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README.md

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+---
+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](https://huggingface.co/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
+
+```javascript
+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](https://huggingface.co/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.