Image Classification with ResNet

Set up Your Azure ML Workspace

Ensure you have a fully configured Azure AI Machine Learning workspace. This includes setting up compute instances or clusters for training.

Learn more about workspace setup

Prepare Your Dataset

Gather and organize your image dataset. For this tutorial, we'll assume a dataset structure where images are organized into folders, each representing a class. You can also use public datasets available through Azure ML.

Example dataset structure:

data/ cat/ cat1.jpg cat2.jpg dog/ dog1.jpg dog2.jpg

Choose a Framework and Implement ResNet

You can implement ResNet using popular deep learning frameworks like TensorFlow or PyTorch. Azure ML supports both, allowing you to bring your existing code or use pre-built components.

Below is a conceptual Python snippet using TensorFlow:

import tensorflow as tf from tensorflow.keras.applications import ResNet50 from tensorflow.keras.layers import Dense, GlobalAveragePooling2D from tensorflow.keras.models import Model # Load a pre-trained ResNet50 model without the top classification layer base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3)) # Add custom layers for your classification task x = base_model.output x = GlobalAveragePooling2D()(x) x = Dense(1024, activation='relu')(x) predictions = Dense(num_classes, activation='softmax')(x) model = Model(inputs=base_model.input, outputs=predictions) # Freeze base model layers (optional, for transfer learning) for layer in base_model.layers: layer.trainable = False model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

Train the Model

Use Azure ML's training capabilities to run your training script. You can leverage distributed training for faster results on large datasets.

Key aspects of training:

• Data preprocessing and augmentation.
• Configuring training parameters (batch size, epochs, learning rate).
• Monitoring training progress using Azure ML studio.

Evaluate and Register the Model

After training, evaluate your model's performance using metrics like accuracy, precision, and recall. Once satisfied, register your trained model in your Azure ML workspace for versioning and deployment.

Deploy the Model

Deploy your registered model as a web service on Azure Kubernetes Service (AKS) or Azure Container Instances (ACI) for real-time inference or batch scoring.

Explore deployment options