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Train CNN Model on A Dataset with Ground Truth Labels
Evaluate CNN Model Using Encord Active
What have you learned in this series?

Encord Blog

Part 2: Evaluating Foundation Models (CLIP) using Encord Active

August 22, 2023

5 mins

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Contents

Train CNN Model on A Dataset with Ground Truth Labels
Evaluate CNN Model Using Encord Active
What have you learned in this series?

Written by

Stephen Oladele

View more posts

In the first article of this series on evaluating foundation models using Encord Active, you applied a CLIP model to a dataset that contains images of different facial expressions. You also saw how you could generate the classifications for the facial expressions using the CLIP model and import the predictions into Encord Active.

To round up that installment, you saw how Encord Active can help you evaluate your model quality by providing a handy toolbox to home in on how your model performs on different subsets of data and metrics (such as image singularity, redness, brightness, blurriness, and so on).

In this installment, you will focus on training a CNN model on the ground truth labels generated by the CLIP model. Toward the end of the article, you will import the dataset, ground truth labels, and model into Encord Active to evaluate the model and interpret the results to analyze the quality of your model.

Let’s jump right in! 🚀

Train CNN Model on A Dataset with Ground Truth Labels

In this section, you will train a CNN on the dataset created from labels predicted by the CLIP model. We saved the name of the dataset folder as Clip_GT_labels in the root directory—the code snippet for creating the new dataset from the CLIP predictions.

Remember to check out the complete code for the article in this repository.

Create a new Python script named “train_cnn.py” in the root directory. Import the required libraries:

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader
from torch.autograd import Variable
from tqdm import tqdm

Next, define transforms for data augmentation and load the dataset:

# Define the data transformations
train_transforms = transforms.Compose([
	transforms.Resize((256, 256)),
	transforms.RandomHorizontalFlip(),
	transforms.RandomVerticalFlip(),
	transforms.ToTensor(),
])
 
val_transforms = transforms.Compose([
	transforms.Resize((256, 256)),
	transforms.ToTensor(),
])
 
# Load the datasets
train_dataset = datasets.ImageFolder(
	r'Clip_GT_labels\Train',
	transform=train_transforms
)
 
val_dataset = datasets.ImageFolder(
	r'Clip_GT_labels\Val',
	transform=val_transforms
)
 
# Create the data loaders
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)

Next, define the CNN architecture, initialize the model, and define the loss function and optimizer:

# Define the CNN architecture
class CNN(nn.Module):
	def __init__(self, num_classes=7):
    	super(CNN, self).__init__()
    	# input shape (3, 256, 256)
    	self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)
    	self.relu1 = nn.ReLU(inplace=True)
    	self.pool1 = nn.MaxPool2d(kernel_size=2)
    	# shape (16, 128, 128)
 
    	# input shape (16, 128, 128)
    	self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
    	self.relu2 = nn.ReLU(inplace=True)
    	self.pool2 = nn.MaxPool2d(kernel_size=2)
    	# output shape (32, 64, 64)
 
    	# input shape (32, 64, 64)
    	self.conv3 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
    	self.relu3 = nn.ReLU(inplace=True)
    	self.pool3 = nn.MaxPool2d(kernel_size=2)
    	# output shape (64, 32, 32)
 
    	# input shape (64, 32, 32)
    	self.conv4 = nn.Conv2d(64, 32, kernel_size=3, padding=1)
    	self.relu4 = nn.ReLU(inplace=True)
    	self.pool4 = nn.MaxPool2d(kernel_size=2)
    	# output shape (32, 16, 16)
 
    	self.fc1 = nn.Linear(32 * 16 * 16, 128)
    	self.relu5 = nn.ReLU(inplace=True)
    	self.dropout = nn.Dropout(0.5)
    	self.fc2 = nn.Linear(128, num_classes)
 
	def forward(self, x):
    	x = self.conv1(x)
    	x = self.relu1(x)
    	x = self.pool1(x)
    	x = self.conv2(x)
    	x = self.relu2(x)
    	x = self.pool2(x)
    	x = self.conv3(x)
    	x = self.relu3(x)
    	x = self.pool3(x)
    	x = self.conv4(x)
    	x = self.relu4(x)
    	x = self.pool4(x)
    	x = x.view(-1, 32 * 16 * 16)
    	x = self.fc1(x)
    	x = self.relu5(x)
    	x = self.dropout(x)
    	x = self.fc2(x)
    	return x
 
