
The art of constructing deep learning models to solve numerous Computer Vision projects has gained immense popularity ever since deep neural networks won the ISBI challenge on segmenting neuronal networks. Over the past decade, the majority of the significant computer vision competitions have been successfully conquered by deep learning.
With this rapid progression in the field of deep learning with new methodologies and technologies discovered each passing year, it becomes essential to stay updated with these massive trends. While most data science enthusiasts and aspirants might want to construct and build some unique computer vision applications and projects with the best set of deep learning tools, they might, unfortunately, be able to capitalize on the best opportunities.
In this article, we will discuss a few significant computer vision tasks, namely object classification and detection, and instance segmentation. Once we understand the basics of these topics, we will discuss how we can accomplish these projects with the help of one of the best platforms available for these tasks. A major contributing factor to achieve most deep learning tasks is GPU. To understand and explore more about the significance of GPU, check out the following article from the link provided below.
Crucial Computer Vision Applications:

As the shine of computer vision begins to sparkle with each progression made in the fields of deep learning and computer vision, there are several modern computer vision projects that are in demand in the modern world. Projects such as the classification of cats and dogs, face recognition, emotion and gesture recognition, object detection, and segmentation tasks are highly renowned.
In this section of the article, we will understand and cover three major applications of computer vision that are considered to be the most significant ones, namely classification tasks, object detection, and instance segmentation. Without further ado, let us explore these basic concepts and understand an overview of the following.

1. Classification:
Image classification is one of the most notable computer vision operations that is performed. A common classification image processing task could include something as simple as classifying between dogs or cats or classifying between numerous dog breeds. Image classification is the most basic task that we tend to perform on images or even videos. Most image classification tasks can be performed with convolutional neural networks, transfer learning models, and other similar projects.
2. Object Detection:
Object detection is one of the most vital tasks of computer vision. There have been several algorithms that have been continuously developed for decades to approach the following problem. However, it wasn’t until recently (almost a decade ago) that the task garnered more traction. The purpose of object detection in simple terms is to create bounding boxes around the desired object location.
3. Instance Segmentation:
The final significant computer vision operation that we will discuss in this section of the article is segmentation. Segmentation is used to deduce individual entities in an image. With the help of the segmentation techniques, you can segregate the essential elements accordingly. While semantic segmentation tries to identify the role of each pixel in a given particular image, instance segmentation tends to identify the labels of each class.
Discussing The Best Platform For These Tasks:

One of the best platforms for achieving these tasks mentioned in the previous section while accomplishing the best possible results is Datature. To solve these problems, the time required for constructing the respective deep learning models and building them from scratch is quite hard. However, with the help of this end-to-end platform, you can successfully annotate, train and deploy your models without incurring any technical debt.
Upon entering the Nexus website, you can sign up for a free plan which is more than enough to get started on your computer vision journey. The free plan is perfect for teams exploring MLOps tools, students, researchers and Data Science enthusiasts looking to take a deep dive into computer vision. Some highlights of the free plan include; (i) access to augmentations library (ii) web-based annotator and (iii) complimentary GPU compute time.
Data Pre-processing:

Once you’re logged in, you can easily proceed to create a new project from scratch with their drag-and-drop editor to upload your images and annotations. Annotations are supported in a variety of formats so there’s no issue importing datasets from OpenCV. You can then proceed to annotate your images using their web-based annotator that supports the drawing of bounding boxes, polygons and masks.
Training your Neural Network Model:
After annotating the images, you can then proceed to build your workflow in just a few clicks.

The platform provides you with numerous options to construct different types of models. Some examples of models you can build are RetinaNet, Faster R-CNN, Mask R-CNN, and EfficientDet models with support for hyperparameter tuning as well. You can select one of their four available GPU models and proceed to train the network. Once the neural network is initialized, you can monitor the progress rapidly on the Neural monitor. Depending on the size of your dataset, your model may take as fast as 20 minutes to train and once the model is generated, you are ready to perform predictions tasks.

Visualizing Model Inferences:

One of the critical issues that most developers face during the process of constructing any deep learning project to solve computer vision tasks is the high amount of code they need to write. Once you finish training a model successfully, you then have to write more huge chunks of code just to ensure the mode can make appropriate predictions and can be successfully deployed.
This procedure could be quite tedious for most developers as they have to tackle these situations every time they build a new deep learning model. However, Datature offers its very own open-source Portal library that helps to overcome most of the common difficulties faced by data science enthusiasts. Portal is a game-changer as it allows the developers to load and visualize models in the fastest way possible.
Portal is also quite easy to utilize for most beginner-level developers. With a few steps, you can perform all the desired actions that are required. Portal currently supports both TensorFlow and DarkNet models, while PyTorch support is planned to be released in the near future. The basic steps involved in using the Portal library after its installation is to register and load your desired models accordingly. Once these steps are completed, you can add your images or videos and make the appropriate predictions with ease.

To summarize the steps to visualize the predictions with Portal is as follows:
- Launch Portal
- Register Model (locally or using Datature Nexus)
- Load assets (images / videos)
- Watch model inferences in real-time
Additional features are as follows:
- Class Filtering
- Setting IoU thresholds
- Running video inferences
- Selecting video frame intervals
Conclusion:

The rise of deep learning and computer vision in the modern era is indisputable. While there are several approaches to solve a variety of computer vision tasks, developers often face several issues trying to find the best approach. Having a proper MLOps pipeline allows teams to effectively launch and iterate on their models when it comes to building the model from scratch and deploying them. In this article, we discussed the best platform for achieving both these tasks with relative ease.
The Datature platform is a fantastic place for most of the essential computer vision projects. Apart from allowing you to construct numerous models without the requirement of coding and just visual workflows, the annotations are also fully monitored and managed online. The training process is completed on the cloud and the users without access to GPU support can largely benefit from these features.
The best part about the platform is that you can not only construct and train the models, but you can also use their open-source Portal library to register and load the models that you have constructed to make the appropriate predictions on your images or video content. Doing so will significantly reduce the computational work required for developers and enables them to perform tasks more effectively. Looking to get started? Check out this 5 minute tutorial video and launch your first computer vision project today!
If you have any queries related to the various points stated in this article, then feel free to let me know in the comments below. I will try to get back to you with a response as soon as possible.
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Thank you all for sticking on till the end. I hope all of you enjoyed reading the article. Wish you all a wonderful day!