diff --git a/references/detection/train_pytorch.py b/references/detection/train_pytorch.py
index 2c4208cee5..7569eb202e 100644
--- a/references/detection/train_pytorch.py
+++ b/references/detection/train_pytorch.py
@@ -15,6 +15,7 @@
 import torch
 from torchvision.transforms import Compose, Lambda, Normalize, ColorJitter
 from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
+from torch.optim.lr_scheduler import OneCycleLR, CosineAnnealingLR
 from contiguous_params import ContiguousParams
 import wandb
 
@@ -26,7 +27,7 @@
 from utils import plot_samples
 
 
-def fit_one_epoch(model, train_loader, batch_transforms, optimizer, mb):
+def fit_one_epoch(model, train_loader, batch_transforms, optimizer, scheduler, mb):
     model.train()
     train_iter = iter(train_loader)
     # Iterate over the batches of the dataset
@@ -42,6 +43,7 @@ def fit_one_epoch(model, train_loader, batch_transforms, optimizer, mb):
         optimizer.zero_grad()
         train_loss.backward()
         optimizer.step()
+        scheduler.step()
 
         mb.child.comment = f'Training loss: {train_loss.item():.6}'
 
@@ -172,6 +174,11 @@ def main(args):
     model_params = ContiguousParams([p for p in model.parameters() if p.requires_grad]).contiguous()
     optimizer = torch.optim.Adam(model_params, args.lr,
                                  betas=(0.95, 0.99), eps=1e-6, weight_decay=args.weight_decay)
+    # Scheduler
+    if args.sched == 'cosine':
+        scheduler = CosineAnnealingLR(optimizer, args.epochs * len(train_loader), eta_min=args.lr / 25e4)
+    elif args.sched == 'onecycle':
+        scheduler = OneCycleLR(optimizer, args.lr, args.epochs * len(train_loader))
 
     # Training monitoring
     current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
@@ -201,7 +208,7 @@ def main(args):
     # Training loop
     mb = master_bar(range(args.epochs))
     for epoch in mb:
-        fit_one_epoch(model, train_loader, batch_transforms, optimizer, mb)
+        fit_one_epoch(model, train_loader, batch_transforms, optimizer, scheduler, mb)
         # Validation loop at the end of each epoch
         val_loss, recall, precision, mean_iou = evaluate(model, val_loader, batch_transforms, val_metric)
         if val_loss < min_loss:
@@ -253,6 +260,7 @@ def parse_args():
                         help='Load pretrained parameters before starting the training')
     parser.add_argument('--rotation', dest='rotation', action='store_true',
                         help='train with rotated bbox')
+    parser.add_argument('--sched', type=str, default='cosine', help='scheduler to use')
     args = parser.parse_args()
 
     return args
diff --git a/references/recognition/train_pytorch.py b/references/recognition/train_pytorch.py
index 63442b61bd..fcbf77355b 100644
--- a/references/recognition/train_pytorch.py
+++ b/references/recognition/train_pytorch.py
@@ -15,6 +15,7 @@
 import torch
 from torchvision.transforms import Compose, Lambda, Normalize, ColorJitter
 from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
+from torch.optim.lr_scheduler import OneCycleLR, CosineAnnealingLR
 from contiguous_params import ContiguousParams
 import wandb
 from pathlib import Path
@@ -27,7 +28,7 @@
 from utils import plot_samples
 
 
-def fit_one_epoch(model, train_loader, batch_transforms, optimizer, mb):
+def fit_one_epoch(model, train_loader, batch_transforms, optimizer, scheduler, mb):
     model.train()
     train_iter = iter(train_loader)
     # Iterate over the batches of the dataset
@@ -41,6 +42,7 @@ def fit_one_epoch(model, train_loader, batch_transforms, optimizer, mb):
         optimizer.zero_grad()
         train_loss.backward()
         optimizer.step()
+        scheduler.step()
 
         mb.child.comment = f'Training loss: {train_loss.item():.6}'
 
@@ -162,6 +164,11 @@ def main(args):
     model_params = ContiguousParams([p for p in model.parameters() if p.requires_grad]).contiguous()
     optimizer = torch.optim.Adam(model_params, args.lr,
                                  betas=(0.95, 0.99), eps=1e-6, weight_decay=args.weight_decay)
+    # Scheduler
+    if args.sched == 'cosine':
+        scheduler = CosineAnnealingLR(optimizer, args.epochs * len(train_loader), eta_min=args.lr / 25e4)
+    elif args.sched == 'onecycle':
+        scheduler = OneCycleLR(optimizer, args.lr, args.epochs * len(train_loader))
 
     # Training monitoring
     current_time = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
@@ -190,7 +197,7 @@ def main(args):
     # Training loop
     mb = master_bar(range(args.epochs))
     for epoch in mb:
-        fit_one_epoch(model, train_loader, batch_transforms, optimizer, mb)
+        fit_one_epoch(model, train_loader, batch_transforms, optimizer, scheduler, mb)
 
         # Validation loop at the end of each epoch
         val_loss, exact_match, partial_match = evaluate(model, val_loader, batch_transforms, val_metric)
@@ -239,6 +246,7 @@ def parse_args():
                         help='Log to Weights & Biases')
     parser.add_argument('--pretrained', dest='pretrained', action='store_true',
                         help='Load pretrained parameters before starting the training')
+    parser.add_argument('--sched', type=str, default='cosine', help='scheduler to use')
     args = parser.parse_args()
 
     return args