--- license: mit pretty_name: EquiFashion-DB (Mini) language: - en task_categories: - text-to-image - image-to-image size_categories: - 1K.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/` and `test/` - **Text**: `train.json`, `test.json` (captions) - **Pose**: `train_pose/json/*.json` (keypoints) and `train_pose/pose/*.jpg` (visualization) - **Sketch**: `train_sketch/.png` - **Fabric**: `train_fabric/.png` (fixed-size texture patch) ## Data construction pipeline The mini version follows the EquiFashion-DB construction pipeline: ![EquiFashion data pipeline](equifashionDB_pipeline_diagram.png) 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} } ```