diff --git a/references/classification/train_pytorch_orientation.py b/references/classification/train_pytorch_orientation.py
index 688e48564..82c2bd46a 100644
--- a/references/classification/train_pytorch_orientation.py
+++ b/references/classification/train_pytorch_orientation.py
@@ -11,6 +11,7 @@
 import logging
 import multiprocessing as mp
 import time
+from pathlib import Path
 
 import numpy as np
 import torch
@@ -35,6 +36,33 @@
 from doctr.models.utils import export_model_to_onnx
 from utils import EarlyStopper, plot_recorder, plot_samples
 
+SLACK_WEBHOOK_URL = None
+SLACK_WEBHOOK_PATH = Path(os.path.join(os.path.expanduser("~"), ".config", "doctr", "slack_webhook_url.txt"))
+if SLACK_WEBHOOK_PATH.exists():
+    with open(SLACK_WEBHOOK_PATH) as f:
+        SLACK_WEBHOOK_URL = f.read().strip()
+else:
+    print(f"{SLACK_WEBHOOK_PATH} does not exist, skip Slack integration configuration...")
+
+
+def send_on_slack(text: str):
+    """Send a message on Slack.
+
+    Args:
+        text (str): message to send on Slack
+    """
+    if SLACK_WEBHOOK_URL:
+        try:
+            import requests
+
+            requests.post(
+                url=SLACK_WEBHOOK_URL,
+                json={"text": f"[{datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}]: {text}"},
+            )
+        except Exception:
+            print("Impossible to send message on Slack, continue...")
+
+
 CLASSES = [0, 90, 180, 270]
 
 
@@ -121,7 +149,10 @@ def fit_one_epoch(model, train_loader, batch_transforms, optimizer, scheduler, a
 
     model.train()
     # Iterate over the batches of the dataset
+    last_progress = 0
+    interval_progress = 5
     pbar = tqdm(train_loader, position=1)
+    send_on_slack(str(pbar))
     for images, targets in pbar:
         if torch.cuda.is_available():
             images = images.cuda()
@@ -146,15 +177,24 @@ def fit_one_epoch(model, train_loader, batch_transforms, optimizer, scheduler, a
         scheduler.step()
 
         pbar.set_description(f"Training loss: {train_loss.item():.6}")
+        current_progress = pbar.n / pbar.total * 100
+        if current_progress - last_progress > interval_progress:
+            send_on_slack(str(pbar))
+            last_progress = int(current_progress)
+    send_on_slack(f"Final training loss: {train_loss.item():.6}")
 
 
 @torch.no_grad()
 def evaluate(model, val_loader, batch_transforms, amp=False):
     # Model in eval mode
     model.eval()
+    last_progress = 0
+    interval_progress = 5
+    pbar = tqdm(val_loader)
+    send_on_slack(str(pbar))
     # Validation loop
     val_loss, correct, samples, batch_cnt = 0.0, 0.0, 0.0, 0.0
-    for images, targets in tqdm(val_loader):
+    for images, targets in pbar:
         images = batch_transforms(images)
 
         if torch.cuda.is_available():
@@ -175,6 +215,11 @@ def evaluate(model, val_loader, batch_transforms, amp=False):
         batch_cnt += 1
         samples += images.shape[0]
 
+        current_progress = pbar.n / pbar.total * 100
+        if current_progress - last_progress > interval_progress:
+            send_on_slack(str(pbar))
+            last_progress = int(current_progress)
+
     val_loss /= batch_cnt
     acc = correct / samples
     return val_loss, acc
@@ -214,6 +259,9 @@ def main(args):
         pin_memory=torch.cuda.is_available(),
     )
     print(f"Validation set loaded in {time.time() - st:.4}s ({len(val_set)} samples in " f"{len(val_loader)} batches)")
+    send_on_slack(
+        f"Validation set loaded in {time.time() - st:.4}s ({len(val_set)} samples in " f"{len(val_loader)} batches)"
+    )
 
     batch_transforms = Normalize(mean=(0.694, 0.695, 0.693), std=(0.299, 0.296, 0.301))
 
@@ -223,6 +271,7 @@ def main(args):
     # Resume weights
     if isinstance(args.resume, str):
         print(f"Resuming {args.resume}")
+        send_on_slack(f"Resuming {args.resume}")
         checkpoint = torch.load(args.resume, map_location="cpu")
         model.load_state_dict(checkpoint)
 
@@ -276,6 +325,9 @@ def main(args):
         pin_memory=torch.cuda.is_available(),
     )
     print(f"Train set loaded in {time.time() - st:.4}s ({len(train_set)} samples in " f"{len(train_loader)} batches)")
+    send_on_slack(
+        f"Train set loaded in {time.time() - st:.4}s ({len(train_set)} samples in " f"{len(train_loader)} batches)"
+    )
 
     if args.show_samples:
         x, target = next(iter(train_loader))
@@ -338,9 +390,11 @@ def main(args):
         val_loss, acc = evaluate(model, val_loader, batch_transforms)
         if val_loss < min_loss:
             print(f"Validation loss decreased {min_loss:.6} --> {val_loss:.6}: saving state...")
+            send_on_slack(f"Validation loss decreased {min_loss:.6} --> {val_loss:.6}: saving state...")
             torch.save(model.state_dict(), f"./{exp_name}.pt")
             min_loss = val_loss
         print(f"Epoch {epoch + 1}/{args.epochs} - Validation loss: {val_loss:.6} (Acc: {acc:.2%})")
+        send_on_slack(f"Epoch {epoch + 1}/{args.epochs} - Validation loss: {val_loss:.6} (Acc: {acc:.2%})")
         # W&B
         if args.wb:
             wandb.log({
@@ -349,6 +403,7 @@ def main(args):
             })
         if args.early_stop and early_stopper.early_stop(val_loss):
             print("Training halted early due to reaching patience limit.")
+            send_on_slack("Training halted early due to reaching patience limit.")
             break
     if args.wb:
         run.finish()