The Ultimate Guide to Launching Your Computer Vision Project: Resources for Every Step
Computer vision, a branch of artificial intelligence (AI), allows machines to ‘see’ and interpret the world through images and videos, mimicking human visual capabilities. Working on a computer vision project involves using the latest technologies to create real-world applications. There are countless opportunities, whether it’s improving security systems, making shopping better, or helping self-driving cars.
Bringing your computer vision project to life requires careful planning and the right toolbox. This guide will walk you through every step of a computer vision project and give you the best resources along the way. Whether you’re a beginner hacking away at a 24-hour hackathon, a startup founder building a facial recognition attendance system, or a developer working on the next big computer vision project at a tech giant, this guide is here for you.
Introduction
While taking on a computer vision project, it’s important to stay organized and follow a structured approach. By doing so, you can manage different tasks and resources to keep your project on track. We will discuss everything you need to know, from the basics of computer vision techniques to model evaluation and deploying your project. So, let’s dive in and get started!
Step 1: Understanding the Basics of Computer Vision
The first step to starting a computer vision project is analyzing the requirements of your problem statement. Having a clear understanding of the requirements, let’s you make better decisions about which computer vision techniques to use in your computer vision project.
Before we take a look at different computer vision techniques, it’s essential to understand the underlying power that drives computer vision — deep learning. Deep learning is a branch of machine learning inspired by the structure and function of the human brain. It uses artificial neural networks with multiple hidden layers to learn intricate patterns directly from data, eliminating the need for extensive manual feature engineering.
Convolutional Neural Networks (CNNs) are a particular type of deep neural network architecture specifically designed to analyze visual data like images and videos. CNNs can automatically extract meaningful features from visual data, allowing them to accomplish complex computer vision techniques.
Deep learning is fundamental for computer vision tasks. If you’re interested in learning more about it, check out Andrew Ng’s YouTube playlist on deep learning.
There are four main computer vision techniques or tasks that allow computer vision to accurately distinguish objects in an image or video: image classification, object detection, semantic segmentation, and instance segmentation. Let’s take a closer look at these tasks with the help of an example.
Image Classification
Image classification is used to determine what category an object in a picture falls into. For example, the categories or classes could be cats, dogs, or cows, like in the image above. Image classification works by analyzing the features of an image using algorithms. These algorithms, part of deep learning, learn from thousands of examples of what different objects look like.
When you give the system a new image, it compares its features — like shapes, colors, and textures — with what it has learned. Then, it predicts the category the image most likely belongs to based on similarities.
Object Detection
Object detection helps us figure out not just what’s in a picture but also where it is. It’s a bit like image classification, where computers learn to spot different things in photos, like cats, dogs, or cows, by looking at their shapes, colors, and textures. But object detection goes one step further. It learns to find exactly where those things are in a picture.
These algorithms are trained on vast datasets with images labeled with annotations outlining where each object is. When presented with a new image, the system uses what it has learned to identify objects and their positions. It draws bounding boxes around each detected object and labels them with the category it believes they belong to.
Semantic Segmentation
Semantic segmentation precisely outlines each object in the image. This process involves the algorithm dividing the whole image into segments, or “pixels,” and classifying each pixel into a specific category. For example, in an image that includes cats, dogs, or cows, semantic segmentation doesn’t just recognize these animals; it also traces the exact shape of each animal by assigning each pixel within the shape to the appropriate category.
Instance Segmentation
While semantic segmentation labels every pixel in an image as belonging to a certain category, instance segmentation goes a step further by distinguishing between different instances of the same category.
For example, in an image with several animals, instance segmentation not only identifies pixels that belong to cats, dogs, or cows but also differentiates between one dog and another dog in the same image. This is crucial in scenarios where understanding the individual entities is important, like counting the number of animals or tracking their movements.
Here are some resources to learn about computer vision techniques:
- Learn the basics of AI and computer vision on Tutorials Point.
- Discover tutorials on the Computer Vision Engineer YouTube Channel.