# Initialize the model and define the loss function and optimizer
model = CNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

Finally, here’s the code to train the CNN on the dataset and export the model:

# Train the model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device)

num_epochs = 50
best_acc = 0.0

for epoch in range(num_epochs):
    train_loss = 0.0
    train_acc = 0.0

    model.train()
    for images, labels in train_loader:
        images = Variable(images.to(device))
        labels = Variable(labels.to(device))

        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        train_loss += loss.item() * images.size(0)
        _, preds = torch.max(outputs, 1)
        train_acc += torch.sum(preds == labels.data)

    train_loss = train_loss / len(train_dataset)
    train_acc = train_acc / len(train_dataset)

    val_loss = 0.0
    val_acc = 0.0

    model.eval()
    with torch.no_grad():
        for images, labels in val_loader:
            images = images.to(device)
            labels = labels.to(device)

            outputs = model(images)
            loss = criterion(outputs, labels)

            val_loss += loss.item() * images.size(0)
            _, preds = torch.max(outputs, 1)
            val_acc += torch.sum(preds == labels.data)

        val_loss = val_loss / len(val_dataset)
        val_acc = val_acc / len(val_dataset)

    print('Epoch [{}/{}], Train Loss: {:.4f}, Train Acc: {:.4f}, Val Loss: {:.4f}, Val Acc: {:.4f}'.format(epoch+1, num_epochs, train_loss, train_acc, val_loss, val_acc))

    if val_acc > best_acc:
        best_acc = val_acc
torch.save(model.state_dict(), 'cnn_model.pth')

Now, execute the script:

# Go back to root folder
cd ..
 
# execute script
python train_cnn.py

If the script executes successfully, you should see the exported model in your root directory:

├── Clip_GT_labels
├── EAemotions
├── classification_transformer.py
├── cnn_model.pth
├── emotions
├── make_clip_predictions.py
└── train_cnn.py

Evaluate CNN Model Using Encord Active

In this section, you will perform the following task:

Create a new Encord project using the test set in the Clip_GT_labels dataset.
Load the trained CNN model above (“cnn_model.pth”) and use it to make predictions on the test.
Import the predictions into Encord for evaluation.

Create An Encord Project

Just as you initially created a project in the first article, use the test set in the Clip_GT_labels dataset to initialize a new Encord project. The name specified here for the new project is EAsota.

# Create project
encord-active init --name EAsota --transformer classification_transformer.py Clip_GT_labels\Test
 
# Change to project directory
cd EAsota
 
# Store ontology
encord-active print --json ontology

Make Predictions using CNN Model

In the root directory, create a Python script with the name cnn_prediction.py.

Load the new project into the script:

# Import encord project
project_path = r'EASOTA'
project = Project(Path(project_path)).load()
 
project_ontology = json.loads(
	(project.file_structure.project_dir/'ontology_output.json').read_text()
)
 
ontology = json.loads(
	project.file_structure.ontology.read_text(encoding="utf-8")
)

Next, instantiate the CNN model and load the artifact (saved state):

# Create an instance of the model
model = CNN()
 
# Load the saved state dictionary file
model_path = 'cnn_model.pth'
model.load_state_dict(torch.load(model_path))

Using the same procedures as in the previous article, make predictions on the test images and export the predictions by appending them to the predictions_to_import list:

model.eval()
output = model(image_transformed.to(device).unsqueeze(dim=0))
class_id = output.argmax(dim=1, keepdim=True)[0][0].item()
model_prediction = project_ontology['classifications'][classes[class_id]]
my_predictions.append(classes[class_id])
confidence = output.softmax(1).tolist()[0][class_id]

If you included the same custom metrics, you should have an output in your console:

Import Predictions into Encord

In the EAsota project, you should find the predictions.pkl file, which stores the predictions from the CNN model.

