Datasets:

FixThePhoto
/

professional-image-editing-dataset-sample

---
language:
- en
pretty_name: Professional Image Editing Dataset Sample for AI Training
task_categories:
- image-to-image
tags:
- image-editing
- image-editing-dataset
- ai-training-data
- visual-ai
- human-edited
- before-after
- human-ground-truth
- generative-ai
- computer-vision
- instruction-based-image-editing
---
# Professional Image Editing Dataset for AI Training — Sample

Professional human-edited visual data for training, fine-tuning and evaluating image-editing and generative AI models.

FixThePhoto produces image-editing training datasets based on professional retouching and visual-production workflows.

Typical data structures can include:

- Source → Human-Edited Target
- Source → Instruction → Human-Edited Target
- AI Output → Human Correction
- Masks and alpha mattes
- Editing metadata
- Human QA metadata
- Preference and evaluation data
- Multi-turn image editing sequences

> **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 →](https://fixthephoto.com/photo-edit-data-sets-for-ai-training)**

---

## 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.`

<!-- INSERT BEFORE/AFTER SHOWCASE HERE -->

*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.`

<!-- INSERT BEFORE/AFTER SHOWCASE HERE -->

---

### 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.

<!-- INSERT BEFORE/AFTER SHOWCASE HERE -->

---

### 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.

<!-- INSERT BEFORE/AFTER SHOWCASE HERE -->

---

### 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.

<!-- INSERT BEFORE/AFTER SHOWCASE HERE -->

---

### 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

<!-- INSERT BEFORE/AFTER SHOWCASE HERE -->

---

### 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.


![ba1](https://cdn-uploads.huggingface.co/production/uploads/6a96e91430ca5a2ed5ee039a/WfFbbiy4FynKlhzsoEcQJ.png)


**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

<!-- INSERT BEFORE/AFTER SHOWCASE HERE -->

---

### 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

<!-- INSERT BEFORE/AFTER SHOWCASE HERE -->

---

### 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.

<!-- INSERT AI OUTPUT / HUMAN CORRECTION SHOWCASE HERE -->

---

### 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.

<!-- INSERT SHOWCASE HERE -->

---

### 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

<!-- INSERT BEFORE/AFTER SHOWCASE HERE -->

---

### 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

<!-- INSERT SHOWCASE HERE -->

---

## 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 →](https://fixthephoto.com/photo-edit-data-sets-for-ai-training)**

## 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 →](https://fixthephoto.com/photo-edit-data-sets-for-ai-training)**

	---
	language:
	- en
	pretty_name: Professional Image Editing Dataset Sample for AI Training
	task_categories:
	- image-to-image
	tags:
	- image-editing
	- image-editing-dataset
	- ai-training-data
	- visual-ai
	- human-edited
	- before-after
	- human-ground-truth
	- generative-ai
	- computer-vision
	- instruction-based-image-editing
	---

	# Professional Image Editing Dataset for AI Training — Sample

	Professional human-edited visual data for training, fine-tuning and evaluating image-editing and generative AI models.

	FixThePhoto produces image-editing training datasets based on professional retouching and visual-production workflows.

	Typical data structures can include:

	- Source → Human-Edited Target
	- Source → Instruction → Human-Edited Target
	- AI Output → Human Correction
	- Masks and alpha mattes
	- Editing metadata
	- Human QA metadata
	- Preference and evaluation data
	- Multi-turn image editing sequences

	> 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 →](https://fixthephoto.com/photo-edit-data-sets-for-ai-training)

	---

	## 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.`

	<!-- INSERT BEFORE/AFTER SHOWCASE HERE -->

	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.`

	<!-- INSERT BEFORE/AFTER SHOWCASE HERE -->

	---

	### 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.

	<!-- INSERT BEFORE/AFTER SHOWCASE HERE -->

	---

	### 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.

	<!-- INSERT BEFORE/AFTER SHOWCASE HERE -->

	---

	### 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.

	<!-- INSERT BEFORE/AFTER SHOWCASE HERE -->

	---

	### 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

	<!-- INSERT BEFORE/AFTER SHOWCASE HERE -->

	---

	### 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.


	![ba1](https://cdn-uploads.huggingface.co/production/uploads/6a96e91430ca5a2ed5ee039a/WfFbbiy4FynKlhzsoEcQJ.png)


	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

	<!-- INSERT BEFORE/AFTER SHOWCASE HERE -->

	---

	### 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

	<!-- INSERT BEFORE/AFTER SHOWCASE HERE -->

	---

	### 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.

	<!-- INSERT AI OUTPUT / HUMAN CORRECTION SHOWCASE HERE -->

	---

	### 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.

	<!-- INSERT SHOWCASE HERE -->

	---

	### 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

	<!-- INSERT BEFORE/AFTER SHOWCASE HERE -->

	---

	### 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

	<!-- INSERT SHOWCASE HERE -->

	---

	## 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 →](https://fixthephoto.com/photo-edit-data-sets-for-ai-training)

	## 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 →](https://fixthephoto.com/photo-edit-data-sets-for-ai-training)