diff --git a/sotabench.py b/sotabench.py
index 7a67d0c..d623e47 100644
--- a/sotabench.py
+++ b/sotabench.py
@@ -23,77 +23,6 @@
 
 # parsing args
 args = parser.parse_args()
-# TResNet-M
-model_path = './tresnet_m.pth'
-model = create_model(args)
-state = torch.load(model_path, map_location='cpu')['model']
-model.load_state_dict(state, strict=True)
-model = InplacABN_to_ABN(model)
-model = fuse_bn_recursively(model)
-model = model.cuda()
-model.eval()
-
-val_bs = args.batch_size
-val_tfms = transforms.Compose(
-    [transforms.Resize(int(args.input_size / args.val_zoom_factor)),
-     transforms.CenterCrop(args.input_size)])
-val_tfms.transforms.append(transforms.ToTensor())
-
-print('Benchmarking TResNet-M')
-
-# Run the benchmark
-ImageNet.benchmark(
-    model=model,
-    paper_model_name='TResNet-M',
-    paper_arxiv_id='2003.13630',
-    input_transform=val_tfms,
-    batch_size=432,
-    num_workers=args.num_workers,
-    num_gpu=1,
-    pin_memory=True,
-    paper_results={'Top 1 Accuracy': 0.807, 'Top 5 Accuracy': 0.948},
-    model_description="Official weights from the author's of the paper."
-)
-
-del model
-gc.collect()
-torch.cuda.empty_cache()
-
-# TResNet-M-288
-model_path = './tresnet_m.pth'
-model = create_model(args)
-state = torch.load(model_path, map_location='cpu')['model']
-model.load_state_dict(state, strict=True)
-model = InplacABN_to_ABN(model)
-model = fuse_bn_recursively(model)
-model = model.cuda()
-model.eval()
-
-val_bs = args.batch_size
-val_tfms = transforms.Compose(
-    [transforms.Resize(int(288 / args.val_zoom_factor)),
-     transforms.CenterCrop(288)])
-val_tfms.transforms.append(transforms.ToTensor())
-
-print('Benchmarking TResNet-M-288')
-
-# Run the benchmark
-ImageNet.benchmark(
-    model=model,
-    paper_model_name='TResNet-M (input=288)',
-    paper_arxiv_id='2003.13630',
-    input_transform=val_tfms,
-    batch_size=352,
-    num_workers=args.num_workers,
-    num_gpu=1,
-    pin_memory=True,
-    paper_results={'Top 1 Accuracy': 0.807, 'Top 5 Accuracy': 0.948},
-    model_description="Official weights from the author's of the paper."
-)
-
-del model
-gc.collect()
-torch.cuda.empty_cache()
 
 # MTResNet 224-Mean-Max-vazoom
 
@@ -133,305 +62,3 @@
 del model
 gc.collect()
 torch.cuda.empty_cache()
-
-# MTResNet 288-Mean-Max
-
-val_bs = args.batch_size
-val_tfms = transforms.Compose(
-    [transforms.Resize(288),
-     transforms.CenterCrop(288)])
-val_tfms.transforms.append(transforms.ToTensor())
-
-model_path = './tresnet_m.pth'
-model = create_model(args)
-state = torch.load(model_path, map_location='cpu')['model']
-model.load_state_dict(state, strict=True)
-
-model = TestTimePoolHead(model)
-
-model = InplacABN_to_ABN(model)
-model = fuse_bn_recursively(model)
-model = model.cuda()
-model.eval()
-print('Benchmarking TResNet-M (288-Mean-Max)')
-
-# Run the benchmark
-ImageNet.benchmark(
-    model=model,
-    paper_model_name='TResNet-M (288-Mean-Max)',
-    paper_arxiv_id='2003.13630',
-    input_transform=val_tfms,
-    batch_size=432,
-    num_workers=args.num_workers,
-    num_gpu=1,
-    pin_memory=True,
-    paper_results={'Top 1 Accuracy': 0.807, 'Top 5 Accuracy': 0.948},
-    model_description="Official weights from the author's of the paper."