Import the predictions into Encord Active for evaluation:

# Change to Project directory
cd ./EAsota
 
# Import Predictions
encord-active import predictions predictions.pkl
 
# Start encord-active webapp server
encord-active visualize

Below is Encord Active’s evaluation of the CNN model’s performance:

Interpreting the model's results

The classification metrics provided show that the model is performing poorly. The accuracy of 0.27 means that only 27% of the predictions are correct. The mean precision of 0.18 indicates that only 18% of the positive predictions are correct, and the mean recall of 0.23 means that only 23% of the instances belonging to a class are captured.

The mean F1 score of 0.19 reflects the overall balance between precision and recall, but it is still low. These metrics suggest that the model is not making accurate predictions and needs significant improvement.

Encord also visualized each metric's relative importance and correlation to the model's performance. For example, increasing the image-level annotation quality (P), slightly reducing the brightness of the images in the dataset, etc., can positively impact the model’s performance.

What have you learned in this series?

Over the past two articles, you have seen how to use a CLIP model and train a CNN model for image classification.

Most importantly, you learned to use Encord Active, an open-source computer vision toolkit, to evaluate the model’s performance using an interactive user interface. You could also visually get the accuracy, precision, f1-score, recall, confusion matrix, feature importance, etc., from Encord Aactive.

Check out the Encord Active documentation to explore other functionalities of the open-source framework for computer vision model testing, evaluation, and validation. Check out the project on GitHub, leave a star 🌟 if you like it, or leave an issue if you find something is missing—we love feedback!

