| 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. | |