README.md

---
license: openrail
tags:
- fashion
- clothing
- virtual-try-on
- e-commerce
- flatlay
- image-generation
pretty_name: Fashion 1K
size_categories:
- 1K<n<10K
task_categories:
- object-detection
language:
- en
---

# Fashion 1K

## Dataset Summary

**Fashion 1K** is a curated collection of 1,000 high-quality fashion images, focusing on apparel and outfit compositions without human models.

Unlike typical street-style datasets (like DeepFashion) that include human poses and complex backgrounds, this dataset provides **clean, human-free** images. The images primarily feature **Flat Lay** (clothing arranged on a flat surface) or **Ghost Mannequin** styles, making them ideal for tasks that require a clear view of the garment's structure, texture, and color without occlusion.

**Key Features:**
* **Human-Free:** No faces, limbs, or skin tones—strictly focused on the garments.
* **Outfit-Centric:** Many images showcase complete looks (e.g., Top + Bottom + Shoes) to aid in compatibility learning.
* **Clean Backgrounds:** Minimized background noise to facilitate easier segmentation and feature extraction.

## Supported Tasks

This dataset is particularly suitable for:

* **Virtual Try-On (VTON):** Serving as the "garment" reference image (`g_img`) for 2D try-on pipelines.
* **Fashion Compatibility Learning:** Learning which items (e.g., shirt and trousers) go well together based on the curated outfits.
* **Generative AI Training:** Training LoRAs or ControlNets for specific clothing styles without the bias of human figures.
* **E-commerce Tagging:** Automated classification of clothing categories and attributes.

## Dataset Structure

### Data Fields

* **`image`** (image): The high-resolution image of the clothing item or outfit.

## Usage Example

```python
from datasets import load_dataset

# Load the dataset
ds = load_dataset("Codatta/Fashion-1K", split="train")

# Display the first image
sample = ds[0]
sample['image'].show()