Add pretrained models guide

content/pytorch/concepts/loading-pretrained-models/loading-pretrained-models.md

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+---
+Title: Loading Pretrained Models
+Description: 'A brief description.' # Required; ideally under 150 characters and starts with a noun (used in search engine results and content previews)
+Subjects:
+  - Machine Learning
+Tags:
+  - Machine Learning
+  - Models
+  - Neural Networks
+  - Deep Learning
+  - TensorFlow
+  - PyTorch
+  - Datasets
+  - Scikit-learn
+  - AI
+CatalogContent: # Please use course/path landing page slugs, rather than linking to individual content items. If listing multiple items, please put the most relevant one first
+  - 'learn-example-course'
+  - 'paths/example-path'
+---
+
+**Pretrained models** are a foundational concept in machine learning, allowing developers and researchers to leverage models that have already been trained on extensive datasets. This approach accelerates development and improves accuracy by transferring knowledge from existing solutions to new tasks. In PyTorch, loading pretrained models is straightforward and provides access to many state-of-the-art models via the torchvision library and other community-supported sources.
+
+This guide introduces the concept of loading pretrained models in PyTorch, explains the syntax, and provides an example to demonstrate its usage.
+
+## Syntax
+
+To load a pretrained model in PyTorch, use the following syntax:
+
+```python
+from torchvision import models
+
+# Load a pretrained model
+model = models.<model_name>(pretrained=True)
+```
+
+- `<model_name>`: Replace this with the desired model name (e.g., `resnet18`, `vgg16`).
+- `pretrained=True`: Specifies that the model should load pretrained weights.
+
+## Example
+
+### Using a Pretrained ResNet-18 for Inference
+
+This example demonstrates loading a pretrained ResNet-18 model and using it for image classification.
+
+```python
+import torch
+from torchvision import models, transforms
+from PIL import Image
+
+# Load the pretrained ResNet-18 model
+model = models.resnet18(pretrained=True)
+model.eval()  # Set the model to evaluation mode
+
+# Define a transform for preprocessing the image
+preprocess = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+# Load an image
+image_path = "path/to/your/image.jpg"
+image = Image.open(image_path).convert("RGB")
+input_tensor = preprocess(image).unsqueeze(0)  # Add batch dimension
+
+# Perform inference
+with torch.no_grad():
+    output = model(input_tensor)
+
+# Get the predicted class
+predicted_class = torch.argmax(output[0]).item()
+print(f"Predicted Class: {predicted_class}")
+```
+
+### Fine-Tuning a Pretrained Model
+
+Here is an example of modifying a pretrained ResNet-18 model for a custom classification task:
+
+```python
+import torch.nn as nn
+
+# Load pretrained model
+model = models.resnet18(pretrained=True)
+
+# Modify the final fully connected layer to match the number of output classes
+num_classes = 10  # Adjust based on your dataset
+model.fc = nn.Linear(model.fc.in_features, num_classes)
+
+# Define loss and optimizer
+criterion = nn.CrossEntropyLoss()
+optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
+
+# The model is now ready for training on your dataset
+```
+
+## Additional Notes
+
+- **Customization**: Pretrained models can be fine-tuned by freezing certain layers and updating only the desired ones.
+- **Compatibility**: Ensure the input data format matches the model's expected input dimensions and preprocessing steps.
+
+For a comprehensive list of available pretrained models, visit the [PyTorch model documentation](https://pytorch.org/vision/stable/models.html).
+
+## Conclusion
+
+Loading pretrained models in PyTorch is a powerful technique to accelerate development and improve performance for deep learning tasks. With PyTorch's extensive model library and ease of use, pretrained models are accessible for various applications, from image classification to object detection and beyond.