Data is considered to be the propelling factor behind every successful AI model. For computer vision fields, image datasets are important since they form the training base for machine learning algorithms.
Computer vision datasets, including image datasets, are the backbone for training computer vision models. With large datasets of images to analyze, AI models learn pretty well how to identify objects, detect patterns in them, and make predictions. Thus, the more diverse and inclusive a dataset is, the better the model gets at making its performance generalize to new, unseen data.
For instance, a facial recognition model trained on robust face datasets can identify faces with different angles of view, under different lighting conditions, and even with partial occlusions. Similarly, in the medical domain, image datasets allow AI models to detect abnormalities in radiographs and other imaging modalities.
Meanwhile, the quality, size, and diversity of the dataset have a direct impact on the model's capability for generalization across a wide range of use cases. In ML, the larger and more diverse the data, the better features can be learned to perform accurate predictions without overfitting. Conversely, biased or not sufficiently diverse datasets result in suboptimal models that contain errors or misclassifications.
Whether your model is for what, the quality and diversity of the dataset contribute much to determining the accuracy of the final model. Before the collection of image datasets, there has to be a clear definition of the problem your AI model will be solving.
First, understand the specific task for which your model is supposed to perform. Some models, like object detection models, might need the location of each object in an image to be annotated while image classification just needs labeled images of different classes. The same goes for segmentation. This requires pixel-level annotation for separating different regions in an image.
Once the task is defined, key attributes such as image resolution, file format, and size become important. A problem might want images in high definition for detailed recognition of objects or smaller in size for less computationally intensive processing. Other attributes that may influence dataset choice will be color, light conditions, and number of classes.
This determines the volume required for a particular task. Simple classification tasks can be done with a few thousand images, while complex tasks like face recognition or medical imaging require millions of labeled images to attain high accuracy and generalization. It is also important to mention that gathering too few images results in overfitting: where the model performs well on the training data but does poorly on new, unseen data.
Image datasets can be gathered for the AI model in a lot of ways. Each has its pros and cons.
Thousands of open datasets regarding images are available for different AI purposes. A few of the most popular include ImageNet, COCO (Common Objects in Context), and MNIST.
They are readily available and accessible. Many of them are pre-labeled, hence reducing the costs of annotations. However, they may not fit into specific use cases. Not diverse under some categories.
Other specialized image data can be obtained by web scraping. You can use Python's BeautifulSoup or Selenium to gather images from websites, social media, or search engines.
The legal and ethical considerations are crucial in this method. Be sure that there is compliance with copyright laws and website terms of service. Several websites ban scraping, while making use of copyrighted pictures without permission may have one face legal consequences.
You can outsource AI data collection and labeling through services such as Amazon Mechanical Turk, Appen, or Figure Eight. You can create tasks of uploading images or labeling already existing images.
When real data is difficult to obtain, you can generate synthetic data through simulation software or Generative Adversarial Networks. For instance, GANs generate photorealistic images by learning from preexisting datasets.
Data collection is performed through hardware sensors and cameras in domains like aerial imagery-drones, or medical imagery-special devices.
At the same time, data collection from sensors and cameras can be performed in several ways. Make sure to ensure proper calibration of the cameras, as well as control over the environmental factors, for homogeneous quality of the images.
These are the companies that come in when one needs AI data to feed ML models. Examples include Surfing AI, a company providing high-quality datasets with related services.
The quality of your labels is directly related to the performance of your AI model: poorly labeled or inconsistent data can easily result in misclassifications or poor performance by a model.
Tools like LabelImg, RectLabel, or Supervisely will have you manually draw bounding boxes or segment images. It is really accurate, but at the same time, it is very time-consuming and needs immense manpower.
The common annotation issues are, where there are subjective decisions on, for example, where an object stops and another one begins, scenes are complex in the sense of containing an awful lot of objects, and there are several time-consuming human annotations.
On the other hand, automatic tools provide much assistance in labeling the images, especially for large data sets. One can bootstrap labeling using semi-supervised learning or transfer learning methodologies, but it still needs manual verification.
If the datasets are not diverse, then AI models are prejudiced. For example, if an AI is trained from images of only one location, then the model will fail to generalize the results to other locations. Take extra care that your data set has representations of images taken from different demographic, geographic, and environmental backgrounds.
Image augmentation can increase dataset diversity. Through rotation, flipping, or other changes in brightness, this way will help increase the variety in a dataset without necessarily collecting more data.
The cleaning involves the removal of irrelevant images or those that are too blurry or even duplicated from your dataset. You could do this through manual inspection or via an algorithmic filter that removes them for you.
After data cleaning, you should focus on quality control. It involves manual reviews and algorithmic checks, such as the detection of anomalies, that make sure there are no errors in the dataset and that the images are up to standard.
Image data collection is a multistep process that needs scrutiny right from problem formulation to quality assurance. Merging open-source datasets with synthetic and real-world data collection methods, along with proper labeling and diversity in a dataset, builds a strong dataset to power high-performing AI models.
Image datasets can be gathered from open-source repositories, by web scraping, through crowdsourcing, synthetic data generation, specialized hardware, or buying image datasets from AI data providers.
Data collection has many shapes and forms, which can take the form of open datasets, custom collection, synthetic data generation, or in partnership with AI data providers.
This may also have legal implications, as scraping on some websites is prohibited, and images can be copyrighted. Never forget to respect the website's terms of service and any copyrights.
A diverse dataset would contain all types of conditions, demographics, and settings. Monitor for possible biases and gaps in your dataset on a regular basis.