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Written by

Stephen Oladele

View more posts

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May 11 2023

5 M

machine learning

machine learning

Guide to Image Segmentation in Computer Vision: Best Practices

Image segmentation is a crucial task in computer vision, where the goal is to divide an image into different meaningful and distinguishable regions or objects. It is a fundamental task in various applications such as object recognition, tracking, and detection, medical imaging, and robotics. Many techniques are available for image segmentation, ranging from traditional methods to deep learning-based approaches. With the advent of deep learning, the accuracy and efficiency of image segmentation have improved significantly. In this guide, we will discuss the basics of image segmentation, including different types of segmentation, applications, and various techniques used for image segmentation, including traditional, deep learning, and foundation model techniques. We will also cover evaluation metrics and datasets for evaluating image segmentation algorithms and future directions in image segmentation. By the end of this guide, you will have a better understanding of image segmentation, its applications, and the various techniques used for segmenting images. This guide is for you if you are a data scientist, machine learning engineer or your team is considering using image segmentation as part of an artificial intelligence computer vision project. What is Image Segmentation? Image segmentation is the process of dividing an image into multiple meaningful and homogeneous regions or objects based on their inherent characteristics, such as color, texture, shape, or brightness. Image segmentation aims to simplify and/or change the representation of an image into something more meaningful and easier to analyze. Here, each pixel is labeled. All the pixels belonging to the same category have a common label assigned to them. The task of segmentation can further be done in two ways: Similarity: As the name suggests, the segments are formed by detecting similarity between image pixels. It is often done by thresholding (see below for more on thresholding). Machine learning algorithms (such as clustering) are based on this type of approach for image segmentation. Discontinuity: Here, the segments are formed based on the change of pixel intensity values within the image. This strategy is used by line, point, and edge detection techniques to obtain intermediate segmentation results that may be processed to obtain the final segmented image. Types of Segmentation Image segmentation modes are divided into three categories based on the amount and type of information that should be extracted from the image: Instance, semantic, and panoptic. Let’s look at these various modes of image segmentation methods. Also, to understand the three modes of image segmentation, it would be more convenient to know more about objects and backgrounds. Objects are the identifiable entities in an image that can be distinguished from each other by assigning unique IDs, while the background refers to parts of the image that cannot be counted, such as the sky, water bodies, and other similar elements. By distinguishing between objects and backgrounds, it becomes easier to understand the different modes of image segmentation and their respective applications. Instance Segmentation Instance segmentation is a type of image segmentation that involves detecting and segmenting each object in an image. It is similar to object detection but with the added task of segmenting the object’s boundaries. The algorithm has no idea of the class of the region, but it separates overlapping objects. Instance segmentation is useful in applications where individual objects need to be identified and tracked. Instance segmentation Semantic Segmentation Semantic segmentation is a type of image segmentation that involves labeling each pixel in an image with a corresponding class label with no other information or context taken into consideration. The goal is to assign a label to every pixel in the image, which provides a dense labeling of the image. The algorithm takes an image as input and generates a segmentation map where the pixel value (0,1,...255) of the image is transformed into class labels (0,1,...n). It is useful in applications where identifying the different classes of objects on the road is important. Semantic segmentation - the human and the dog are classified together as mammals and separated from the rest of the background. Panoptic Segmentation Panoptic segmentation is a combination of semantic and instance segmentation. It involves labeling each pixel with a class label and identifying each object instance in the image. This mode of image segmentation provides the maximum amount of high-quality granular information from machine learning algorithms. It is useful in applications where the computer vision model needs to detect and interact with different objects in its environment, like an autonomous robot. Panoptic segmentation Each type of segmentation has its unique characteristics and is useful in different applications. In the following section, let’s discuss the various applications of image segmentation. Image Segmentation Techniques Traditional Techniques Traditional image segmentation techniques have been used for decades in computer vision to extract meaningful information from images. These techniques are based on mathematical models and algorithms that identify regions of an image with common characteristics, such as color, texture, or brightness. Traditional image segmentation techniques are usually computationally efficient and relatively simple to