ocsai-d-web-onnx

OCSAI-D Web (ONNX Quantized)

This is a quantized ONNX version of the POrg/ocsai-d-web model, optimized for web deployment with Transformers.js.

Model Description

This model assesses originality/creativity in figural drawings. It's a fine-tuned BEiT-large model that outputs a regression score indicating the creativity level of the input drawing.

Model Details

• Base Model: microsoft/beit-large-patch16-224-pt22k-ft22k
• Task: Image regression (creativity scoring)
• Input: 224x224 RGB images
• Output: Single regression score (0-1 range)
• Quantization: INT8 dynamic quantization
• File Size: ~300MB (vs 1.1GB original)

Usage with Transformers.js

import { pipeline } from '@xenova/transformers';

// Load the model
const classifier = await pipeline('image-classification', 'your-username/ocsai-d-web-onnx');

// Run inference on an image
const result = await classifier('path/to/drawing.jpg');
console.log(result);

Important Notes

This model is specifically designed for creativity assessment of figural drawings. The output is a single regression score that needs to be post-processed according to the original paper's methodology.

Original Model

Based on POrg/ocsai-d-web - please refer to the original model for citation information and detailed usage instructions.

Performance

The quantized model provides significant size reduction (~4x smaller) while maintaining compatibility with Transformers.js for browser-based inference.