tags:
- onnx
- transformers.js
- image-classification
- creativity-assessment
- beit
license: apache-2.0
datasets:
- figural-drawings
metrics:
- accuracy
pipeline_tag: image-classification
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.