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---
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license: apache-2.0
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tags:
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- object-detection
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- pytorch
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library_name: doctr
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datasets:
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- docartefacts
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---
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# Faster-RCNN model
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Pretrained on [DocArtefacts](https://mindee.github.io/doctr/datasets.html#doctr.datasets.DocArtefacts). The Faster-RCNN architecture was introduced in [this paper](https://arxiv.org/pdf/1506.01497.pdf).
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## Model description
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The core idea of the author is to unify Region Proposal with the core detection module of Fast-RCNN.
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## Installation
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### Prerequisites
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Python 3.6 (or higher) and [pip](https://pip.pypa.io/en/stable/) are required to install docTR.
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### Latest stable release
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You can install the last stable release of the package using [pypi](https://pypi.org/project/python-doctr/) as follows:
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```shell
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pip install python-doctr[torch]
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```
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### Developer mode
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Alternatively, if you wish to use the latest features of the project that haven't made their way to a release yet, you can install the package from source *(install [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) first)*:
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```shell
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git clone https://github.com/mindee/doctr.git
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pip install -e doctr/.[torch]
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```
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## Usage instructions
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```python
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from PIL import Image
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import torch
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from torchvision.transforms import Compose, ConvertImageDtype, PILToTensor
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from doctr.models.obj_detection.factory import from_hub
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model = from_hub("mindee/fasterrcnn_mobilenet_v3_large_fpn").eval()
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img = Image.open(path_to_an_image).convert("RGB")
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# Preprocessing
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transform = Compose([
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PILToTensor(),
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ConvertImageDtype(torch.float32),
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])
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input_tensor = transform(img).unsqueeze(0)
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# Inference
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with torch.inference_mode():
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output = model(input_tensor)
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```
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## Citation
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Original paper
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```bibtex
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@article{DBLP:journals/corr/RenHG015,
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author = {Shaoqing Ren and
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Kaiming He and
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Ross B. Girshick and
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Jian Sun},
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title = {Faster {R-CNN:} Towards Real-Time Object Detection with Region Proposal
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Networks},
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journal = {CoRR},
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volume = {abs/1506.01497},
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year = {2015},
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url = {http://arxiv.org/abs/1506.01497},
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eprinttype = {arXiv},
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eprint = {1506.01497},
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timestamp = {Mon, 13 Aug 2018 16:46:02 +0200},
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biburl = {https://dblp.org/rec/journals/corr/RenHG015.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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```
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Source of this implementation
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```bibtex
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@misc{doctr2021,
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title={docTR: Document Text Recognition},
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author={Mindee},
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year={2021},
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publisher = {GitHub},
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howpublished = {\url{https://github.com/mindee/doctr}}
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}
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```
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