Showcase Notice

The images displayed on this page are illustrative showcase examples only. They demonstrate the types of editing tasks, visual quality and data structures that FixThePhoto can produce.

The displayed images are not distributed as training data through this repository and are not licensed through this repository for AI/ML use.

A separate rights-cleared technical dataset sample can be provided upon request.

Request a Dataset Sample

If you are evaluating visual training data for an image-editing, generative image or multimodal model, FixThePhoto can prepare a separate technical sample according to your requirements.

A sample can include:

source images
professional human-edited targets
natural-language editing instructions
masks
metadata
QA information
human-corrected AI outputs

Request a Dataset Sample →

Image Editing Tasks Covered

This showcase represents several categories of professional image-editing data available for custom dataset production.

1. Object Removal Dataset

Training examples for removing unwanted objects while reconstructing the surrounding scene naturally.

Possible data:

source image
removal instruction
object mask
human-edited target
QA metadata

Example instruction:
Remove the unwanted object while preserving surrounding textures, lighting and perspective.

Illustrative showcase only. The displayed images are not distributed as training data through this repository.

2. Object Addition Dataset

Human-created examples showing how new objects can be introduced into existing scenes while maintaining realistic scale, perspective, shadows and lighting.

Example instruction:
Add the requested object naturally to the scene while matching perspective and lighting.

3. Object Replacement Dataset

Source-to-target examples for replacing one visual element with another while preserving scene consistency.

Applications can include generative editing, instruction-following image models and visual model evaluation.

4. Background Editing Dataset

Professional examples for:

background replacement
background removal
scene cleanup
environment changes
subject/background separation

High-quality targets can be created with attention to edges, hair, reflections, shadows and natural compositing.

5. Object Color Editing Dataset

Data for controlled color changes while preserving texture, material appearance, highlights and shadows.

This type of data can be useful for instruction-based image editing models where only a specific visual property should change.

6. Material and Texture Editing Dataset

Human-edited targets for changing materials or surface appearance while maintaining object geometry, perspective and scene lighting.

Examples may include:

fabric → leather
wood → metal
matte → glossy
texture replacement
surface finish changes

7. Portrait Editing Dataset

Professional portrait retouching data can provide human-created ground truth for models that need to improve portraits while preserving identity.

Tasks can include:

natural skin retouching
hair corrections
eye and teeth corrections
local cleanup
lighting and tone correction
controlled face or body adjustments
full professional portrait retouching

A major quality requirement is preserving the identity and natural appearance of the person.

Explore specialized sample: Portrait Editing Dataset Sample — coming soon.

8. Product Image Editing Dataset

Professional product-image training data for e-commerce, advertising and product visualization models.

Possible tasks include:

product cleanup
background editing
color changes
material changes
reflections and shadows
object geometry preservation
label and detail preservation
commercial product retouching

9. Photo Restoration Dataset

Before-and-after data showing professional restoration of damaged or degraded photographs.

Examples can include:

scratch removal
dust and damage repair
missing-detail reconstruction
contrast restoration
color correction
old-photo cleanup

10. Human-Corrected AI Image Dataset

Instead of using only synthetic AI outputs, model-generated images can be reviewed and professionally corrected by human image editors.

Possible structure:

Prompt → AI Output → Human-Corrected Target

This makes it possible to capture real model errors and provide a professionally corrected reference target.

11. Image Matting and Segmentation Data

Professional masks and alpha mattes can be produced for complex image regions such as:

hair
fur
transparent materials
glass
fine edges
products
people

Outputs can include binary masks, segmentation masks or alpha mattes depending on model requirements.

12. Real Estate Image Editing Dataset

Professional real-estate image editing can provide training targets for tasks such as:

interior enhancement
HDR-style correction
object removal
decluttering
virtual staging
lighting correction

13. Graphic Design and Layered PSD Data

FixThePhoto can also produce structured creative data based on professional graphic-design workflows.

Possible outputs include:

layered PSD files
editable designs
layout variations
typography data
advertising creatives
human-created design targets

Example Training Data Structure

Depending on the project, a training unit can be structured as:

Source Image → Editing Instruction → Human-Edited Target

Example:

Instruction

Change the chair upholstery from fabric to brown leather while preserving the chair geometry, perspective, highlights and scene lighting.

Possible associated fields:

task category
source image
target image
natural-language instruction
mask
editing attributes
QA status
reviewer metadata
difficulty
version
provenance information

Human Ground Truth

The primary distinction of this type of data is that targets can be created and reviewed by professional image editors rather than generated automatically.

Human production can be particularly useful when a model needs examples involving:

realistic visual judgment
subtle retouching
identity preservation
material consistency
natural compositing
commercial image quality
complex correction of AI-generated artifacts

Human Quality Assurance

Dataset QA can be configured around the requirements of the model and task.

Evaluation criteria may include:

instruction compliance
target quality
unchanged-region preservation
visual realism
identity preservation
edge quality
geometry consistency
lighting consistency
artifact detection
metadata validation

Custom Dataset Configuration

Visual datasets can be produced according to project-specific requirements.

Configurations can include:

Task • Volume • Image Mix • Resolution • Instructions • Masks • Metadata • QA • Delivery • Licensing

Projects can range from calibration and evaluation batches to larger production datasets.

Dataset Samples and Licensing

The public images shown on this Hugging Face page are provided only to demonstrate editing categories and production quality.

They are not the training assets offered for download or licensing through this repository.

For technical evaluation, FixThePhoto can provide a separate sample prepared with appropriate usage rights and documentation.

Request a Dataset Sample →

Custom Image Dataset Production

About FixThePhoto Visual AI Data

FixThePhoto combines professional image-editing expertise with structured visual-data production for AI training and evaluation.

Our work includes human-edited image pairs, custom visual datasets, human ground truth, model evaluation, human feedback and creative visual data.

Explore FixThePhoto Visual AI Training Data →

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