feat: Added Pytorch model

README.md

@@ -0,0 +1,105 @@
+---
+license: apache-2.0
+tags:
+- object-detection
+- pytorch
+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 holocron.models import model_from_hf_hub
+
+model = model_from_hf_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}}
+}
+```

config.json

@@ -0,0 +1 @@
+{"mean": [0.485, 0.456, 0.406], "std": [0.229, 0.224, 0.225], "arch": "fasterrcnn_mobilenet_v3_large_fpn", "interpolation": "bilinear", "input_shape": [3, 1024, 1024], "classes": ["background", "qr_code", "bar_code", "logo", "photo"]}

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d5b2490d6f0185186fc6e76323aa5192bf79cc2231ff9e01589a1619ea02f428
+size 76078985