---
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```