- Check out these free computer vision courses on Udemy.
- Explore this article on the best Computer Vision books.
Step 2: Dataset Collection
After deciding what computer vision technique to use, we need to consider data.
Computer vision projects depend on high-quality models. To build these models, you need excellent datasets filled with top-notch data. A dataset is a collection of various data stored in a digital format. In computer vision, data often means images and videos. Data forms the core foundation of any computer vision project.
You can find and download outstanding datasets from several key sources:
To create the best models, it is important to have a large amount of high-quality and diverse data. If low-quality data is fed into the model, it will produce similarly flawed results. As the saying goes, “Garbage In, Garbage Out (GIGO).”
Always ensure to review the data you collect to remove duplicates and inaccuracies, such as wrong data. After cleaning your data, the next step is storage and management. It’s important to organize and store your data securely and accessibly. You can store data online (in the cloud) or offline. Each option uses different frameworks. Offline storage relies on batch cluster frameworks. Online storage uses frameworks like PyTorch and TensorFlow.
After successfully collecting, reviewing, and storing the data you want to use for your computer vision project, you can create a dataset by annotating and labeling the data.
Step 3: Data Annotation and Labeling
There are two methods to create custom datasets. You can combine existing datasets or label and annotate data collected specifically for your project. We will focus on the latter. Labeling and annotating data is crucial for training computer vision models. It involves labeling elements within the ‘training data.’ Labeling improves model accuracy by guiding computer vision algorithms to make precise and detailed predictions based on the data’s context.
For annotating images, we use methods like drawing boxes around objects, tagging them, and categorizing the images. Annotation tools like Annotab Studio and CVAT make this job easier because they are designed to be user-friendly. These tools have simple interfaces that are easy to learn and use.
If you want to learn more, check out this article on how to manage data annotation projects effectively.
Let’s take a look at some of the annotation techniques side by side:
You can also use tools like PyTorch, which makes it easy to create custom datasets. They provide many functions and classes to organize, load, and preprocess your data. If you’d like to learn more, check out this tutorial on how to write custom datasets with PyTorch.
Step 4: Model Selection and Training
After you’ve created a dataset to train a model with, you need to select a model and train it. When choosing a model for your project, you need to consider several factors, such as the type of project, the dataset, and the model type. However, the decision can often be simplified by focusing on just two key factors: the purpose of selecting the model and its performance. Let’s walk through the different steps behind selecting a model and training it.
Formulate the Problem
You’ve already gathered your requirements and selected a computer vision technique, which sets the stage for selecting the right model. For example, object detection is your go-to technique if you’re working on a project where you need to spot and count different fruits on a conveyor belt. This method helps you identify fruits like apples, bananas, and oranges. It also shows where each fruit is and how many there are. So, we are looking for an object detection model.
Choose Potential Models
Next, explore different models that fit your project’s needs. Your choices might range from simpler models like decision trees or linear regression to more complex ones like deep neural networks. Consider your dataset’s nature and the problem’s complexity. For tasks centered on image and video data, like image recognition, deep learning models like Convolutional Neural Networks (CNN) are more effective.
YOLO (You Only Look Once) models are also excellent for image data. Explore tools like the TensorFlow Object Detection API, which offers pre-trained models and resources for various object detection challenges. Kaggle is another resource for finding and deploying pre-trained models suited to your needs.
Hyper-Parameter Tuning
After selecting your models, it’s time to fine-tune them. Adjust hyperparameters such as the learning rate and regularization strength. These adjustments help improve your model’s accuracy while preventing issues like overfitting or underfitting. Hyperparameter tuning is crucial for controlling how your model learns, significantly affecting its outcomes. You can learn more about hyper-parameter tuning here.
Train Each Model
The next step is to train each model with a subset of your data (the training data) and check its performance to see how well it works.
This helps you understand how each model performs on your specific dataset and allows you to compare their performance. Once you’ve trained the models on the training data, you can evaluate them and pick the best one. We’ll learn more about the process in the next step.
