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+  tags:
+    - onnx
+    - document-understanding
+    - layout-detection
+    - table-detection
+    - faria
+  pipeline_tag: object-detection
+  ---
+  # Faria ONNX Models
+  Pre-exported ONNX models used by [Faria](https://github.com/exto360-inc/faria), a document processing library with ML-powered
+  layout detection and table extraction. These files are ready for direct use with ONNX Runtime — no Python or conversion step
+  required.
+  ## Models
+  ### `detr_layout_detection.onnx` (~350 MB)
+  Document layout detection. Identifies structural elements across a page.
+  - **Source:** [`cmarkea/detr-layout-detection`](https://huggingface.co/cmarkea/detr-layout-detection)
+  - **ONNX opset:** 14
+  **Input**
+  | Name | Shape | Type |
+  |------|-------|------|
+  | `pixel_values` | `[batch, 3, 800, 800]` | float32 |
+  **Outputs**
+  | Name | Shape | Type | Description |
+  |------|-------|------|-------------|
+  | `logits` | `[batch, 100, 12]` | float32 | Class scores (11 classes + no-object) |
+  | `pred_boxes` | `[batch, 100, 4]` | float32 | Normalized boxes in `(cx, cy, w, h)` format |
+  **Class labels (DocLayNet)**
+  | Index | Label |
+  |-------|-------|
+  | 0 | Caption |
+  | 1 | Footnote |
+  | 2 | Formula |
+  | 3 | List-item |
+  | 4 | Page-footer |
+  | 5 | Page-header |
+  | 6 | Picture |
+  | 7 | Section-header |
+  | 8 | Table |
+  | 9 | Text |
+  | 10 | Title |
+  | 11 | (no object) |
+  **Post-processing**
+  1. Apply softmax to `logits` to get class probabilities
+  2. Filter detections by confidence threshold
+  3. Convert boxes from `(cx, cy, w, h)` to `(x1, y1, x2, y2)`
+  4. Scale boxes from `[0, 1]` to image dimensions
+  ---
+  ### `nemotron_table_structure.onnx` (~200 MB)
+  Table structure recognition. Detects cells, rows, columns, and headers within a detected table region.
+  - **Source:** [`nvidia/nemotron-table-structure-v1`](https://huggingface.co/nvidia/nemotron-table-structure-v1)
+  - **ONNX opset:** 18
+  **Inputs**
+  | Name | Shape | Type | Description |
+  |------|-------|------|-------------|
+  | `input` | `[1, 3, 1024, 1024]` | float32 | RGB image, normalized |
+  | `orig_sizes` | `[1, 2]` | int64 | Original image `[height, width]` |
+  **Outputs**
+  | Name | Shape | Type | Description |
+  |------|-------|------|-------------|
+  | `labels` | `[N]` | float32 | Class label per detection |
+  | `boxes` | `[N, 4]` | float32 | Normalized boxes `[x1, y1, x2, y2]` |
+  | `scores` | `[N]` | float32 | Confidence score per detection |
+  **Class labels**
+  | Index | Label |
+  |-------|-------|
+  | 1 | cell |
+  | 2 | row |
+  | 3 | column |
+  | 4 | header |
+  ---
+  ## Installation
+  These models are installed automatically by the [faria-install](https://github.com/exto360-inc/faria-install) script:
+  ```bash
+  curl -fsSL https://raw.githubusercontent.com/exto360-inc/faria-install/main/install.sh | bash -s -- --features idp
+  Or download directly:
+  # Layout detection
+  curl -fsSL https://huggingface.co/pavan-synkrato360/faria-models/resolve/main/detr_layout_detection.onnx \
+       -o detr_layout_detection.onnx
+  # Table structure
+  curl -fsSL https://huggingface.co/pavan-synkrato360/faria-models/resolve/main/nemotron_table_structure.onnx \
+       -o nemotron_table_structure.onnx
+  Export
+  These are custom ONNX exports from their respective source models. The export scripts are in the faria-install repository under
+   models/ if you need to re-export.
+  ---
+  **`config.json`**
+  ```json
+  {
+    "models": {
+      "detr_layout_detection": {
+        "filename": "detr_layout_detection.onnx",
+        "task": "document-layout-detection",
+        "source": "cmarkea/detr-layout-detection",
+        "onnx_opset": 14,
+        "input": {
+          "pixel_values": [1, 3, 800, 800]
+        },
+        "outputs": {
+          "logits": [1, 100, 12],
+          "pred_boxes": [1, 100, 4]
+        },
+        "classes": [
+          "Caption", "Footnote", "Formula", "List-item",
+          "Page-footer", "Page-header", "Picture", "Section-header",
+          "Table", "Text", "Title"
+        ]
+      },
+      "nemotron_table_structure": {
+        "filename": "nemotron_table_structure.onnx",
+        "task": "table-structure-recognition",
+        "source": "nvidia/nemotron-table-structure-v1",
+        "onnx_opset": 18,
+        "inputs": {
+          "input": [1, 3, 1024, 1024],
+          "orig_sizes": [1, 2]
+        },
+        "outputs": {
+          "labels": ["N"],
+          "boxes": ["N", 4],
+          "scores": ["N"]
+        },
+        "classes": {
+          "1": "cell",
+          "2": "row",
+          "3": "column",
+          "4": "header"
+        }
+      }
+    }
+  }