Data Set

A crucial aspect of building a custom model in DeepLobe is the availability and quality of a suitable dataset. A dataset is a collection of labeled examples that serves as the foundation for training a model. DeepLobe allows users to upload and utilize their own dataset, specifically curated to address their unique use case.

When creating a custom model, it is important to consider the following factors regarding the dataset:

• Relevance: The dataset should contain examples that are relevant to the problem you are trying to solve. It should cover a wide range of scenarios and variations that the model may encounter in real-world applications.
• Quantity: The size of the dataset plays a significant role in the performance of the model. Generally, larger datasets tend to produce better models by providing more diverse and representative examples for the training process.
• Quality: Ensuring the accuracy and quality of the dataset is crucial. The dataset should have accurate and consistent labels, free from any biases or errors that could negatively impact the model's performance.

DeepLobe provides tools and guidelines to help users prepare and upload their dataset effectively. By leveraging a well-curated dataset, users can create more accurate and robust custom models.