feat: Added PyTorch model

README.md

@@ -1,3 +1,116 @@
 ---
 license: apache-2.0
+tags:
+- image-classification
+- pytorch
+- onnx
+datasets:
+- imagenette
 ---
+
+
+# ReXNet-1.5x model
+
+Pretrained on [ImageNette](https://github.com/fastai/imagenette). The ReXNet architecture was introduced in [this paper](https://arxiv.org/pdf/2007.00992.pdf).
+
+
+## Model description
+
+The core idea of the author is to add a customized Squeeze-Excitation layer in the residual blocks that will prevent channel redundancy.
+
+
+## Installation
+
+### Prerequisites
+
+Python 3.6 (or higher) and [pip](https://pip.pypa.io/en/stable/)/[conda](https://docs.conda.io/en/latest/miniconda.html) are required to install Holocron.
+
+### Latest stable release
+
+You can install the last stable release of the package using [pypi](https://pypi.org/project/pylocron/) as follows:
+
+```shell
+pip install pylocron
+```
+
+or using [conda](https://anaconda.org/frgfm/pylocron):
+
+```shell
+conda install -c frgfm pylocron
+```
+
+### 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/frgfm/Holocron.git
+pip install -e Holocron/.
+```
+
+
+## Usage instructions
+
+```python
+from PIL import Image
+from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize
+from torchvision.transforms.functional import InterpolationMode
+from holocron.models import model_from_hf_hub
+
+model = model_from_hf_hub("frgfm/rexnet1_5x").eval()
+
+img = Image.open(path_to_an_image).convert("RGB")
+
+# Preprocessing
+config = model.default_cfg
+transform = Compose([
+    Resize(config['input_shape'][1:], interpolation=InterpolationMode.BILINEAR),
+    PILToTensor(),
+    ConvertImageDtype(torch.float32),
+    Normalize(config['mean'], config['std'])
+])
+
+input_tensor = transform(img).unsqueeze(0)
+
+# Inference
+with torch.inference_mode():
+    output = model(input_tensor)
+probs = output.squeeze(0).softmax(dim=0)
+```
+
+
+## Citation
+
+Original paper
+
+```bibtex
+@article{DBLP:journals/corr/abs-2007-00992,
+  author    = {Dongyoon Han and
+               Sangdoo Yun and
+               Byeongho Heo and
+               Young Joon Yoo},
+  title     = {ReXNet: Diminishing Representational Bottleneck on Convolutional Neural
+               Network},
+  journal   = {CoRR},
+  volume    = {abs/2007.00992},
+  year      = {2020},
+  url       = {https://arxiv.org/abs/2007.00992},
+  eprinttype = {arXiv},
+  eprint    = {2007.00992},
+  timestamp = {Mon, 06 Jul 2020 15:26:01 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2007-00992.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```
+
+Source of this implementation
+
+```bibtex
+@software{Fernandez_Holocron_2020,
+author = {Fernandez, François-Guillaume},
+month = {5},
+title = {{Holocron}},
+url = {https://github.com/frgfm/Holocron},
+year = {2020}
+}
+```

config.json

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+{"mean": [0.485, 0.456, 0.406], "std": [0.229, 0.224, 0.225], "arch": "rexnet1_5x", "interpolation": "bilinear", "input_shape": [3, 224, 224], "classes": ["tench", "English springer", "cassette player", "chain saw", "church", "French horn", "garbage truck", "gas pump", "golf ball", "parachute"]}

pytorch_model.bin

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+oid sha256:3930c84da0f05a6c34f5ff7308e628ee8f3fdd719c82929a8bbcba5ac2759280
+size 31674977