JanadaSroor/vision-models

AI Kit Gallery - Optimized ONNX Vision Models

This repository contains optimized ONNX models designed for the AI Kit Gallery Android app. These models enable high-performance, offline AI-powered image search and categorization directly on mobile devices.

📁 Available Models

CLIP Models (OpenAI/clip-vit-base-patch32)

• Text Encoder: clip_text_quantized.onnx (62MB)

  • Input: Text tokens (Max length 77)
  • Output: 512D text embedding
  • Optimization: INT8 Dynamic Quantization
  • Use Case: Generating embeddings for text queries.

• Vision Encoder: clip_vision_quantized.onnx (337MB)

  • Input: 224x224 RGB images
  • Output: 512D image embedding
  • Optimization: Full precision (FP32) to maintain accuracy
  • Use Case: Encoding images for similarity search.

ViT Model (Google/vit-base-patch16-224)

• Base Model: vit_base_quantized.onnx (84MB)
  • Input: 224x224 RGB images
  • Output: 768D image embedding (CLS token)
  • Optimization: INT8 Dynamic Quantization
  • Use Case: Alternative high-quality vision encoder.

🚀 Quick Start

1. Try the Interactive Demo

You can view or download the demo notebook from Hugging Face: View AI Models Demo

To run it in Colab: Download the .ipynb file and upload it to Google Colab.

2. Download Models

# Install Hugging Face Hub
pip install huggingface_hub

# Download CLIP Models
huggingface-cli download JanadaSroor/vision-models models/clip_text_quantized.onnx --local-dir .
huggingface-cli download JanadaSroor/vision-models models/clip_vision_quantized.onnx --local-dir .

# Download ViT Model
huggingface-cli download JanadaSroor/vision-models models/vit_base_quantized.onnx --local-dir .

📊 Model Specifications

🏃 Performance Benchmarks

Inference times measured on a standard T4 GPU instance (CPU mode) in Colab:

• CLIP Text (INT8): ~12ms
• CLIP Vision (FP32): ~65ms
• ViT Base (INT8): ~55ms

Note: Mobile performance on modern Android devices (SD 8 Gen 1+) is expected to be 20-30% faster due to NPU/GPU acceleration.

🔧 Deployment in Android

These models are optimized for ONNX Runtime Mobile.

1. Copy the .onnx files to your project's src/main/assets/ directory.
2. Use the ONNX Runtime Kotlin/Java API to load and run inference:

val session = OrtSession.create(env, modelBytes, options)
val inputs = mapOf("input_ids" to textTensor)
val results = session.run(inputs)

📈 Optimization Details

We used Hugging Face Optimum and ONNX Runtime Quantization tools to achieve these results:

• Dynamic Quantization: Applied to CLIP Text and ViT Base to reduce memory footprint.
• Operator Fusion: Combined multiple layers into single kernels for faster execution.
• Precision Tuning: Kept CLIP Vision in FP32 as INT8 quantization led to significant accuracy loss (>5%).

🔍 Use Cases

• Semantic Search: "Show me photos of mountains at sunset."
• Image Clustering: Automatically group similar photos.
• Fast Tagging: Detect objects and scenes without cloud APIs.

📄 License

This project is licensed under the MIT License. Models are subject to their respective original licenses (OpenAI for CLIP, Google for ViT).

Maintained by JanadaSroor | Developed for AI Kit Gallery