diff --git a/main_dino.py b/main_dino.py
index cade9873d..b82b8c170 100644
--- a/main_dino.py
+++ b/main_dino.py
@@ -1,11 +1,11 @@
 # Copyright (c) Facebook, Inc. and its affiliates.
-# 
+#
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
-# 
+#
 #     http://www.apache.org/licenses/LICENSE-2.0
-# 
+#
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
@@ -42,90 +42,95 @@ def get_args_parser():
     parser = argparse.ArgumentParser('DINO', add_help=False)
 
     # Model parameters
-    parser.add_argument('--arch', default='vit_small', type=str,
+    group = parser.add_argument_group(title="Model parameters", description="")
+    group.add_argument('--arch', default='vit_small', type=str,
         choices=['vit_tiny', 'vit_small', 'vit_base', 'xcit', 'deit_tiny', 'deit_small'] \
                 + torchvision_archs + torch.hub.list("facebookresearch/xcit:main"),
         help="""Name of architecture to train. For quick experiments with ViTs,
         we recommend using vit_tiny or vit_small.""")
-    parser.add_argument('--patch_size', default=16, type=int, help="""Size in pixels
+    group.add_argument('--patch_size', default=16, type=int, help="""Size in pixels
         of input square patches - default 16 (for 16x16 patches). Using smaller
         values leads to better performance but requires more memory. Applies only
         for ViTs (vit_tiny, vit_small and vit_base). If <16, we recommend disabling
         mixed precision training (--use_fp16 false) to avoid unstabilities.""")
-    parser.add_argument('--out_dim', default=65536, type=int, help="""Dimensionality of
+    group.add_argument('--out_dim', default=65536, type=int, help="""Dimensionality of
         the DINO head output. For complex and large datasets large values (like 65k) work well.""")
-    parser.add_argument('--norm_last_layer', default=True, type=utils.bool_flag,
+    group.add_argument('--norm_last_layer', default=True, type=utils.bool_flag,
         help="""Whether or not to weight normalize the last layer of the DINO head.
         Not normalizing leads to better performance but can make the training unstable.
         In our experiments, we typically set this paramater to False with vit_small and True with vit_base.""")
-    parser.add_argument('--momentum_teacher', default=0.996, type=float, help="""Base EMA
+    group.add_argument('--momentum_teacher', default=0.996, type=float, help="""Base EMA
         parameter for teacher update. The value is increased to 1 during training with cosine schedule.
         We recommend setting a higher value with small batches: for example use 0.9995 with batch size of 256.""")
-    parser.add_argument('--use_bn_in_head', default=False, type=utils.bool_flag,
+    group.add_argument('--use_bn_in_head', default=False, type=utils.bool_flag,
         help="Whether to use batch normalizations in projection head (Default: False)")
 
     # Temperature teacher parameters
-    parser.add_argument('--warmup_teacher_temp', default=0.04, type=float,
+    group = parser.add_argument_group(title="Temperature and teacher parameters", description="")
+    group.add_argument('--warmup_teacher_temp', default=0.04, type=float,
         help="""Initial value for the teacher temperature: 0.04 works well in most cases.
         Try decreasing it if the training loss does not decrease.""")
-    parser.add_argument('--teacher_temp', default=0.04, type=float, help="""Final value (after linear warmup)
+    group.add_argument('--teacher_temp', default=0.04, type=float, help="""Final value (after linear warmup)
         of the teacher temperature. For most experiments, anything above 0.07 is unstable. We recommend
         starting with the default value of 0.04 and increase this slightly if needed.""")
-    parser.add_argument('--warmup_teacher_temp_epochs', default=0, type=int,
+    group.add_argument('--warmup_teacher_temp_epochs', default=0, type=int,
         help='Number of warmup epochs for the teacher temperature (Default: 30).')
 
