Using Datature’s Intellibrush for Advanced and Efficient Data Labelling
Historically, computer vision tasks have relied heavily on human-annotated data. It is no secret that machine learning can only generate accurate results if the data input and labelling is accurate as well — no matter how fancy or advanced a computer vision model is, it will be difficult to achieve good performance if the dataset we are training on is lacking. Specifically, computer vision needs a lot of data. However, manual labelling is a time-consuming and error-prone process — particularly for computer vision tasks such as object detection.
Object detection is one of the most important tasks in computer vision — it refers to the detection and localization of objects using bounding boxes. Object detection generally involves detecting objects in an image and classifying them into different categories. This task is important for many applications such as security, surveillance, and automotive.
There are many different methods for object detection. The most common method is to use a set of training images. This is where a computer is shown a series of images with objects in them and is then able to learn to identify the objects. Another popular method is to use a deep learning approach. This is where a computer is given a large dataset of images and is then able to learn to identify the objects in them. The most important feature of any object detection system is that it is able to accurately identify the objects in an image — which means that the model needs to be able to distinguish between different objects, even if they are of the same type (i.e., different dog breeds, even if all the images show dogs).
There are a number of limitations to manual data labelling that make it an inefficient process. First, it is labour intensive and time-consuming, which can lead to high costs. It is also difficult to create consistent labels across a dataset. Specifically, one of the main limitations of manual labeling is the amount of time it can take. For large datasets, it can be very time-consuming to manually label all of the data, and it requires effort to properly draw polygons for tasks such as object detection.
Second, it is prone to error, as human annotators can make mistakes when manually label data. Additionally, it can be difficult to accurately label data, especially if there are a lot of classes or if the data is very complex. Manual data labelling is often inconsistent, as different annotators may label the same data differently. Especially if annotators are outsourced, additional training may be necessary for accurate labels — some datasets require domain expertise, particularly those in the medical and manufacturing fields.
Finally, outsourcing labelling can get very expensive and inefficient. Manual data labelling does not scale well, as the amount of data that can be labelled manually is limited, and it can also be very expensive if researchers need to hire people to do manual annotation.
Most AI-assisted labeling tools work by first extracting features from an image, then using those features to train a machine learning model. Once the model is trained, it can be used to label new images.
There are many different ways to extract features from images, but some of the most common methods include using convolutional neural networks (CNNs) or transfer learning. CNNs are a type of artificial neural network that are designed to mimic the way the human brain processes information, while transfer learning is a method of machine learning where knowledge gained from one task is applied to another similar task.
Both CNNs and transfer learning can be used to train machine learning models for image labelling. However, CNNs tend to be more accurate than transfer learning, as they are specifically designed for image processing.
There are a number of different AI-assisted labeling tools available, and the accuracy of each tool will vary depending on the dataset it is trained on. However, all AI-assisted labeling tools can be used to label images more accurately than humans.
Model-assisted labelling is a relatively new technique in the field of computer vision that has seen rapid uptake in recent years. The increasing interest in model-assisted labelling methods is in part because this method can provide highly accurate labels for data sets with a relatively small amount of human intervention —potentially solving for the challenges that we mentioned earlier, namely efficiency and accuracy.
As described earlier, the traditional method of annotating data sets for computer vision purposes is typically through manual labelling or outsourcing. This process is time consuming and expensive, as it requires skilled workers to carefully label each image. Although there are other methods for more efficient data labelling in domains such as computer vision and natural language processing, like active learning, these methods are still incredibly data-hungry. Model-assisted labelling, on the other hand, uses a computer model to automatically generate labels for a data set. Model-assisted labelling is a method of computer vision in which a model is used to aid in the identification of objects in an image. Particularly, this method uses trained AI models to label existing data.
One of the more popular model-assisted labelling methods involves training a large neural network on a labelled data set — in other words, by using deep learning to generate labels for new data sets with a high degree of accuracy. Deep learning models are not the only type of model that can be used for model-assisted labelling. Other methods include support vector machines and decision trees. However, deep learning models are generally considered to be the best performing.
Labelling can now be done in a fraction of the time and at a fraction of the cost of manual labeling. The broad idea of model-assisted labelling is that the data scientist would train the AI in parallel with the labelling — and thus, as the model starts to see a generalizable pattern in the data, the model itself will suggest labels for the researcher. This can be done by providing labels for the objects in the image, or by providing a set of training data that can be used to train a machine learning model. Thus, we see that model-assisted labelling can be used not only to improve the accuracy of object detection tasks, but also to speed up the process of labelling images.
However, it is important to note that the accuracy of model-assisted labelling depends on the quality of the data used — earlier models were not very accurate due to lack of annotated data, but the performance of these CV models have improved rapidly in recent years. The performance improvement of CV models can be attributed both to the increasing availability of powerful GPUs, the development of new neural network architectures, as well as wider access to annotated data.