implement. They are often used for applications that require fast and accurate segmentation of images, such as object detection, tracking, and recognition. In this section, we will explore some of the most common techniques. Thresholding Thresholding Thresholding is one of the simplest image segmentation methods. Here, the pixels are divided into classes based on their histogram intensity which is relative to a fixed value or threshold. This method is suitable for segmenting objects where the difference in pixel values between the two target classes is significant. In low-noise images, the threshold value can be kept constant, but with images with noise, dynamic thresholding performs better. In thresholding-based segmentation, the greyscale image is divided into two segments based on their relationship to the threshold value, producing binary images. Algorithms like contour detection and identification work on these binarized images. The two commonly used thresholding methods are: Global thresholding is a technique used in image segmentation to divide images into foreground and background regions based on pixel intensity values. A threshold value is chosen to separate the two regions, and pixels with intensity values above the threshold are assigned to the foreground region and those below the threshold to the background region. This method is simple and efficient but may not work well for images with varying illumination or contrast. In those cases, adaptive thresholding techniques may be more appropriate. Adaptive thresholding is a technique used in image segmentation to divide an image into foreground and background regions by adjusting the threshold value locally based on the image characteristics. The method involves selecting a threshold value for each smaller region or block, based on the statistics of the pixel values within that block. Adaptive thresholding is useful for images with non-uniform illumination or varying contrast and is commonly used in document scanning, image binarization, and image segmentation. The choice of adaptive thresholding technique depends on the specific application requirements and image characteristics. Image showing different thresholding techniques. Source: Author Region-based Segmentation Region-based segmentation is a technique used in image processing to divide an image into regions based on similarity criteria, such as color, texture, or intensity. The method involves grouping pixels into regions or clusters based on their similarity and then merging or splitting regions until the desired level of segmentation is achieved. The two commonly used region-based segmentation techniques are: Split and merge segmentation is a region-based segmentation technique that recursively divides an image into smaller regions until a stopping criterion is met and then merges similar regions to form larger regions. The method involves splitting the image into smaller blocks or regions and then merging adjacent regions that meet certain similarity criteria, such as similar color or texture. Split and merge segmentation is a simple and efficient technique for segmenting images, but it may not work well for complex images with overlapping or irregular regions. Graph-based segmentation is a technique used in image processing to divide an image into regions based on the edges or boundaries between regions. The method involves representing the image as a graph, where the nodes represent pixels, and the edges represent the similarity between pixels. The graph is then partitioned into regions by minimizing a cost function, such as the normalized cut or minimum spanning tree. Example of graph-based image segmentation. Source Edge-based Segmentation Edge-based segmentation is a technique used in image processing to identify and separate the edges of an image from the background. The method involves detecting the abrupt changes in intensity or color values of the pixels in the image and using them to mark the boundaries of the objects. The two most common edge-based segmentation techniques are: Canny edge detection is a popular method for edge detection that uses a multi-stage algorithm to detect edges in an image. The method involves smoothing the image using a Gaussian filter, computing the gradient magnitude and direction of the image, applying non-maximum suppression to thin the edges, and using hysteresis thresholding to remove weak edges. Example of canny edge detection Sobel edge detection is a method for edge detection that uses a gradient-based approach to detect edges in an image. The method involves computing the gradient magnitude and direction of the image using a Sobel operator, which is a convolution kernel that extracts horizontal and vertical edge information separately. Example of Sobel edge detection. Laplacian of Gaussian (LoG) edge detection is a method for edge detection that combines Gaussian smoothing with the Laplacian operator. The method involves applying a Gaussian filter to the image to remove noise and then applying the Laplacian operator to highlight the edges. LoG edge detection is a robust and accurate method for edge detection, but it is computationally expensive and may not work well for images with complex edges. Example of Laplacian of Gaussian edge detection. Clustering Clustering is one of the most popular techniques used for image segmentation, as it can group pixels with similar characteristics into clusters or segments. The main idea behind clustering-based segmentation is to group pixels into clusters based on their similarity, where each cluster represents a segment. This can be achieved using various clustering algorithms, such as K means clustering, mean shift clustering, hierarchical clustering, and fuzzy clustering. K-means