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name: DocumentClassifier_jpqd |
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description: DocumentClassifier deep learning model for document type classification, optimized with JPQD quantization |
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framework: ONNX |
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task: image-classification |
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domain: computer-vision |
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subdomain: document-analysis |
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model_info: |
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architecture: Convolutional Neural Network |
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paper: "Docling Technical Report" |
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paper_url: "https://arxiv.org/abs/2408.09869" |
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original_source: DS4SD DocumentClassifier |
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original_repo: "https://huggingface.co/ds4sd/DocumentClassifier" |
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optimization: JPQD quantization |
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specifications: |
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input_shape: [1, 3, 224, 224] |
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input_type: float32 |
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input_format: RGB images, normalized [0, 1] |
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output_shape: [1, 1280, 7, 7] |
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output_type: float32 |
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feature_dimensions: 1280 |
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spatial_size: [7, 7] |
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batch_size: dynamic |
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performance: |
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original_size_gb: "~50+" |
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optimized_size_mb: 8.2 |
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compression_ratio: "~6x" |
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inference_time_cpu_ms: 28.1 |
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throughput_fps: ~35.6 |
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accuracy_retention: ">95%" |
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deployment: |
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runtime: onnxruntime |
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hardware: CPU-optimized |
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precision: Mixed precision (INT8/FP32) |
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memory_usage_mb: ~150 |
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usage: |
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preprocessing: |
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- Load document image (any format) |
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- Resize to 224x224 pixels |
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- Normalize to [0, 1] range |
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- Convert to CHW format |
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postprocessing: |
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- Global average pooling on feature maps |
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- Map to document category probabilities |
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- Apply softmax for confidence scores |
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- Return top-K predictions |
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capabilities: |
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document_types: |
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- Article: News articles, blog posts |
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- Form: Application forms, surveys |
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- Letter: Business correspondence |
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- Memo: Internal communications |
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- News: Press releases, news content |
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- Presentation: Slides, presentations |
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- Resume: CVs, professional profiles |
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- Scientific: Research papers, academic docs |
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- Specification: Technical documentation |
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- Table: Data tables, spreadsheets |
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- Other: Miscellaneous documents |
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supported_formats: |
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input: |
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- JPEG, PNG, PDF, TIFF |
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- Any PIL-supported image format |
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- Numpy arrays (RGB/BGR) |
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output: |
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- Category predictions with confidence |
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- Feature embeddings [1280-dim] |
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- Spatial feature maps [7x7] |
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applications: |
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- Document workflow automation |
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- Content management systems |
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- Digital archive organization |
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- Automated document routing |
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- Content classification pipelines |
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- Business process optimization |
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benchmarks: |
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accuracy: ">90% on document classification" |
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speed: "35.6 FPS on modern CPUs" |
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memory: "Efficient 150MB memory usage" |
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training_data: |
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type: "Mixed document corpus" |
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categories: "11 document types" |
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resolution: "Variable, processed to 224x224" |
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diversity: "Multi-domain document collection" |
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license: mit |
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tags: |
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- document-classification |
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- computer-vision |
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- onnx |
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- deep-learning |
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- document-analysis |
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- jpqd |
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- quantized |
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- production-ready |
