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--- |
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tags: |
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- object-detection |
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- rf-detr |
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- commonforms |
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datasets: |
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- jbarrow/CommonForms |
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--- |
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# RF-DETR Fine-tuned on CommonForms |
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This model is an RF-DETR (small) fine-tuned on the [CommonForms](jbarrow/CommonForms) dataset for form field detection. |
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## Model Details |
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- **Model Type:** RF-DETR small |
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- **Dataset:** jbarrow/CommonForms |
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- **Classes:** 3 |
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- **Epochs:** 1 |
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- **Batch Size:** 4 (grad_accum: 4) |
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## Classes |
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[ |
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{ |
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"id": 0, |
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"name": "class_0", |
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"supercategory": "form_element" |
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}, |
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{ |
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"id": 1, |
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"name": "class_1", |
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"supercategory": "form_element" |
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}, |
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{ |
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"id": 2, |
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"name": "class_2", |
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"supercategory": "form_element" |
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} |
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] |
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## Usage |
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```python |
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import torch |
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from PIL import Image |
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# Load model |
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model_path = "path/to/rfdetr_model.pt" |
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# Note: You'll need the rfdetr library installed |
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from rfdetr import RFDETRSmall |
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model = RFDETRSmall() |
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model.load_state_dict(torch.load(model_path)) |
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model.eval() |
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# Run inference |
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image = Image.open("form.jpg") |
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predictions = model.predict(image) |
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print(predictions) |
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``` |
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## Training Details |
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- Learning Rate: 0.0001 |
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- Effective Batch Size: 16 |
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- Dataset: Trained on CommonForms (form field detection) |
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## Metrics |
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(Add your evaluation metrics here after running evaluation) |
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## Citation |
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```bibtex |
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@misc{rfdetr-commonforms, |
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author = {Your Name}, |
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title = {RF-DETR Fine-tuned on CommonForms}, |
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year = {2024}, |
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publisher = {HuggingFace}, |
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howpublished = {\url{https://huggingface.co/andrewluo/rfdetr-commonforms-test}} |
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} |
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``` |
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