clustering is a widely used clustering algorithm for image segmentation. In this approach, the pixels in an image are treated as data points, and the algorithm partitions these data points into K clusters based on their similarity. The similarity is measured using a distance metric, such as Euclidean distance or Mahalanobis distance. The algorithm starts by randomly selecting K initial centroids, and then iteratively assigns each pixel to the nearest centroid and updates the centroids based on the mean of the assigned pixels. This process continues until the centroids converge to a stable value. ‍ Showing the result of segmenting the image at k=2,4,10. Source Mean shift clustering is another popular clustering algorithm used for image segmentation. In this approach, each pixel is represented as a point in a high-dimensional space, and the algorithm shifts each point toward the direction of the local density maximum. This process is repeated until convergence, where each pixel is assigned to a cluster based on the nearest local density maximum. Source ‍Though these techniques are simple, they are fast and memory efficient. But these techniques are more suitable for simpler segmentation tasks as well. They often require tuning to customize the algorithm as per the use case and also provide limited accuracy on complex scenes. Deep Learning Techniques Neural networks also provide solutions for image segmentation by training neural networks to identify which features are important in an image, rather than relying on customized functions like in traditional algorithms. Neural nets that perform the task of segmentation typically use an encoder-decoder structure. The encoder extracts features of an image through narrower and deeper filters. If the encoder is pre-trained on a task like an image or face recognition, it then uses that knowledge to extract features for segmentation (transfer learning). The decoder then over a series of layers inflates the encoder’s output into a segmentation mask resembling the pixel resolution of the input image. The basic architecture of the neural network model for image segmentation. Source ‍ Many deep learning models are quite adept at performing the task of segmentation reliably. Let’s have a look at a few of them: U-Net U-Net is a modified, fully convolutional neural network. It was primarily proposed for medical purposes, i.e., to detect tumors in the lungs and brain. It has the same encoder and decoder. The encoder is used to extract features using a shortcut connection, unlike in fully convolutional networks, which extract features by upsampling. The shortcut connection in the U-Net is designed to tackle the problem of information loss. In the U-Net architecture, the encoders and decoders are designed in such a manner that the network captures finer information and retains more information by concatenating high-level features with low-level ones. This allows the network to yield more accurate results. U-Net Architecture. Source‍ SegNet SegNet is also a deep fully convolutional network that is designed especially for semantic pixel-wise segmentation. Like U-Net, SegNet’s architecture also consists of encoder and decoder blocks. The SegNet differs from other neural networks in the way it uses its decoder for upsampling the features. The decoder network uses the pooling indices computed in the max-pooling layer which in turn makes the encoder perform non-linear upsampling. This eliminates the need for learning to upsample. SegNet is primarily designed for scene-understanding applications. SegNet Architecture. Source DeepLab DeepLab is primarily a convolutional neural network (CNN) architecture. Unlike the other two networks, it uses features from every convolutional block and then concatenates them to their deconvolutional block. The neural network uses the features from the last convolutional block and upsamples it like the fully convolutional network (FCN). It uses the atrous convolution or dilated convolution method for upsampling. The advantage of atrous convolution is that the computation cost is reduced while capturing more information. The encoder-Decoder architecture of DeepLab v3. Source Foundation Model Techniques Foundation models have also been used for image segmentation, which divides an image into distinct regions or segments. Unlike language models, which are typically based on transformer architectures, foundation models for image segmentation often use convolutional neural networks (CNNs) designed to handle image data. Segment Anything Model Segment Anything Model (SAM) is considered the first foundation model for image segmentation. SAM is built on the largest segmentation dataset to date, with over 1 billion segmentation masks. It is trained to return a valid segmentation mask for any prompt, where a prompt can be foreground/background points, a rough box or mask, freeform text, or general information indicating what to segment in an image. Under the hood, an image encoder produces a one-time embedding for the image, while a lightweight encoder converts any prompt into an embedding vector in real time. These two information sources are combined in a lightweight decoder that predicts segmentation masks.