     # Training/Optimization parameters
-    parser.add_argument('--use_fp16', type=utils.bool_flag, default=True, help="""Whether or not
+    group = parser.add_argument_group(title="Training/optimization parameters", description="")
+    group.add_argument('--use_fp16', type=utils.bool_flag, default=True, help="""Whether or not
         to use half precision for training. Improves training time and memory requirements,
         but can provoke instability and slight decay of performance. We recommend disabling
         mixed precision if the loss is unstable, if reducing the patch size or if training with bigger ViTs.""")
-    parser.add_argument('--weight_decay', type=float, default=0.04, help="""Initial value of the
+    group.add_argument('--weight_decay', type=float, default=0.04, help="""Initial value of the
         weight decay. With ViT, a smaller value at the beginning of training works well.""")
-    parser.add_argument('--weight_decay_end', type=float, default=0.4, help="""Final value of the
+    group.add_argument('--weight_decay_end', type=float, default=0.4, help="""Final value of the
         weight decay. We use a cosine schedule for WD and using a larger decay by
         the end of training improves performance for ViTs.""")
-    parser.add_argument('--clip_grad', type=float, default=3.0, help="""Maximal parameter
+    group.add_argument('--clip_grad', type=float, default=3.0, help="""Maximal parameter
         gradient norm if using gradient clipping. Clipping with norm .3 ~ 1.0 can
         help optimization for larger ViT architectures. 0 for disabling.""")
-    parser.add_argument('--batch_size_per_gpu', default=64, type=int,
+    group.add_argument('--batch_size_per_gpu', default=64, type=int,
         help='Per-GPU batch-size : number of distinct images loaded on one GPU.')
-    parser.add_argument('--epochs', default=100, type=int, help='Number of epochs of training.')
-    parser.add_argument('--freeze_last_layer', default=1, type=int, help="""Number of epochs
+    group.add_argument('--epochs', default=100, type=int, help='Number of epochs of training.')
+    group.add_argument('--freeze_last_layer', default=1, type=int, help="""Number of epochs
         during which we keep the output layer fixed. Typically doing so during
         the first epoch helps training. Try increasing this value if the loss does not decrease.""")
-    parser.add_argument("--lr", default=0.0005, type=float, help="""Learning rate at the end of
+    group.add_argument("--lr", default=0.0005, type=float, help="""Learning rate at the end of
         linear warmup (highest LR used during training). The learning rate is linearly scaled
         with the batch size, and specified here for a reference batch size of 256.""")
-    parser.add_argument("--warmup_epochs", default=10, type=int,
+    group.add_argument("--warmup_epochs", default=10, type=int,
         help="Number of epochs for the linear learning-rate warm up.")
-    parser.add_argument('--min_lr', type=float, default=1e-6, help="""Target LR at the
+    group.add_argument('--min_lr', type=float, default=1e-6, help="""Target LR at the
         end of optimization. We use a cosine LR schedule with linear warmup.""")
-    parser.add_argument('--optimizer', default='adamw', type=str,
+    group.add_argument('--optimizer', default='adamw', type=str,
         choices=['adamw', 'sgd', 'lars'], help="""Type of optimizer. We recommend using adamw with ViTs.""")
-    parser.add_argument('--drop_path_rate', type=float, default=0.1, help="stochastic depth rate")
+    group.add_argument('--drop_path_rate', type=float, default=0.1, help="stochastic depth rate")
 
     # Multi-crop parameters
-    parser.add_argument('--global_crops_scale', type=float, nargs='+', default=(0.4, 1.),
+    group = parser.add_argument_group(title="Multi-crop parameters", description="")
+    group.add_argument('--global_crops_scale', type=float, nargs='+', default=(0.4, 1.),
         help="""Scale range of the cropped image before resizing, relatively to the origin image.
         Used for large global view cropping. When disabling multi-crop (--local_crops_number 0), we
         recommand using a wider range of scale ("--global_crops_scale 0.14 1." for example)""")
-    parser.add_argument('--local_crops_number', type=int, default=8, help="""Number of small
+    group.add_argument('--local_crops_number', type=int, default=8, help="""Number of small
         local views to generate. Set this parameter to 0 to disable multi-crop training.
         When disabling multi-crop we recommend to use "--global_crops_scale 0.14 1." """)
-    parser.add_argument('--local_crops_scale', type=float, nargs='+', default=(0.05, 0.4),
+    group.add_argument('--local_crops_scale', type=float, nargs='+', default=(0.05, 0.4),
         help="""Scale range of the cropped image before resizing, relatively to the origin image.
         Used for small local view cropping of multi-crop.""")
 
     # Misc
-    parser.add_argument('--data_path', default='/path/to/imagenet/train/', type=str,
+    group = parser.add_argument_group(title="Miscellaneous parameters", description="")
+    group.add_argument('--data_path', default='/path/to/imagenet/train/', type=str,
         help='Please specify path to the ImageNet training data.')
-    parser.add_argument('--output_dir', default=".", type=str, help='Path to save logs and checkpoints.')
-    parser.add_argument('--saveckp_freq', default=20, type=int, help='Save checkpoint every x epochs.')
-    parser.add_argument('--seed', default=0, type=int, help='Random seed.')
-    parser.add_argument('--num_workers', default=10, type=int, help='Number of data loading workers per GPU.')
-    parser.add_argument("--dist_url", default="env://", type=str, help="""url used to set up
+    group.add_argument('--output_dir', default=".", type=str, help='Path to save logs and checkpoints.')
+    group.add_argument('--saveckp_freq', default=20, type=int, help='Save checkpoint every x epochs.')
+    group.add_argument('--seed', default=0, type=int, help='Random seed.')
+    group.add_argument('--num_workers', default=10, type=int, help='Number of data loading workers per GPU.')
+    group.add_argument("--dist_url", default="env://", type=str, help="""url used to set up
         distributed training; see https://pytorch.org/docs/stable/distributed.html""")
-    parser.add_argument("--local_rank", default=0, type=int, help="Please ignore and do not set this argument.")
+    group.add_argument("--local_rank", default=0, type=int, help="Please ignore and do not set this argument.")
     return parser