Evaluating Models

Learn about our model evaluation feature

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After training your model successfully, testing its performance is a critical step before deployment in a production environment. Our model evaluation tool enables you to assess a specific model version, offering detailed insights into its performance metrics and ensuring its readiness for real-world applications.

Evaluation varies depending on the model type (such as classification or object detection) and the task (such as image recognition or text analysis). Once the evaluation is complete, you can view various metrics about the model’s behavior.

This helps you to:

• Refine the model further and enhance its performance;
• Understand the model's strengths and weaknesses before deploying it in a real-world scenario;
• Perform a comparison between different versions to select the best performing one.

Model Types Supported

We currently support evaluating the following model types:

• Transfer learn models
• Visual classifiers
• Visual detectors
• Text classifiers
• Visual segmenters
• LLMs for text generation

Prerequisites

To successfully run the evaluation on a model, it must meet the following criteria:

• It should be a custom-trained model with a version you've created
• It should have at least two concepts
• There should be at least ten evaluation training inputs per concept (although at least 50 inputs per concept is recommended for more reliable results)

caution

The evaluation may result in an error if the model version doesn’t satisfy the requirements above.

📄️ Transfer Learn Models

Evaluate visual or text classifiers trained using transfer learning

📄️ Classification Models

Evaluate visual or text classifiers trained using deep fine-tuning

📄️ Visual Detection Models

Evaluate visual detection models

📄️ Visual Segmentation Models

Evaluate segmentation models

📄️ LLM Evaluation

Evaluate your fine-tuned LLMs for text generation tasks

📄️ Evaluation Leaderboard

Compare models' performance based on their evaluation results

📄️ Improving Your Model

Iterate upon and improve your models.

📄️ Model Evaluation FAQs

Frequently asked questions on model evaluation