Datasets:
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license: mit
pretty_name: EquiFashion-DB (Mini)
language:
- en
task_categories:
- text-to-image
- image-to-image
size_categories:
- 1K<n<10K
tags:
- fashion
- diffusion
- multimodal
- text2image
- pose
- sketch
- fabric
---
# EquiFashion-DB (Mini)
**EquiFashion-DB (Mini)** is a compact subset of EquiFashion-DB with **aligned multimodal signals** for controllable fashion generation: **image, text, pose, sketch, and fabric**.
**Full Dataset Public at:** https://drive.google.com/file/d/13TS1U0IY8oG1gjMvsGCQCXrxLxm2SH-Z/view?usp=drive_link
## Structure (current `EquiFashion_DB/`)
```text
EquiFashion_DB/
├── train/ # training images
├── test/ # test images
├── train_pose/ # pose assets (json/ + pose/ visualizations)
├── train_sketch/ # extracted Canny sketch maps (PNG)
├── train_fabric/ # extracted fabric texture patches (PNG)
├── train.json # train captions (list of {gt, caption})
├── test.json # test captions (list of {gt, caption})
└── train_pose.json # train captions + pose path (list of {gt, caption, pose})
```
## Annotation format (as provided)
**Train captions** (`train.json`)
```json
{ "gt": "009292_0.jpg", "caption": "Sweater, Commute, Homewear, ..." }
```
**Train captions + pose path** (`train_pose.json`)
```json
{ "gt": "009292_0.jpg", "caption": "Sweater, ...", "pose": "train_pose/pose/009292_0.jpg" }
```
**Pose keypoints JSON** (`train_pose/json/<gt_stem>.json`)
- Key `candidate`: list of \([x, y, confidence, joint_index]\)
```json
{
"candidate": [[282.0, 3.0, 0.54, 0.0], [247.0, 58.0, 0.92, 2.0]]
}
```
## Modalities
- **Image**: `train/<gt>` and `test/<gt>`
- **Text**: `train.json`, `test.json` (captions)
- **Pose**: `train_pose/json/*.json` (keypoints) and `train_pose/pose/*.jpg` (visualization)
- **Sketch**: `train_sketch/<gt_stem>.png`
- **Fabric**: `train_fabric/<gt_stem>.png` (fixed-size texture patch)
## Data construction pipeline
The mini version follows the EquiFashion-DB construction pipeline:
1. **Public sources → raw pool**
Multiple fashion datasets (captioning, recognition, segmentation, editing) are merged into a raw pool with images, captions/attributes, categories and pose/parsing when available.
2. **Cleaning & quality filtering**
- Remove broken images, heavy occlusions and extreme truncation.
- Discard samples with invalid / missing key joints or inconsistent parsing when pose is available.
3. **Resolution & category normalization**
- Crop/resize all images to \(512 \times 512\) around the main garment / person.
- Map dataset-specific labels into a unified garment taxonomy (40+ categories).
4. **Multimodal enrichment (this repo)**
Using the `equifashion_pipeline/` code:
- Generate Canny **sketch maps** inside garment regions (from pose keypoints when available).
- Sample high-frequency **fabric patches** from garment regions.
- Normalize **pose** JSON into a unified keypoint format.
5. **Packaging**
Final JSON manifests (`train.json`, `test.json`, `train_pose.json`) store standardized paths and captions, with all modalities aligned by filename stem.
## References
[1] Xie et al. — HieraFashDiff: Hierarchical Fashion Design with Mu
[2] Baldrati et al. — Multimodal Garment Designer: Human-Centric Latent Diffusion Models for Fashion Image Editing (2023)
[3] Zhang et al. — ARMANI: Part-level Garment-Text Alignment for Unified Cross-Modal Fashion Design (2022)
[4] Jiang et al. — Text2Human: Text-Driven Controllable Human Image Generation (2022)
[5] Rostamzadeh et al. — Fashion-Gen: The Generative Fashion Dataset and Challenge (2018)
[6] Yang et al. — Fashion Captioning: Towards Generating Accurate Descriptions with Semantic Rewards (2020)
## Usage
```python
from datasets import load_dataset
ds = load_dataset("NguyenDinhHieu/EquiFashion-DB")
```
## Citation
```bibtex
@dataset{NguyenDinhHieu_EquiFashionDBMini,
title = {EquiFashion: Hybrid GAN–Diffusion Balancing Diversity–Fidelity for Fashion Design Generation},
author = {Nguyen Dinh Hieu, Tran Minh Khuong, Phan Duy Hung},
year = {2026},
url = {https://huggingface.co/datasets/NguyenDinhHieu/EquiFashion-DB}
}
```
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