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---
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```
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