An image classification dataset can be considered to be the fuel that runs an image classification system. A model is trained with an image classification dataset; thus, having a high-quality training dataset is imperative to get accurate and speedy results. Using a good-quality dataset also ensures optimal resource utilization. But unreliable data can drastically affect the efficacy of the image classification model.
Image classification applications are used in many applications, from traffic control, disaster recovery, drone operation, medical diagnoses, and more. Hence the image classification datasets too are varied and can be collected from multiple varied domains and industries:
- Art
- Agriculture
- Automobile and Advanced Driver Assistance Systems
- Fashion
- Food and Groceries
- Wildlife
- Sports
- Satellite Imaging
- Medical Imaging and Healthcare
- Security and Surveillance
- Scene type understanding
And many more
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Image Datasets and Photo Datasets
What are image classification labels?
Image classification datasets heavily rely on the concept of labels. Determining the right labels that fit your purpose is the first step to preparing your image classification datasets. The labels are picked depending on the classification goals.
For instance, if you want to identify balls and bats from your input images, your labels should also be centered around these objects. Three key aspects need to be considered when choosing the labels for your image classification dataset, namely:
- The granularity of the label – The level of detail you seek
- The number of labels – The higher the number of labels, the more complex the model could be
- The parts of the image that correspond to the chosen labels.
Here are some ways to ensure gathering a good image classification dataset.
- Determine the granularity of the label early on. This decision depends on how detailed you want your results to be. For instance, you could design a model to detect cars from a picture. A more advanced model can also be designed to detect cars along with the model or brand type of the car. The advanced model would require a higher level of granularity, of course.
- Keeping the same example as identifying cars from a picture, the number of labels would help you determine the number of brands you want to identify. The subclassifications and the number of other things you might want to classify would all depend on the number of labels you want to include in your dataset.
- The third thing to consider is the parts of the image you want to be recognized. Considering the same example as that of a car, would you like to identify only if the entire car is present in the picture or if just a part of it, say, the rear of the car alone, is visible?
When you finalize these considerations, you should be able to define your labels to fit the purpose of your mode exactly.
So as you can see, an image classification dataset will have to include images that can be labeled to the extent of granularity, detail, and parts you want to classify. Your datasets should be able to support these labeling considerations and have huge amounts of relevant data.
A rich dataset helps your model perform better. It should also ensure consistent data points across the various classes. It should be devoid of noise and corrupted data. Outliers should be handled properly, and all given images should have good quality and be easy to label.
What are image classification features?
Besides labels, you should also consider the features you intend to extract from your images. The following are some of the common features your image dataset should offer you:
There are many more image descriptors that can be used as features to classify an image. Depending on the problem you are trying to solve, your dataset should be able to support a smooth and optimal feature extraction for these features.
What is image classification quality?
As already mentioned, good-quality images help improve the model’s performance and can help you reach accurate results faster. Here are some of the quality parameters to consider when gathering your image classification datasets:
- Image contrast
- Contrast sensitivity
- Blur and detail visibility
- Noise
- Artifacts
- Distortion
- Compromises and so on
The quality of any image is usually determined depending on its imaging method, the equipment used, and the various imaging variables such as contrast, blur, noise, distortion, and so on. Based on the problem, the image classification dataset should provide the adequate quality required for the models to work optimally.
How much of image classification datasets would you require?
The amount of training data you need to train an image classification model largely depends on the classification goals of your model. The more items you want to detect and recognize, the more volumes of data you should be using. Here are some minimum requirements when deciding on your image classification dataset size.
As a rule of thumb, it is best to have at least 100 images per particular class of item you want to detect. For example, if you want to detect sunflowers from a picture, you should have at least 100 images of sunflowers in your training data. The more flowers or labels you want to detect, the more images you need.
If you want greater detail, that is, high granularity, the number of images used should also be higher. As a recommendation, it is considered best to use at least 100 images for each sub-label.
The same applies to the number of parts you want to identify. You will need at least a minimum of 100 images per item that you want to identify.
While the arbitrary count of 100 images per label may sound like a good benchmark, one cannot be assured of the accuracy with just this figure. Depending on the complexity of the image classification problem, you might need more images, or sometimes for similar shape identification, a lower number of images could also suffice.
How to use image classification datasets?
Depending on the machine learning model you use, you would be using the image datasets to train and validate the model.
Create your image classification datasets and specify the associated attributes and parameters.
Input this dataset in some form via file storage or upload it to the machine learning model system for training purposes. While uploading, you should specify a certain percentage of the dataset to be used for validation and the rest for testing. For this, you could make use of a split algorithm. Specify where the results of the split algorithm should be stored and the workflows on how the model should consume these datasets.
The steps can be summarized as follows:
- Collecting the image dataset
- Split the images into training and validation groups and test groups. The percentage of this split can be anything from 60%-40% to 80%-20%, depending on the model.
- Pre-process the images by labeling them
- Use the image datasets to train the model, build the model and fine-tune it
- Evaluate the model by testing it against the test dataset
Challenges when training with image classification datasets
Getting high-quality data with consistent view angles and sizes can be a challenge. But to ensure accurate results, you will need sample data of the same item in differing angles, lighting, and other quality concerns. Here are some common challenges you might encounter when trying to finalize your dataset:
- Differing orientations
A single object might look a lot different when viewed from different viewpoints with respect to the camera. This could increase the complexity of the training involved.
- Scales and sizes
Images can sometimes cause optical illusions where the size and scalings of the item could differ a lot. For instance, a car pictured near a tanker lorry could appear much smaller than it really is.
- Visibility
Low visibility and low light conditions can hamper the data quality down to the pixel level, and sometimes the entire object may not be visible in the image.
Best practices when dealing with image classification datasets
You must follow best practices to get high-quality images and ensure your dataset is diversified enough to support accurate results. Here are some practices that can help:
- Ensure your image dataset contains images taken from varying angles and perspectives.
- Collect images in different lighting conditions.
- Try to include as many images with varying sizes and scalings relevant to other objects, so you can include greater variance in your dataset.
- Make sure your images have high visibility.
- In case of limited visibility, try using low-visibility data points when training your dataset.
- Try to limit the data size of your images, as very big images can impact your model’s performance. Most AI models work with images of sizes up to 224 x 224 pixels. So making use of huge file sizes will bear no extra benefit and could only slow down processing.
- It is also a good practice to use high-definition pictures to get better results.
Popular image classification datasets
While you can use your own custom datasets for your image classification algorithms, using existing datasets is often considered a best practice. This is because these datasets usually provide you with well-prepared high-quality images and come with easy licensing options to use in your models. You should also remember that sometimes images on the internet could carry copyright implications, and collecting huge amounts of relevant image classification datasets can be quite challenging.
Here are some popular image classification datasets that you could make use of:
- Image Classification: People and Food
- Images of Crack in Concrete for Classification
- Architectural Heritage Elements
Uses in the field of medicine:
Agriculture-based image classification datasets:
- Indoor Scenes Images
- Images for Weather Recognition
- Intel Image Classification
- TensorFlow Sun397 Image Classification Dataset
- Coastset Image Classification Dataset
Conclusion
Image classification is a growing and commonly used machine learning-based task that finds applications in various industries. Every computer vision system uses them, from surveillance applications to medical diagnosis systems. And such advanced AI-based image classification systems would not be possible without the huge volumes of training data used to train, model, and evaluate these systems. Thus, image classification datasets play an integral part in the ongoing development of AI technologies, and researchers worldwide need many good-quality image classification datasets. Understanding their utilization, fair usage, and quality data collection is necessary to create better machine learning models and AI systems.
