Choosing the Right Database
Last updated
Last updated
The success of an object detection model heavily relies on the quality and diversity of the database used for training and evaluation.
Selecting the appropriate database is a critical step in the development process, as it directly impacts the model's performance and generalization capabilities.
Before selecting a database, it is essential to clearly define the requirements and goals of the object detection model. Consider the following factors:
Object Categories: Identify the specific types of objects the model needs to detect. The database should contain a representative sample of these categories.
Image Variability: Assess the variation in image quality, lighting conditions, and object poses present in the target application. Choose/create a database that captures these real-world scenarios.
Scale and Complexity: Determine the scale and complexity of objects. Some databases focus on small objects, while others cover larger or more complex scenes.
The size of the database directly affects the model's performance and training time. For models with a large number of parameters, a larger database is generally preferred. However, small databases might suffice for simpler models or proof-of-concept implementations.
A good rule of thumb is to use a minimum of 1000 images per class. This ensures each class can be taught with precision.
Ensuring good bounding box quality will ensure better learning process. Two examples of good and bad bounding box implementations:
You see, good annotation only encapsulates the object bounding box. This makes easier for model to be optimized and gives less importance on the background.
Ensure that the database is well-balanced across different object categories to avoid bias towards dominant classes.
A skewed distribution may lead to suboptimal performance on underrepresented categories during training and evaluation.
Dividing visually indistinguisable objects into different classes may yield an ineffective training process.
Augmentation is a method of artificially creating new data by modifiying the existing ones. In image processing, this is done by modifying constrast, values, width, height, perspective and many more feature to create a more generic database.
You can use Plug-in Window download augmentation tools in AugeLab Studio.
