📚 fix(docs): fixed the "Anomalib in 15 Minutes" train and inference code (#2904)

* docs: fixed the 'Anomalib in 15 Minutes' train and inference code to match 2.1.0

Signed-off-by: FedericoDeBona <federicodebona@hotmail.it>

* Update examples/api/01_getting_started/basic_openvino_inference.py

Co-authored-by: Ashwin Vaidya <ashwinnitinvaidya@gmail.com>
Signed-off-by: Federico <36895247+FedericoDeBona@users.noreply.github.com>

* Update examples/api/01_getting_started/basic_torch_inference.py

Co-authored-by: Ashwin Vaidya <ashwinnitinvaidya@gmail.com>
Signed-off-by: Federico <36895247+FedericoDeBona@users.noreply.github.com>

---------

Signed-off-by: FedericoDeBona <federicodebona@hotmail.it>
Signed-off-by: Federico <36895247+FedericoDeBona@users.noreply.github.com>
Co-authored-by: Ashwin Vaidya <ashwinnitinvaidya@gmail.com>

Author: FedericoDeBona

docs/source/markdown/get_started/anomalib.md

@@ -52,7 +52,7 @@ interfaces, and might be easier for those who would like to use anomalib off-the
 
 ```{literalinclude} ../../../../examples/api/01_getting_started/basic_training.py
 :language: python
-:lines: 10-34
+:lines: 10-53
 ```
 
 :::
@@ -61,6 +61,7 @@ interfaces, and might be easier for those who would like to use anomalib off-the
 
 ```{literalinclude} ../../../../examples/cli/01_getting_started/basic_training.sh
 :language: bash
+:lines: 10-33
 ```
 
 :::
@@ -81,6 +82,7 @@ Anomalib includes multiple inferencing scripts, including Torch, Lightning, Grad
 
 ```{literalinclude} ../../../../examples/api/01_getting_started/basic_inference.py
 :language: python
+:lines: 10-40
 ```
 
 :::
@@ -104,8 +106,9 @@ Anomalib includes multiple inferencing scripts, including Torch, Lightning, Grad
 :::{tab-item} API
 :sync: label-1
 
-```{code-block} python
-
+```{literalinclude} ../../../../examples/api/01_getting_started/basic_torch_inference.py
+:language: python
+:lines: 10-11
 ```
 
 :::
@@ -129,8 +132,9 @@ Anomalib includes multiple inferencing scripts, including Torch, Lightning, Grad
 :::{tab-item} API
 :sync: label-1
 
-```{code-block} python
-
+```{literalinclude} ../../../../examples/api/01_getting_started/basic_openvino_inference.py
+:language: python
+:lines: 10-28
 ```
 
 :::

examples/api/01_getting_started/basic_inference.py

@@ -8,20 +8,18 @@
 """
 
 # 1. Import required modules
-from pathlib import Path
-
 from anomalib.data import PredictDataset
 from anomalib.engine import Engine
-from anomalib.models import EfficientAd
+from anomalib.models import Patchcore
 
 # 2. Initialize the model and load weights
-model = EfficientAd()
+model = Patchcore()
 engine = Engine()
 
 # 3. Prepare test data
 # You can use a single image or a folder of images
 dataset = PredictDataset(
-    path=Path("path/to/test/images"),
+    path="path/to/test/images",
     image_size=(256, 256),
 )
 

examples/api/01_getting_started/basic_openvino_inference.py

@@ -0,0 +1,28 @@
+# Copyright (C) 2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+"""Getting Started with Anomalib Inference using the Python API.
+
+This example shows how to perform inference on a trained model
+using the Anomalib Python API.
+"""
+
+# 1. Import required modules
+from anomalib.deploy import OpenVINOInferencer
+
+# 2. Initialize the inferencer
+inferencer = OpenVINOInferencer(
+    path="/path/to/openvino/model.bin",
+)
+
+# 4. Get predictions
+predictions = inferencer.predict(
+    image="/path/to/image.png",
+)
+
+# 5. Access the results
+if predictions is not None:
+    for prediction in predictions:
+        anomaly_map = prediction.anomaly_map  # Pixel-level anomaly heatmap
+        pred_label = prediction.pred_label  # Image-level label (0: normal, 1: anomalous)
+        pred_score = prediction.pred_score  # Image-level anomaly score

