Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+datasets:
+  - alfredplpl/Japanese-photos
+  - 3sara/colpali_italian_documents
+pipeline_tag: image-classification
+tags:
+  - image-classification
+  - mobile
+  - tablet
+  - quantization
+  - onnx
+  - mobilenetv3
+  - mobilenet_v3
+  - mobilenetv3_onnx
+  - document-classification
+  - photo-classification
+  - real-time
+  - lightweight
+  - efficient
+  - document
+  - photo
+  - images
+  - q8
+  - int8
+  - edge-ai
+  - ai-on-device
+  - offline
+  - privacy
+  - fast
+  - android
+  - ios
+  - gallery
+---
+
+# MobileNetV3 — ONNX, Quantized
+
+### 🔥 Lightweight mobile model for **image classification** into two categories:
+- **`document`** (scans, receipts, papers, invoices)
+- **`photo`** (regular phone photos: scenes, people, nature, etc.)
+
+---
+
+## 🟢 Overview
+
+- **Designed for mobile devices** (phones and tablets, Android/iOS), perfect for real-time on-device inference!
+- Architecture: **MobileNetV2**
+- Format: **ONNX** (both float32 and quantized int8 versions included)
+- Trained on balanced, real-world open-source datasets for both documents and photos.
+- Ideal for tasks like:
+  - Document detection in gallery/camera rolls
+  - Screenshot, receipt, photo, and PDF preview classification
+  - Image sorting for privacy-first offline AI assistants
+
+---
+
+## 🏷️ Model Classes
+- **0** — `document`
+- **1** — `photo`
+
+---
+
+## ⚡️ Versions
+
+- `mobilenet_v3_small.onnx` — Standard float32 for maximum accuracy (best for ARM/CPU)
+- `mobilenet_v3_small_quant.onnx` — Quantized int8 for even faster inference and smaller file size (best for low-power or edge devices)
+
+---
+
+## 🚀 Why this model?
+
+- **Ultra-small size** (~10-15MB), real-time inference (<100ms) on most phones
+- **Runs 100% offline** (privacy, no cloud required)
+- **Easy integration** with any framework, including React Native (`onnxruntime-react-native`), Android (ONNX Runtime), and iOS.
+
+---
+
+## 🗃️ Datasets
+
+- **Photos:** [alfredplpl/Japanese-photos](https://huggingface.co/datasets/alfredplpl/Japanese-photos)
+- **Documents:** [3sara/colpali_italian_documents](https://huggingface.co/datasets/3sara/colpali_italian_documents)
+
+---
+
+## 🤖 Author
+@vlad-m-dev
+Built for edge-ai/phone/tablet offline image classification: document vs photo
+Telegram: https://t.me/dwight_schrute_engineer
+
+---
+
+## 🛠️ Usage Example
+
+```python
+import onnxruntime as ort
+import numpy as np
+
+session = ort.InferenceSession(MODEL_PATH)
+img = np.random.randn(1, 3, 224, 224).astype(np.float32)  # Replace with your image preprocessing!
+output = session.run(None, {"input": img})
+pred_class = np.argmax(output[0])
+print(pred_class)  # 0 = document, 1 = photo```