12  Exploring Advanced Neural Networks: Instance Segmentation

Overview

In this section, we delve into the evolution of Faster R-CNN, a pivotal model in object detection, and explore its transformation into Mask R-CNN for instance segmentation tasks. Participants will learn how to extend the network architecture to generate both class labels and precise object masks, enabling the identification and delineation of individual objects within an image. By building on their existing knowledge of modifying classification networks, this section provides practical insights into adapting neural networks for advanced computer vision applications like instance segmentation.

12.1 What is instance segmentation?

Source: https://manipulation.csail.mit.edu/segmentation.html

• Image recognition: Indentifies the presence of objects in an image and provides a list of class probabilities.
• Semantic segmentation: Classifies each pixel in an image into a category, but not distinguish between different instances of the same class.
• Object detection: Detects objects by drawing bounding boxes around them, distinguishing individual entities but not providing pixel-level details.
• Instance segmentation: Combines object detection and semantic segmentation to identify and delineate individual objects at the pixel level.

12.2 Importance and applications

Instance segmentation is a fundamental task in computer vision with:

• Wide-ranging applications: Robotics ¹, medical imaging ², and more.
• Foundation for advanced tasks: Object tracking, pose estimation, and action recognition.

12.3 Comparison of classification networks and instance segmentation networks

In the previous section, we discussed how deep learning models consist of three core components: input adaptation, feature extractor, and output adaptation.

Using a ResNet-50 model as an example, let’s examine the structure of a classification network:

Code snippet to load a pre-trained ResNet-50 model

import torch
import torch.nn as nn
import torchvision.models as models

# Load the pre-trained ResNet-50 model
model = models.resnet50(pretrained=True)

# Modify the output layer to predict 10 classes
model.fc = nn.Linear(2048, 10)

# Print the model architecture
print(model)

The output of the code snippet above provides the architecture of the ResNet-50 model:

Output: Classification network architecture

ResNet(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer2): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(layer3): Sequential(
(0): BasicBlock(
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer4): Sequential(
(0): BasicBlock(
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
(fc): Linear(in_features=512, out_features=10, bias=True)
)
)

The output above is a liitle verbose, so let’s summarize the key components of the ResNet-50 model:

Summary: Classification network architecture

ResNet(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential()
(layer2): Sequential()
(layer3): Sequential()
(layer4): Sequential()
(avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
(fc): Linear(in_features=512, out_features=10, bias=True)
)

Where layerx represents the convolutional blocks in the network.

Now, let’s check what modifications we made to the ResNet-50 model:

• Input Adaptation: No changes needed, as ResNet-50 accepts images as input.
• Feature Extractor: Utilizes the pre-trained ResNet-50 model to extract features from images.
• Output Adaptation: Modified the output layer to predict 10 classes instead of the original 1000.

Question: Instance segmentation

Using the ResNet-50 model as a base, how would you modify each component to perform instance segmentation?

For instance segmentation, one possible adjustment is as follows:

• Input Adaptation: Remains unchanged as input is still an image.
• Feature Extractor: Utilizes the pre-trained ResNet-50 model.
• Output Adaptation: Must be modified to predict both class labels and object masks.

12.4 A step-by-step guide to instance segmentation

To achieve instance segmentation, let’s begin from object detection and progress to instance segmentation:

12.4.1 The First Step: R-CNN

Our journey starts with Region-based Convolutional Neural Networks (R-CNN). This model laid the groundwork for object detection by introducing a structured approach:

1. Region Proposal: Uses the Selective Search algorithm to generate potential object regions.
2. Feature Extraction: Applies a pre-trained CNN to each region to extract features.
3. Classification: Employs a linear SVM to classify these regions.
4. Bounding Box Regression: Refines the coordinates using a linear regression model.

Limitations of R-CNN

• Slow Inference: Processes each region independently, leading to inefficiency.
• Complex Training: Requires multiple models and stages.
• Inefficient Feature Extraction: Redundant computations for overlapping regions.

12.4.2 Evolution to Fast R-CNN

To address R-CNN’s limitations, Fast R-CNN was developed, enhancing speed and efficiency:

1. Region Proposal: Continues using Selective Search for generating proposals.
2. Feature Extraction: Extracts features from the entire image in a single pass.
3. RoI Pooling: Efficiently crops and resizes features for each region.
4. Classification and Bounding Box Regression: Utilizes fully connected layers for tasks.

Advantages of Fast R-CNN

• Single-Stage Inference: Processes the whole image once, enhancing speed.
• End-to-End Training: Trains the entire network in a unified stage.
• Efficient Feature Extraction: Avoids redundant calculations.

12.4.3 Introducing Faster R-CNN

Faster R-CNN further revolutionizes object detection by integrating a Region Proposal Network (RPN):

1. Feature Extraction: Continues with a pre-trained CNN for the entire image.
2. Region Proposal Network (RPN): Uses anchor boxes for generating proposals.
3. RoI Pooling: Maintains efficient cropping and resizing.
4. Classification and Bounding Box Regression: Achieves higher accuracy with shared layers.

Advantages of Faster R-CNN

• Unified Architecture: Merges feature extraction and region proposal steps.
• Improved Speed: Faster due to shared computation.
• Enhanced Accuracy: Produces better region proposals and detection results.

12.5 Transition to Instance Segmentation: Mask R-CNN

Building on Faster R-CNN, Mask R-CNN introduces instance segmentation by adding a mask prediction branch:

1. Feature Extraction: Utilizes a pre-trained CNN for comprehensive feature maps.
2. Region Proposal Network (RPN): Continues generating proposals with anchors.
3. RoI Pooling: Efficiently processes each region.
4. Classification and Bounding Box Regression: Refines object localization and classification.
5. Mask Prediction: Adds a convolutional branch to predict precise object masks.

Advantages of Mask R-CNN

• Instance Segmentation: Delivers pixel-level delineation of individual objects.
• Unified Architecture: Seamlessly combines detection and segmentation tasks.
• Enhanced Accuracy: Provides more precise object masks.

12.6 Conclusion

We’ve explored the evolution from R-CNN to Mask R-CNN, showing how each step builds on the last to improve performance. This process demonstrates how you can apply similar improvements to tackle a wide range of advanced tasks, not just instance segmentation. By understanding these developments, you’re equipped to enhance existing models and create innovative solutions for various challenges in computer vision.

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1. https://www.youtube.com/watch?v=tNLtXb04i3w

2. https://www.linkedin.com/pulse/medical-image-diagnosis-roles-object-detection-segmentation-egvcc