Update README.md

README.md

@@ -1,3 +1,61 @@
----
-license: openrail
----
+---
+license: openrail
+tags:
+- fashion
+- clothing
+- virtual-try-on
+- e-commerce
+- flatlay
+- image-generation
+pretty_name: Codatta Fashion 1K
+size_categories:
+- 1K<n<10K
+task_categories:
+- image-classification
+- object-detection
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+---
+
+# 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()