Train Your AI Model

Unleash the Power of Visual AI for Your Business

An AI model training pipeline is a series of interconnected steps that automate the process of training, validating, and fine-tuning a machine learning model. DataVerse simplifies the creation of an AI model training pipeline, allowing users to focus on obtaining the best results for their specific use cases. This guide will walk you through the process of creating an AI model training pipeline using the DataVerse platform.

Quick Start

Begin with simpler models – they're like your warm-up laps. Once you're in the groove, you can amp up the intensity for better performance.

Click on the video below for a quick overview:

Preparing Your Data

To begin, gather and organize the data that you intend to use for training your AI model. Ensure that the dataset is accurately labeled and representative of the problem you aim to solve. Divide your dataset into separate subsets for training, validation, and testing purposes.

Model Selection and Configuration

Select a suitable model architecture based on your specific use case and requirements. Dataverse offers a variety of popular and proven model architectures to choose from. Configure the model by setting the appropriate parameters, such as the learning rate, optimizer, and the number of epochs.

Metrics performance parameter configuration

Training Type: Clear choice between two training workflows with distinct purposes.

• New Model: Training for new object categories or new tasks

• Fine-Tune Model: Update existing model with new data using fewer epochs

Model Structure: Clear size options with explicit model architectures

• Small - YOLOv9-s: Fastest inference, lower accuracy

• Medium - YOLOv9-c: Balanced performance (Default)

• Large - YOLOv9-e: Highest accuracy, slower inference

Data Augmentation: Create variations of your images (e.g., flip, rotate, crop) to expand your training dataset.

• Photometric (Color & Brightness)

  • Off: No color changes (Use when color is critical, e.g., clothing color classification)

  • Conservative (Default): Light color/brightness variation, stays close to original appearance

  • Aggressive: Heavy variation for diverse lighting conditions

  • Custom: Manual control of hue, saturation, and brightness parameters

• Geometric (Position & Size)

  • Off: No spatial changes (Use when orientation matters, e.g., directional traffic signs)

  • Conservative: Moderate position/size variation without flipping

  • Aggressive: Comprehensive transformations including mosaic, mixup, copy-paste

  • Custom: Manual control of translate, scale, flip, mosaic, mixup, and copy-paste

Class-Agnostic NMS: When enabled, NMS filters overlapping boxes across all classes instead of per class. Available during model training, prediction, and model conversion.

Metrics Settings: Customize IoU thresholds and object size classifications (large, medium, small) for your metrics results. Settings are applied after training completes to generate performance results accordingly.

Good to know: Setting the classification of large, medium and small objects is helpful for detailed analysis, especially when detecting objects that tend to have similar sizes.

Start Training

Initiate the training process by clicking the "Trigger Training Job" button. During this phase, Dataverse will automatically train the model on the provided training dataset and validate its performance using the validation dataset. Monitor the training progress and validation metrics to ensure that the model is learning effectively.

Good to know: Begin with simpler models – they're like your warm-up laps. Once you're in the groove, you can amp up the intensity for better performance. And remember, good training takes time. So, grab a coffee and let the platform work its magic.

Once the training process is complete, evaluate the performance of your AI model using the testing dataset. Analyze key performance metrics, such as accuracy, precision, recall, and F1 score in the next session.