Step 5: Model Evaluation and Iteration
Model evaluation is a critical step that uses different metrics to assess the performance of a model. There are several evaluation metrics used in model evaluation, including precision, recall, and F1 score.
Precision measures the ratio of true positives to the sum of true positives and false positives, focusing on the accuracy of positive predictions. However, it does not consider true negatives and false negatives. Recall, on the other hand, calculates the ratio of true positives to the sum of true positives and false negatives, emphasizing the number of correct positive samples. Yet, recall can lead to a higher false positive rate.
The F1 score is a metric that combines precision and recall into a single value, representing the harmonic mean of the two. It addresses the trade-off between precision and recall, where an increase in one often leads to a decrease in the other. The goal of the F1 score is to find a balance between precision and recall, providing a good measure of a model’s performance.
There are many tools available for model evaluation, such as COCO Eval API, Scale Validate, and Why Labs, which help simplify the evaluation process and provide insights into the model’s performance. Check out this blog to learn more about model performance evaluation.
Once you’ve evaluated the models based on their performance and accuracy, choose the one that performs best for your project. This model can then be used to make predictions on new data.
After evaluating your model, you’re ready for deployment.
Step 6: Deployment and Integration
Congratulations, you’re ready to deploy the model! It’s the most crucial phase, marking the integration of trained models into production systems for real-world decision-making applications. This process involves several key steps to make sure the model can operate effectively and efficiently.
First, the trained model must be saved in a compatible format for deployment. Next, a suitable deployment environment must be chosen, such as cloud platforms or on-premises infrastructure.
You can also deploy your models on edge devices, which involves deploying models directly on local devices for local processing to get faster results. Keep in mind, it is only possible if the overall size of the model is small enough for a small edge computing device.
Then, the model and its dependencies are containerized, typically using platforms like Docker containers, to ensure portability. Following containerization, the model can be deployed. Monitoring mechanisms are then implemented to track the model’s performance in real-time, with alerts to detect any issues.
A scaling strategy can also be implemented to accommodate increased demand. Additionally, a continuous integration and deployment (CI/CD) pipeline is established to automate deployment, testing, version control, and updates to your model.
Once you’ve deployed your model, monitor it for maintenance. Post-deployment maintenance involves continuous evaluation of the model’s performance, monitoring data quality and drift, and making improvements based on new data and feedback.
Also, make sure to check out this article to learn more about model deployment. Take a look at these blogs on how to use and deploy models using platforms like Amazon Sagemaker and TensorFlow Serving.
Conclusion
In this article, we’ve covered the basics of computer vision and object detection, from understanding the core concepts to exploring deployment options. We’ve also provided a list of courses and resources to help you along the way. Computer vision may seem complex, but with the right mindset and advanced tools, like Annotab Studio and WhyLabs, it can be both manageable and exciting to learn. If you’ve read this far, you’ve taken an essential step in your learning journey. Congratulations, and keep up the great work!
Call to Action (CTA)
If you’re interested in taking your computer vision projects further, take a look at an article on instance segmentation. It’s filled with detailed insights that could really boost your creativity and understanding.
FAQs
- Where can I learn more about computer vision? If you want to learn more about computer vision and its tasks, you can check some of the courses offered by edu-platforms like Udemy, Coursera, and edX. They offer various free and paid courses that cover almost everything from the very basics of computer vision to advanced, in-depth studies of certain models. You can also check out the freecodecamp course on deep learning for computer vision, which goes into computer vision concepts in depth.
- Where can I get Pre-Trained models? You can get your hands on pre-trained models in several ways. Frameworks like Tensorflow and PyTorch provide high-quality pre-trained models for your computer vision projects. Another excellent choice is the YOLO models that are pre-trained on the COCO dataset. You can even use the latest zero-shot YOLO-World Model, which can use your input prompts as labels for detection. You can also get free datasets and pre-trained models from Roboflow.
