---
license: apache-2.0

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 `(cx, cy, w, h)`          |

**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`
2. Filter by confidence threshold
3. Convert `(cx, cy, w, h)` → `(x1, y1, x2, y2)`
4. Scale boxes to image size

---

### `nemotron_table_structure.onnx` (~200 MB)

Table structure recognition.

- **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                        |
| `orig_sizes` | `[1, 2]`            | int64   | `[height, width]`                |

**Outputs**

| Name    | Shape    | Type    |
|---------|----------|---------|
| labels  | `[N]`    | float32 |
| boxes   | `[N, 4]` | float32 |
| scores  | `[N]`    | float32 |

**Class labels**

| Index | Label  |
|-------|--------|
| 1     | cell   |
| 2     | row    |
| 3     | column |
| 4     | header |

---

## Installation

```bash
curl -fsSL https://raw.githubusercontent.com/exto360-inc/faria-install/main/install.sh | bash -s -- --features idp
```

Or download manually:

```bash
# 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
```

---

## 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"
      }
    }
  }
}
```