Upload FFDNet-S-cpu weights and README

FFDNet-S.pt

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README.md

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+---
+pipeline_tag: object-detection
+tags:
+- form-field-detection
+- documents
+- commonforms
+library_name: commonforms
+datasets:
+  - jbarrow/CommonForms
+---
+
+🪄 Automatically convert a PDF into a fillable form.
+
+[💻 Hosted Models (detect.semanticdocs.org)](https://detect.semanticdocs.org) | [📄 CommonForms Paper](https://arxiv.org/abs/2509.16506) | [🤗 Dataset](https://huggingface.co/datasets/jbarrow/CommonForms) | [🦾 Models](https://github.com/jbarrow/commonforms/tree/main/commonforms/models)
+
+![Pipeline](https://raw.githubusercontent.com/jbarrow/commonforms/main/assets/pipeline.png)
+
+# FFDNet-S
+
+FFDNet-S is the 6 million parameter object detector trained on the dataset from the paper [CommonForms: A Large, Diverse Dataset for Form Field Detection](https://arxiv.org/abs/2509.16506).
+The model detects widgets from among three classes: TextBoxes, ChoiceButtons (checkboxes), and Signature fields.
+
+## Results
+
+| Model | Text | Choice | Signature | AP (↑) |
+|-------|------|--------|-----------|--------|
+| FFDNet-S (1216px) | 61.5 | 71.3 | 84.2 | 72.3 |
+| FFDNet-L (1216px) | 71.4 | 78.1 | 93.5 | 81.0 |
+
+
+## Installation
+
+The `commonforms` package can be installed with either `uv` or `pip`, feel free to choose your package manager flavor.
+The `uv` command:
+
+```sh
+uv pip install commonforms
+```
+
+The `pip` command:
+
+```
+pip install commonforms
+```
+
+Once it's installed, you should be able to run the CLI command on ~any PDF.
+Refer to [the `commonforms` documentation](https://github.com/jbarrow/commonforms) for the latest information.
+
+## CLI
+
+The simplest usage will run inference on your CPU using the default suggested settings:
+
+```
+commonforms <input.pdf> <output.pdf>
+```
+
+| Input | Output |
+|-------|--------|
+| ![Input PDF](https://raw.githubusercontent.com/jbarrow/commonforms/main/assets/input.png) | ![Output PDF](https://raw.githubusercontent.com/jbarrow/commonforms/main/assets/output.png) |
+
+### Command Line Arguments
+
+| Argument | Type | Default | Description |
+|----------|------|---------|-------------|
+| `input` | Path | Required | Path to the input PDF file |
+| `output` | Path | Required | Path to save the output PDF file |
+| `--model` | str | `FFDNet-L` | Model name (FFDNet-L/FFDNet-S) or path to custom .pt file |
+| `--keep-existing-fields` | flag | `False` | Keep existing form fields in the PDF |
+| `--use-signature-fields` | flag | `False` | Use signature fields instead of text fields for detected signatures |
+| `--device` | str | `cpu` | Device for inference (e.g., `cpu`, `cuda`, `0`) |
+| `--image-size` | int | `1600` | Image size for inference |
+| `--confidence` | float | `0.3` | Confidence threshold for detection |
+| `--fast` | flag | `False` | If running on a CPU, you can trade off accuracy for speed and run in about half the time |
+
+## CommonForms API
+
+In addition to the CLI, you can use
+
+```py
+from commonforms import prepare_form
+
+prepare_form(
+    "path/to/input.pdf",
+    "path/to/output.pdf"
+)
+```
+
+All of the above arguments are keyword arguments to the `prepare_form` function.
+E.g. if you want to prepare a with signature fields and keep existing fields at 1216 resolution, you would run:
+
+```
+from commonforms import prepare_form
+
+prepare_form(
+    "path/to/input.pdf",
+    "path/to/output.pdf",
+    keep_existing_fields=True,
+    use_signature_fields=True,
+    image_size=1216
+)
+```
+
+## References
+
+* [CommonForms: A Large, Diverse Dataset for Form Field Detection](https://arxiv.org/abs/2509.16506)