‍ Source Metrics for Evaluating Image Segmentation Algorithms Pixel Accuracy Pixel accuracy is a common evaluation metric used in image segmentation to measure the overall accuracy of the segmentation algorithm. It is defined as the ratio of the number of correctly classified pixels to the total number of pixels in the image. Pixel accuracy is a straightforward and easy-to-understand metric that provides a quick assessment of the segmentation performance. However, it does not account for the spatial alignment between the ground truth and the predicted segmentation, which can be important in some applications. In addition, pixel accuracy can be sensitive to class imbalance, where one class has significantly more pixels than another. This can lead to a biased evaluation of the algorithm's performance. Dice Coefficient The dice coefficient measures the similarity between two sets of binary data, in this case, the ground truth segmentation and the predicted segmentation. The dice coefficient is calculated as Where intersection is the number of pixels that are correctly classified as positive by both the ground truth and predicted segmentations, and ground truth and predicted are the total number of positive pixels in the respective segmentations. The Dice coefficient ranges from 0 to 1, with higher values indicating better segmentation performance. A value of 1 indicates a perfect overlap between the ground truth and predicted segmentations. The Dice coefficient is a popular metric for image segmentation because it is sensitive to small changes in the segmentation and is not affected by class imbalance. However, it does not account for the spatial alignment between the ground truth and predicted segmentation, which can be important in some applications. Jaccard Index (IOU) The Jaccard index, also known as the intersection over union (IoU) score, measures the similarity between the ground truth segmentation and the predicted segmentation. It is formulated as Where intersection is the number of pixels that are correctly classified as positive by both the ground truth and predicted segmentations, and ground truth and predicted are the total number of positive pixels in the respective segmentations. The IoU score ranges from 0 to 1, with higher values indicating better segmentation performance. A value of 1 indicates a perfect overlap between the ground truth and predicted segmentations. The Jaccard index takes into account both the true positives and false positives and is not affected by class imbalance. It also accounts for the spatial alignment between the ground truth and predicted segmentations. Datasets for Evaluating Image Segmentation Algorithms The evaluation of image segmentation algorithms is a crucial task n computer vision research. To measure the performance of these algorithms, various benchmark datasets have been developed. We will be discussing three popular datasets for evaluating image segmentation algorithms. These datasets provide carefully annotated images with pixel-level annotations, allowing researchers to test and compare the effectiveness of their segmentation algorithms. Barkley Segmentation Dataset and Benchmark The Barkley Segmentation Dataset is a standard benchmark for contour detection. This dataset is intended for testing natural edge detection, which takes into account background boundaries in addition to object interior and exterior boundaries as well as object contours. It includes 500 natural images with carefully annotated boundaries collected from multiple users. The dataset is divided into three parts: 200 for training, 100 for validation, and the rest 200 for testing. Pascal VOC Segmentation Dataset The Pascal VOC Segmentation Dataset is a popular benchmark dataset for evaluating image segmentation algorithms. It contains images from 20 object categories and provides pixel-level annotations for each image. The dataset is divided into the train, validation, and test sets, with the test set used to evaluate the performance of segmentation algorithms. The Pascal VOC Segmentation Dataset has been used as a benchmark for various computer vision challenges, including the Pascal VOC Challenge and the COCO Challenge. MS COCO Segmentation Dataset The Microsoft Common Objects in Context (COCO) Segmentation Dataset is another widely used dataset for evaluating image segmentation algorithms. It contains over 330,000 images with object annotations, including segmentations of 80 object categories. The dataset is divided into the train, validation, and test sets, with the test set containing around 5,000 images. The MS COCO Segmentation Dataset is often used as a benchmark for evaluating segmentation algorithms in various computer vision challenges, including the COCO Challenge. Future Direction of Image Segmentation Auto-Segmentation with SAM Auto-segmentation refers to the process of automatically segmenting an image without human intervention. Auto-segmentation with Meta’s Segment Anything Model (SAM) has instantly become popular as it shows remarkable performance in image segmentation tasks. It is a single model that can easily perform both interactive segmentation and automatic segmentation. Since SAM is trained on a diverse, high-quality dataset, it can generalize to new types of objects and images beyond what is observed during training. This ability to generalize means that by and large, practitioners will no longer need to collect their segmentation data and fine-tune a model for their use case. Improvement in segmentation accuracy Improving segmentation accuracy is one of the main goals of researchers in the field of computer vision. Accurate segmentation is essential for various applications, including medical imaging, object recognition, and autonomous vehicles. While deep learning techniques have led to significant improvements in segmentation accuracy in recent years, there is still much room for improvement. Here are some ways researchers are working to improve segmentation accuracy: Incorporating additional data sources: One approach to improving segmentation accuracy is incorporating additional data sources beyond the raw image data. For example, depth information can provide valuable cues for object boundaries and segmentation, particularly in complex scenes with