-)
-
-del model
-gc.collect()
-torch.cuda.empty_cache()
-
-# TResNet-L
-args.model_name = 'tresnet_l'
-model_path = './tresnet_l.pth'
-model = create_model(args)
-state = torch.load(model_path, map_location='cpu')['model']
-model.load_state_dict(state, strict=True)
-model = InplacABN_to_ABN(model)
-model = fuse_bn_recursively(model)
-model = model.cuda()
-model.eval()
-
-val_bs = args.batch_size
-val_tfms = transforms.Compose(
-    [transforms.Resize(int(args.input_size / args.val_zoom_factor)),
-     transforms.CenterCrop(args.input_size)])
-val_tfms.transforms.append(transforms.ToTensor())
-
-print('Benchmarking TResNet-L')
-
-# Run the benchmark
-ImageNet.benchmark(
-    model=model,
-    paper_model_name='TResNet-L',
-    paper_arxiv_id='2003.13630',
-    input_transform=val_tfms,
-    batch_size=250,
-    num_workers=args.num_workers,
-    num_gpu=1,
-    pin_memory=True,
-    paper_results={'Top 1 Accuracy': 0.814, 'Top 5 Accuracy': 0.956},
-    model_description="Official weights from the author's of the paper."
-)
-
-del model
-gc.collect()
-torch.cuda.empty_cache()
-
-# LTResNet 288-Mean-Max
-
-val_bs = args.batch_size
-val_tfms = transforms.Compose(
-    [transforms.Resize(288),
-     transforms.CenterCrop(288)])
-val_tfms.transforms.append(transforms.ToTensor())
-
-model_path = './tresnet_l.pth'
-model = create_model(args)
-state = torch.load(model_path, map_location='cpu')['model']
-model.load_state_dict(state, strict=True)
-
-model = TestTimePoolHead(model)
-
-model = InplacABN_to_ABN(model)
-model = fuse_bn_recursively(model)
-model = model.cuda()
-model.eval()
-print('Benchmarking TResNet-L (288-Mean-Max)')
-
-# Run the benchmark
-ImageNet.benchmark(
-    model=model,
-    paper_model_name='TResNet-L (288-Mean-Max)',
-    paper_arxiv_id='2003.13630',
-    input_transform=val_tfms,
-    batch_size=250,
-    num_workers=args.num_workers,
-    num_gpu=1,
-    pin_memory=True,
-    paper_results={'Top 1 Accuracy': 0.814, 'Top 5 Accuracy': 0.956},
-    model_description="Official weights from the author's of the paper."
-)
-
-del model
-gc.collect()
-torch.cuda.empty_cache()
-
-# TResNet-XL
-args.model_name = 'tresnet_xl'
-model_path = './tresnet_xl.pth'
-model = create_model(args)
-state = torch.load(model_path, map_location='cpu')['model']
-model.load_state_dict(state, strict=True)
-model = InplacABN_to_ABN(model)
-model = fuse_bn_recursively(model)
-model = model.cuda()
-model.eval()
-
-val_bs = args.batch_size
-val_tfms = transforms.Compose(
-    [transforms.Resize(int(args.input_size / args.val_zoom_factor)),
-     transforms.CenterCrop(args.input_size)])
-val_tfms.transforms.append(transforms.ToTensor())
-
-print('Benchmarking TResNet-XL')
-
-# Run the benchmark
-ImageNet.benchmark(
-    model=model,
-    paper_model_name='TResNet-XL',
-    paper_arxiv_id='2003.13630',
-    input_transform=val_tfms,
-    batch_size=250,
-    num_workers=args.num_workers,
-    num_gpu=1,
-    pin_memory=True,
-    paper_results={'Top 1 Accuracy': 0.820, 'Top 5 Accuracy': 0.959},
-    model_description="Official weights from the author's of the paper."
-)
-
-del model
-gc.collect()
-torch.cuda.empty_cache()
-
-# XLTResNet 288-Mean-Max
-
-val_bs = args.batch_size
-val_tfms = transforms.Compose(
-    [transforms.Resize(288),
-     transforms.CenterCrop(288)])
-val_tfms.transforms.append(transforms.ToTensor())
-
-model_path = './tresnet_xl.pth'
-model = create_model(args)
-state = torch.load(model_path, map_location='cpu')['model']
-model.load_state_dict(state, strict=True)
-
-model = TestTimePoolHead(model)
-
-model = InplacABN_to_ABN(model)
-model = fuse_bn_recursively(model)
-model = model.cuda()
-model.eval()
-print('Benchmarking TResNet-XL (288-Mean-Max)')
-
-# Run the benchmark
-ImageNet.benchmark(
-    model=model,
-    paper_model_name='TResNet-XL (288-Mean-Max)',
-    paper_arxiv_id='2003.13630',
-    input_transform=val_tfms,
-    batch_size=212,
-    num_workers=args.num_workers,
-    num_gpu=1,
-    pin_memory=True,
-    paper_results={'Top 1 Accuracy': 0.820, 'Top 5 Accuracy': 0.959},
-    model_description="Official weights from the author's of the paper."
