SentiSight.ai offers three different image recognition model types, single-label classification, multi-label classification and object detection, all of which have similarities as well as differences, with each of them excelling at different types of tasks. While the three models can be used to classify the content within images, the approach they undertake is dependent on the task aims and envisioned results. This article defines key similarities and differences between the 3 models, as well as providing examples of the use cases of each model, to help you to decide which model type is needed for your project requirements.
SentiSight AI image recognition software provides 2 types of classification: single-label classification and multi-label classification. Both models adhere to the same recognition principle which assigns one or few concepts or “tags” to an image. A single-label classification model can recognise a single object (for example, certain type of fruit, animal, or specific electronic device) or an abstract concept (such as a season of the year, animal taxonomy or a mood of a person) in an image. On the other hand, a multi-label classification model allows recognition of multiple objects or concepts in one image. Therefore, it can recognize a number of different physical objects, such as fruits, vegetables, or different electronic devices within one image. It could also recognize more than one abstract concept in the same image, for example, a mood and a gender of a person.
Object detection is another AI image recognition model offered by SentiSight.ai. Similar to the multi-label classification model, object detection can identify a number of different objects in an image. However, unlike the multi-label classification model, object detection also identifies the exact location of each object and marks it with a dedicated rectangular box. Moreover, object detection models can identify several instances of the same type of objects (for example several apples in an image) and can even be used for counting these instances, which is not possible by multi-label classification. However, object detection models fall short to identify some of the abstract concepts, which we will discuss later.
Each SentiSight image recognition model has certain strengths and limitations that are important to consider when choosing which model to work with.
Single-label classification models are best suited for when there is a single object or concept in an image and this object/concept occupies most of the space. Additionally, this model is best applied when the background is uniform/insignificant and persists in all images within the dataset to be classified.
For example, single-label models are very well suited to a type of fruit (apple, orange, kiwi, pineapple, etc.) and if there is always just a single fruit with uniform background in an image. It is also very useful to determine a single concept in an image, for example a room type (living-room, kitchen, bedroom, etc.).
In some cases, a single-label classification model can even be used if the user is seeking to detect multiple objects or concepts in an image. In order to achieve this, a single-label classifier can be used to detect whether a certain object or concept (e.g. pineapple) is present in the specified image or not. This process can be run multiple times in order to detect several concepts within a dataset of images, with different models being applicable for different concepts. In this case, each concept requires a dedicated single-label model. Although reliance on this model might require more time compared to a multi-label model, the results can sometimes be more accurate.
When training a single-label model to determine whether a particular object or concept is present in an image, it is important to distinguish between the object/concept of interest and the background. Images that do not contain the desired object/concept should be labelled as “background”. Users must then train a single-label model that can differentiate between the two classes, a process that requires to be repeated for each concept of interest. To learn more about training a single-label classification model to distinguish one or more non-exclusive concepts in an image watch our video tutorial.
As indicated in the previous section, SentiSight’s multi-label classification model has certain advantages compared to the single-label model. This model is designed to detect multiple object or concept classes within a set of images in a relatively short period of time. In other words, this model works faster than running the single-label model multiple times.
Compared to object detection models, multi-label classification model has an advantage of being able to recognize multiple abstract concepts within an image. For example, it can be utilised to separate abstract image settings (such as summer/autumn/winter/spring, outside/inside, cloudy/sunny). Such abstract concepts do not stand for specific objects per se. Therefore, they cannot be identified using object detection models.
Comparing object detection models with single-label and multi-label classification models, a few important differences are noteworthy. Object detection model performs much better than both types of classification models when the objects of interest within the image are small and the image contains a lot of background objects. Moreover, object detection models allow for the possibility to detect several instances of the same object and count those that are present which is not possible to do using the classification models.
Despite its similarity to multi-label classifications, object detection is designed to mark specific objects’ locations within images and present more accurate results with an additional layer of identification. This model is more convenient in situations where the purpose underpinning the detection is to detect and highlight a specific object.
Another important aspect when comparing multi-label classification and object detection models is speed. Although the prediction time for object detection models is very similar to multi-label classification models, the training time varies. Object detection models require significantly longer time for training than multi-label classification models. Moreover, the image labelling process for object detection models takes longer since it requires marking each object of interest with a bounding box.
Therefore, although multi-label classification and object detection models have similarities, certain projects would require using one over the other. This is dependent on the aim of the task.
Each SentiSight AI image recognition model can be utilised for a different set of projects. The following paragraphs will provide a few examples of how each model can be employed in various fields.
An example use-case for a single-label model could be defect detection of products. This is most commonly used in factories by manufacturers in order to reduce human error and help speed up the manufacturing process at an efficient cost. A single-label model will be best suited in this case when an image is taken of the object without much background present and there is no need to localize the defect. Otherwise, object detection model might be more suitable.
Also, single-label models could be used in museums or science centres. The web-based tool can be used to separate various animals or plants’ species and sort image collections accordingly. Therefore, this model could significantly ease the process of visual database management or help to retrieve the best images for specific purposes.
Multi-label classification models can be used to manage large image databases for various purposes. For example, multi-label labels are most suited to adding automatic hashtags for photos. These hashtags can later be used to quickly filter through relevant images with the same hashtag or even group photos with certain hashtags. This application is prevalent across social media networks, such as Instagram and Twitter, or any website that contains a vast amount of user uploaded images.
Multi-label models can be used for content moderation. For example, this tool can scan large amounts of content in social media or other sites to identify inappropriate visual content such as guns, drugs, or nudity, and identify multiple concepts of these examples with one trained model. Thus, multi-label models highlight the content for website moderators, and speed up the process of content removal/moderation.
SentiSight.ai object detection models can be very useful in the medical field. For example, this model can be trained to spot different types of cancer in MRI scans and improve the efficiency of early diagnosis. The ability to mark the exact location of the irregularities and relatively accurate object labelling (compared to multi-label classification model) would be crucial for doctors or medical students.
Another field where object detection models could be highly beneficial is agriculture. This model could be used in large agricultural farms to spot plant diseases, insects, and worms as well as indicate ripe crops. Therefore, this software can facilitate the management of large farms by eliminating the need to check every plant individually.
To sum up, the 3 SentiSight AI image recognition models discussed in this article have very distinctive yet useful features. This article highlighted the key differences of these models, which revolve around performance, accuracy, and speed. These features are particularly important when choosing the best SentiSight.ai image recognition model for the fulfilment of certain tasks and projects.