The rise of AI-assisted labelling in computer vision is something that has been on the horizon for some time now. With the increasing popularity of deep learning and the ever-growing amount of data that is available, the ability to train models to automatically label images is becoming more and more practical. There are a number of different applications for this technology, including providing labels for training data sets, helping to identify objects in images, and even automatically generating descriptions of images.
AI-assisted labelling is a process of adding labels to images to help identify objects within the image — just like model-assisted labelling, this can be done either manually by a person, or automatically by a computer. One of the key benefits of AI-assisted labelling is that it can help to reduce the amount of time and resources that are required to label data sets by hand. This is particularly valuable for large data sets, which can take a significant amount of time to label manually. In addition, it can also help to improve the accuracy of labels, as machine learning models can often identify patterns that humans may not be able to see.
There are a number of different ways in which AI-assisted labelling can be used in computer vision. One common approach is to use a pre-trained model that has been designed for a similar task. For example, there are a few different image classification models that can be used to label images of objects. Another approach is to train a model from scratch on a specific data set. This can be more time-consuming but can often provide more accurate results, particularly if given context-specific datasets.
Compared to model-assisted labelling, AI-assisted labelling requires no pre-training —specifically using models like DEXTR that works by placing markers on the edges of the items you want to label.
Regardless of the approach that is used, the goal of AI-assisted labelling is to reduce the amount of time and resources that are required to label datasets by hand. This methodology is becoming more and more important as the relative sizes of datasets continue to grow, and as the need for more accurate annotation increases.
IntelliBrush is a Datature’s AI-guided image labeling tool that enables users to make pixel-accurate annotations of complex images. It’s easy to use, flexible, and does not require any pre-training of models. As someone who is just getting started in the CV field, I really enjoyed using IntelliBrush to help me with image labelling and object detection.
To get started, sign up for your own Datature account here, then you can start using IntelliBrush too. After uploading your dataset and any already-annotated labels, select the IntelliBrush tool on the right-hand panel or by pressing
T on the keyboard.
You can additionally specify the level of granularity you want depending on the kind of image that you are labelling by using Intelli-Settings. The two common settings are
Full-Image. The former is better suited if your image has multiple objects of interest within frame, while the latter (which is what I used more often) is recommended if your image has 1–2 main objects of interest.
Object detection is a vital task in computer vision and is used in a variety of applications. It is important to choose an appropriate method for object detection and to use high-quality training data. The object detection system also needs to be able to run in real-time.
There are a few reasons why manual labelling and outsourced labelling may not be the best means to label data currently. Manual labelling is time-consuming and tedious. Additionally, scaling this labelling process is difficult, and is often associated with high costs, or necessitates expert knowledge. Because manual labelling relies on human intervention and expertise, it often is also subject to human error and inconsistencies.
There are many different factors that can affect the accuracy of an object detection system. The most important factor is the quality of the training data. If the training data is of poor quality, then the system will not be able to learn to accurately identify the objects in an image. The other important factor is the algorithm that is used for object detection. There are many different algorithms that can be used, and each has its own strengths and weaknesses. It is important to choose an algorithm that is well suited to the task at hand. Finally, the object detection system needs to be able to run in real-time. This means that it needs to be able to process images very quickly and accurately identify the objects in them.
IntelliBrush solves for these challenges, and creates pixel-perfect data labelling with just a few clicks — efficient, accurate, and intuitive to use. There are a few key benefits of Datature’s IntelliBrush platform that made it really great for me to use:
- It works out of the box. I didn’t have to do a ton of setup and there wasn’t too much platform onboarding that required me to add my personal information on the website. Surprisingly, there also wasn’t any coding required — typically, I have used a lot of code for my CV projects, so it was refreshing to be able to use a platform that allowed the same efficient and accurate results, but with half the effort.
- Responsive annotations. One feature I loved about IntelliBrush is that the user interface allowed me to pan, zoom, and even hide unwanted classes in the same window — things that would naturally be a bit more challenging with just traditional code.
- Fast and intuitive. Unlike other platforms that I had to take a lot of time out to study before using, IntelliBrush allowed me to select and deselect classes in a few clicks — no rigorous training required.
- Tunable and configurable. I was also able to specify different granularity levels when I was labelling images — whether those images be corgi images or food images, or any other dataset.,
- Adaptive Learning. The more you use IntelliBrush, the smarter it gets and the more precise it gets.
- Multi-Object Labelling. As shown in some of the examples above, I was able to label multiple objects in a single image accurately and efficiently.
- Tag Filtering. It was also incredibly easy to declutter annotations by filtering tags that I was interested — this then allowed me to train my neural network just on specific classes, labels, or tags.