readme: ---
  license: apache-2.0
  tags:
  - object-detection
  - pytorch
  library_name: doctr
  datasets:
  - docartefacts
  ---
  
  
  # Faster-RCNN model
  
  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).
  
  
  ## Model description
  
  The core idea of the author is to unify Region Proposal with the core detection module of Fast-RCNN.
  
  
  ## Installation
  
  ### Prerequisites
  
  Python 3.6 (or higher) and [pip](https://pip.pypa.io/en/stable/) are required to install docTR.
  
  ### Latest stable release
  
  You can install the last stable release of the package using [pypi](https://pypi.org/project/python-doctr/) as follows:
  
  ```shell
  pip install python-doctr[torch]
  ```
  
  ### Developer mode
  
  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)*:
  
  ```shell
  git clone https://github.com/mindee/doctr.git
  pip install -e doctr/.[torch]
  ```
  
  
  ## Usage instructions
  
  ```python
  from PIL import Image
  import torch
  from torchvision.transforms import Compose, ConvertImageDtype, PILToTensor
  from doctr.models.obj_detection.factory import from_hub
  
  model = from_hub("mindee/fasterrcnn_mobilenet_v3_large_fpn").eval()
  
  img = Image.open(path_to_an_image).convert("RGB")
  
  # Preprocessing
  transform = Compose([
      PILToTensor(),
      ConvertImageDtype(torch.float32),
  ])
  
  input_tensor = transform(img).unsqueeze(0)
  
  # Inference
  with torch.inference_mode():
      output = model(input_tensor)
  ```
  
  
  ## Citation
  
  Original paper
  
  ```bibtex
  @article{DBLP:journals/corr/RenHG015,
    author    = {Shaoqing Ren and
                 Kaiming He and
                 Ross B. Girshick and
                 Jian Sun},
    title     = {Faster {R-CNN:} Towards Real-Time Object Detection with Region Proposal
                 Networks},
    journal   = {CoRR},
    volume    = {abs/1506.01497},
    year      = {2015},
    url       = {http://arxiv.org/abs/1506.01497},
    eprinttype = {arXiv},
    eprint    = {1506.01497},
    timestamp = {Mon, 13 Aug 2018 16:46:02 +0200},
    biburl    = {https://dblp.org/rec/journals/corr/RenHG015.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
  }
  ```
  
  Source of this implementation
  
  ```bibtex
  @misc{doctr2021,
      title={docTR: Document Text Recognition},
      author={Mindee},
      year={2021},
      publisher = {GitHub},
      howpublished = {\url{https://github.com/mindee/doctr}}
  }
  ```
  