examples/api/01_getting_started/basic_torch_inference.py

@@ -0,0 +1,11 @@
+# Copyright (C) 2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+"""Getting Started with Anomalib Inference using the Python API.
+
+This example shows how to perform inference on a trained model
+using the Anomalib Python API.
+"""
+
+# TorchInferencer is a legacy inferencer. Consider using Engine.predict() instead,
+# which provides a more modern and feature-rich interface for model inference.

examples/api/01_getting_started/basic_training.py

@@ -9,8 +9,9 @@
 
 # 1. Import required modules
 from anomalib.data import MVTecAD
+from anomalib.deploy import ExportType
 from anomalib.engine import Engine
-from anomalib.models import EfficientAd
+from anomalib.models import Patchcore
 
 # 2. Create a dataset
 # MVTecAD is a popular dataset for anomaly detection
@@ -19,15 +20,29 @@
     category="bottle",  # MVTec category to use
     train_batch_size=32,  # Number of images per training batch
     eval_batch_size=32,  # Number of images per validation/test batch
-    num_workers=8,  # Number of parallel processes for data loading
 )
 
 # 3. Initialize the model
-# EfficientAd is a good default choice for beginners
-model = EfficientAd()
+# Patchcore is a good choice for beginners
+model = Patchcore(
+    num_neighbors=6,  # Override default model settings
+)
 
 # 4. Create the training engine
-engine = Engine(max_epochs=10)  # Train for 10 epochs
+engine = Engine(
+    max_epochs=1,  # Override default trainer settings
+)
 
 # 5. Train the model
+# This produces a lightning model (.ckpt)
 engine.fit(datamodule=datamodule, model=model)
+
+# 6. Test the model performance
+test_results = engine.test(datamodule=datamodule, model=model)
+
+# 7. Export the model
+# Different formats are available: Torch, OpenVINO, ONNX
+engine.export(
+    model=model,
+    export_type=ExportType.OPENVINO,
+)

examples/cli/01_getting_started/basic_training.sh

@@ -8,19 +8,26 @@
 # This example shows the basic steps to train an anomaly detection model.
 
 # 1. Basic Training
-# Train a model using default configuration (recommended for beginners)
+# Train and test a model using default configuration on MVTecAD bottle (default category)
 echo "Training with default configuration..."
-anomalib train --model efficient_ad
+anomalib train --model Patchcore --data anomalib.data.MVTecAD
 
 # 2. Training with Basic Customization
-# Customize basic parameters like batch size and epochs
+# Customize basic parameters like batch size and epochs.
+# For example `EfficientAd` requires a train batch size of 1
 echo -e "\nTraining with custom parameters..."
-anomalib train --model efficient_ad \
-    --data.train_batch_size 32 \
-    --trainer.max_epochs 10
+anomalib train --model EfficientAd --data anomalib.data.MVTecAD \
+    --data.category hazelnut \
+    --data.train_batch_size 1 \
+    --trainer.max_epochs 200
 
-# 3. Using a Different Dataset
-# Train on a specific category of MVTecAD dataset
-echo -e "\nTraining on MVTecAD bottle category..."
-anomalib train --model efficient_ad \
-    --data.category bottle
+# 3. Train with config file
+# Train with a custom config file
+echo -e "\nTraining with config file..."
+anomalib train --config path/to/config.yaml
+
+# 4. Export a trained model into OpenVINO
+echo .e "\nExporting model into OpenVINO..."
+anomalib export --model Patchcore \
+        --ckpt_path /path/to/model.ckpt \
+        --export_type OPENVINO