Introduction to Model Training
Model training is the foundation of creating custom AI-powered image generation capabilities. When you train a Samsa model, you're teaching the AI to understand and reproduce specific subjects, whether they're people, objects, or artistic styles. The model will learn from the images you provide and can later be used to generate new images based on this learning.
Getting Started
To begin training a model in Samsa:
Navigate to the Models section
Select "Create New Model"
Upload your training dataset
Configure your model parameters
What Makes a Good Dataset?
1. Diverse Backgrounds
Background variation in your training images is crucial for several reasons:
Different backgrounds help the model distinguish between the subject and its environment
Varied settings teach the model what features are constant across contexts
Natural backgrounds are often better than artificial studio settings
Include both simple and complex backgrounds to improve model flexibility
For example, if you're training a model of a coffee mug, include images of it on a desk, in a kitchen, outdoors, and in different lighting conditions. This teaches the model to focus on the mug's constant features while understanding how it appears in different contexts.
2. Different Angles and Perspectives
The angles included in your training dataset directly determine what viewpoints your model can generate. This is a critical decision point in dataset creation.
If you want your model to generate from multiple angles:
Start with cardinal angles (front, side, back, 45-degree views)
Include both eye-level and elevated perspectives
For products, demonstrate any functional angles
Capture any unique features from multiple viewpoints
Consider the most common use cases for your generated images
However, if you only want to generate images from the front, include only front-view training images. For example:
Portrait models intended for front-facing headshots only
Product photos that should always show a specific view
Brand assets that need to maintain a consistent perspective
Marketing materials with standardized viewing angles
Your choice of angles should align with your specific generation needs. Every angle you include tells the model "this is a valid way to show this subject." Make sure that matches your intentions.
3. Varied Lighting Conditions
Lighting dramatically affects how subjects appear, and your model needs to understand these variations:
Natural daylight (morning, noon, and evening)
Artificial indoor lighting (warm and cool)
Directional lighting that highlights specific features
Diffused lighting that shows overall form
Shadow interactions that demonstrate depth and dimension
Poor lighting in training images often results in models that struggle with shadow placement and realistic lighting in generated images.
4. Minimum Dataset Size
We recommend a minimum of 10 different training images per style, subject or product. Here's why this number matters:
Benefits of a 10+ image dataset:
Provides sufficient data for pattern recognition
Reduces model inflexibility and bias
Minimizes the impact of unwanted details
Allows for proper feature learning
Quality remains paramount - a smaller set of excellent images often outperforms a larger set of poor-quality ones.
5. Different Shot Types
To build a comprehensive model, include these essential shot types:
Full Shots:
Establish overall proportions and scale
Show complete subject in context
Demonstrate natural positioning and stance
Help the model understand full composition
Medium Shots:
Capture regular interaction distance
Show common viewing perspectives
Balance detail with context
Demonstrate typical use cases
Detail Shots:
Highlight specific features and textures
Show important small elements
Capture surface qualities and materials
Provide reference for fine details
6. Image Quality Requirements
All images should meet these technical specifications:
Minimum resolution: 1024x1024 px or 1920x1080 px
Sharp focus on the subject
Clear definition without blur
No pixelation or compression artifacts
Consistent quality across the dataset
Person-Specific Guidelines
For training models of people:
Maintain consistent appearance across images
Choose recent photos taken within a short timeframe
Include characteristic expressions and poses
Consider permanent features (like glasses) carefully and show them in all images if they should be trained on
Common Pitfalls to Avoid
Inconsistent Data
Mixing different versions of products
Including temporary subject variations
Using inconsistent lighting or processing
Limited Perspectives
Only showing front views when multiple angles are needed
Missing important angles
Insufficient environmental variety
Poor Type Selection
Over-restrictive type definitions
Too general descriptions
Conflicting attributes
Professional Services
For enterprise clients requiring specialized model development, Samsa offers professional training services including:
Custom dataset curation
Optimized type configuration
Performance tuning
Integration support and success program
Contact enterprise@samsa.ai for enterprise solutions.
Best Practice Examples
Professional Portrait Model
Required elements:
Full face views (from intended angles only)
Various expressions
Different lighting conditions
Consistent personal style
Range of environmental contexts
Product Model
Required elements:
Required viewing angles (front-only or multi-angle as needed)
Detail shots of key features
Scale reference shots
Various use case scenarios
Different lighting conditions
Model Limitations and Expectations
Remember these key points:
Models can only generate based on learned patterns
Missing perspectives will be approximated
Consistent features in training will be persistent in output
Quality of training data directly influences output quality
Your model is ready for use when:
Generation attempts closely match training data
Consistent features appear reliably
Subject is recognizable from new angles
Style and quality remain stable across generations