Where style meets pixels, and creativity meets machine learning.
Fashion Vision is your experimental playground for building AI-powered fashion understanding systems — from classification to visual search.
It’s designed for builders, students, and researchers who want to turn messy outfit photos into structured, searchable data — fast and beautifully.
- 🧩 Structure the chaos: Turn unorganized fashion images into labeled, searchable datasets.
- ⚡ Prototype faster: Get up and running with working ML examples in minutes.
- 🧠 Learn by doing: Each script and notebook is self-explanatory and focused on real-world fashion tasks — classification, detection, and recommendation.
- 📈 Bridge research & product: Build models that don’t just work in notebooks — but scale to production.
- 🧪 Mini Notebooks: Run compact, self-contained experiments without setup headaches.
- 🎯 Plug-and-Play Scripts: Prebuilt training, evaluation, and inference scripts for image tasks.
- 🧼 Preprocessing Tools: Utilities for cleaning and normalizing fashion datasets.
- 📘 Practical Notes: Guidance on model choices, metrics, and common pitfalls in fashion AI.
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Clone the Repository
git clone https://github.com/isatyamks/fashion-vision.git cd fashion-vision -
Setup Your Environment
python -m venv venv source venv/bin/activate # On Windows: .\venv\Scripts\activate pip install -r requirements.txt || echo "Install torch, torchvision, pandas manually if needed"
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Run a Demo
Open the notebook below in Jupyter or VS Code:notebooks/demo.ipynbRun the first cell to download a sample dataset and see a pretrained model classify clothing items like T-shirts, jackets, and dresses.
- 🧍♀️ Fashion Classifier: Train a lightweight model to identify T-shirts, blouses, or jackets.
- 🔍 Visual Search Engine: Upload an image — find similar catalog items using embeddings.
- 🎨 Attribute Extractor: Detect color, pattern, or sleeve length to enrich metadata.
- 📱 Deploy Anywhere: Optimize for mobile and edge inference.
- Practical over perfect: Ship working examples first, polish later.
- Transparency first: Every decision is explained with short, clear notes.
- Reproducibility matters: Seed everything, log configs, and version datasets.
Want to join the runway? Here’s the fast lane:
- ⭐ Star & Fork this repo — it helps more builders discover it.
- 🐛 Open an Issue with a crisp title & short reproduction.
- 🌿 Create a Branch:
feat/<short>orfix/<short>, then submit a Pull Request.
Good starter tasks:
- 🧩 Add an inference script (
inference.py) for single-image predictions. - 📦 Create a minimal
requirements.txtfor the notebooks.
| Feature | Status | Description |
|---|---|---|
| 🧵 Tiny curated datasets | 🕓 Planned | 2–5 classes, ~200 images each |
| 🧮 Evaluation scripts | 🕓 Planned | Retrieval metrics (mAP, Top-k) |
| 🐳 Docker image | 🕓 Planned | One-command demo setup |
| 🖼️ More demo notebooks | 🕓 Planned | Attribute extraction & search demos |
No license yet. MIT is recommended if you’d like others to freely reuse and extend your work.
Maintainer: Satyam Kumar
- 📧 Email: isatyamks@gmail.com
- 🌐 GitHub: github.com/isatyamks/fashion-vision
“Fashion is about expressing identity — and so is code.
Build, experiment, and make AI wear your creativity.” 👕✨