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0xhalfmoonkid/
ritual-models

Name: ritual-models
Availability: InStock

onnx

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Ritual ONNX Models

This repository contains ONNX models optimized for use with the Ritual platform's ONNX precompile.

Models

1. MobileNet v2 (`mobilenetv2-12.onnx`)

Size: 13.3 MB
Purpose: Lightweight image classification
Input: 224x224 RGB images
Output: 1000 ImageNet classes
ONNX Opset: 12

2. ResNet50 (`resnet50-v2-7.onnx`)

Size: 97.4 MB
Purpose: High-accuracy image classification
Input: 224x224 RGB images
Output: 1000 ImageNet classes
ONNX Opset: 12

3. EfficientNet B0 (`efficientnet-b0.onnx`)

Size: 20.2 MB
Purpose: Balanced accuracy and efficiency
Input: 224x224 RGB images
Output: 1000 ImageNet classes
ONNX Opset: 12

Usage with Ritual

These models can be used in Ritual smart contracts via the ONNX precompile:

solidity

// Example model ID format
string memory modelId = "huggingface/wallychang/ritual-models/mobilenetv2-12.onnx@main";

// Call ONNX precompile
(bool success, bytes memory result) = ONNX_PRECOMPILE.staticcall(
    abi.encode(
        1, // precompile version
        modelId,
        inputTensor,
        inputNumberFormat,
        outputNumberFormat,
        roundingMode
    )
);

Model Details

All models are pre-trained on ImageNet and converted to ONNX format using PyTorch. They expect:

Input shape: [1, 3, 224, 224] (batch_size, channels, height, width)
Input format: Float32 normalized to [0, 1]
Output shape: [1, 1000] (batch_size, num_classes)

License

These models are based on pre-trained PyTorch models and are subject to their respective licenses.

0xhalfmoonkid/ritual-models