-)
-
-del model
-gc.collect()
-torch.cuda.empty_cache()
-
-# TResNet-M-448
-args.model_name = 'tresnet_m'
-model_path = './tresnet_m_448.pth'
-args.input_size = 448
-model = create_model(args)
-state = torch.load(model_path, map_location='cpu')['model']
-model.load_state_dict(state, strict=True)
-model = InplacABN_to_ABN(model)
-model = fuse_bn_recursively(model)
-model = model.cuda()
-model.eval()
-
-val_bs = args.batch_size
-val_tfms = transforms.Compose(
-    [transforms.Resize((args.input_size, args.input_size))])
-val_tfms.transforms.append(transforms.ToTensor())
-
-print('Benchmarking TResNet-M 448')
-
-# Run the benchmark
-ImageNet.benchmark(
-    model=model,
-    paper_model_name='TResNet-M (input=448)',
-    paper_arxiv_id='2003.13630',
-    input_transform=val_tfms,
-    batch_size=125,
-    num_workers=args.num_workers,
-    num_gpu=1,
-    pin_memory=True,
-    paper_results={'Top 1 Accuracy': 0.832},
-    model_description="Official weights from the author's of the paper."
-)
-
-del model
-gc.collect()
-torch.cuda.empty_cache()
-
-# TResNet-L-448
-args.model_name = 'tresnet_l'
-model_path = './tresnet_l_448.pth'
-args.input_size = 448
-model = create_model(args)
-state = torch.load(model_path, map_location='cpu')['model']
-model.load_state_dict(state, strict=True)
-model = InplacABN_to_ABN(model)
-model = fuse_bn_recursively(model)
-model = model.cuda()
-model.eval()
-
-val_bs = args.batch_size
-val_tfms = transforms.Compose(
-    [transforms.Resize((args.input_size, args.input_size))])
-val_tfms.transforms.append(transforms.ToTensor())
-
-print('Benchmarking TResNet-L 448')
-
-# Run the benchmark
-ImageNet.benchmark(
-    model=model,
-    paper_model_name='TResNet-L (input=448)',
-    paper_arxiv_id='2003.13630',
-    input_transform=val_tfms,
-    batch_size=64,
-    num_workers=args.num_workers,
-    num_gpu=1,
-    pin_memory=True,
-    paper_results={'Top 1 Accuracy': 0.838},
-    model_description="Official weights from the author's of the paper."
-)
-
-del model
-gc.collect()
-torch.cuda.empty_cache()
-
-# TResNet-XL-448
-args.model_name = 'tresnet_xl'
-model_path = './tresnet_xl_448.pth'
-args.input_size = 448
-model = create_model(args)
-state = torch.load(model_path, map_location='cpu')['model']
-model.load_state_dict(state, strict=True)
-model = InplacABN_to_ABN(model)
-model = fuse_bn_recursively(model)
-model = model.cuda()
-model.eval()
-
-val_bs = args.batch_size
-val_tfms = transforms.Compose(
-    [transforms.Resize((args.input_size, args.input_size))])
-val_tfms.transforms.append(transforms.ToTensor())
-
-print('Benchmarking TResNet-XL 448')
-
-# Run the benchmark
-ImageNet.benchmark(
-    model=model,
-    paper_model_name='TResNet-XL (input=448)',
-    paper_arxiv_id='2003.13630',
-    input_transform=val_tfms,
-    batch_size=32,
-    num_workers=args.num_workers,
-    num_gpu=1,
-    pin_memory=True,
-    paper_results={'Top 1 Accuracy': 0.843},
-    model_description="Official weights from the author's of the paper."
-)
-
-del model
-gc.collect()
-torch.cuda.empty_cache()