occlusions and clutter. Developing new segmentation algorithms: Researchers continuously develop new algorithms for image segmentation that can improve accuracy. For example, some recent approaches use adversarial training or reinforcement learning to refine segmentation results. Improving annotation quality: The quality of the ground truth annotations used to train segmentation algorithms is essential to achieving high accuracy. Researchers are working to improve annotation quality through various means, including incorporating expert knowledge and utilizing crowdsourcing platforms. Refining evaluation metrics: Evaluation metrics play a crucial role in measuring the accuracy of segmentation algorithms. Researchers are exploring new evaluation metrics beyond the traditional Dice coefficient and Jaccard index, such as the Boundary F1 score, which can better capture the quality of object boundaries. Integrate Deep Learning with Traditional Techniques While deep learning techniques have shown remarkable performance in segmentation tasks, traditional techniques such as clustering, thresholding, and morphological operations can still provide useful insights and improve accuracy. Here are some ways researchers are integrating deep learning with traditional techniques in image segmentation: Hybrid models: Researchers are developing hybrid models that combine deep learning with traditional techniques. For example, some approaches use clustering or thresholding to initialize deep learning models or post-process segmentation results. Multi-stage approaches: Multi-stage approaches involve using deep learning for initial segmentation and then refining the results using traditional techniques. For example, some approaches use morphological operations to smooth and refine segmentation results. Attention-based models: Attention-based models are a type of deep learning model that incorporates traditional techniques for computing attention weights within a feature map. Attention-based models can improve accuracy by focusing on relevant image features and ignoring irrelevant ones. Transfer learning: Transfer learning involves pretraining deep learning models on large datasets and then fine-tuning them for specific segmentation tasks. Traditional techniques such as clustering or thresholding can be used to identify relevant features for transfer learning. Applications of Image Segmentation Image segmentation has a wide range of applications in various fields, including medical imaging, robotics, autonomous vehicles, and surveillance. Here are some examples of how image segmentation is used in different fields: Medical imaging: Image segmentation is widely used in medical imaging for tasks such as tumor detection, organ segmentation, and disease diagnosis. Accurate segmentation is essential for treatment planning and monitoring disease progression. Robotics: Image segmentation is used in robotics for object recognition and manipulation. For example, robots can use segmentation to recognize and grasp specific objects, such as tools or parts, in industrial settings. Autonomous vehicles: Image segmentation is essential for the development of autonomous vehicles, allowing them to detect and classify objects in their environment, such as other vehicles, pedestrians, and obstacles. Accurate segmentation is crucial for safe and reliable autonomous navigation. Surveillance: Image segmentation is used in surveillance for detecting and tracking objects and people in real-time video streams. Segmentation can help to identify and classify objects of interest, such as suspicious behavior or potential threats. Agriculture: Image segmentation is used in agriculture for crop monitoring, disease detection, and yield prediction. Accurate segmentation can help farmers make informed decisions about crop management and optimize crop yields. Art and design: Image segmentation is used in art and design for tasks such as image manipulation, color correction, and style transfer. Segmentation can help to separate objects or regions of an image and apply different effects or modifications to them. Image Segmentation: Key Takeaways Image segmentation is a powerful technique that allows us to identify and separate different objects or regions within an image. It has a wide range of applications in fields such as medical imaging, robotics, and computer vision. In this guide, we covered various image segmentation techniques, including traditional techniques such as thresholding, region-based segmentation, edge-based segmentation, and clustering, as well as deep learning and foundation model techniques. We also discussed different evaluation metrics and datasets used to evaluate segmentation algorithms. As image segmentation continues to advance, future directions will focus on improving segmentation accuracy, integrating deep learning with traditional techniques, and exploring new applications in various fields. Auto-segmentation with the Segment Anything Model (SAM) is a promising direction that can reduce manual intervention and improve accuracy. Integration of deep learning with traditional techniques can also help to overcome the limitations of individual techniques and improve overall performance. With ongoing research and development, we can expect image segmentation to continue to make significant contributions to various fields and industries. Further Reading on Image Segmentation Comparing Two Object Segmentation Models: Mask-RCNN vs. Personalized-SAM Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges Visual Segmentation of “Simple” Objects for Robots Best practices in deep learning based segmentation of microscopy image Image segmentation: Papers with code

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