models/helpers.py
+++ b/timm/models/helpers.py
@@ -60,7 +60,7 @@ def load_pretrained(model, default_cfg, num_classes=1000, in_chans=3, filter_fn=
         logging.warning("Pretrained model URL is invalid, using random initialization.")
         return
 
-    state_dict = model_zoo.load_url(default_cfg['url'])
+    state_dict = model_zoo.load_url(default_cfg['url'], progress=False)
 
     if in_chans == 1:
         conv1_name = default_cfg['first_conv']