docs: Updated README and json

README.md

@@ -1,3 +1,123 @@
 ---
 license: apache-2.0
+tags:
+- image-classification
+- pytorch
+- onnx
+datasets:
+- wildfire
 ---
+
+
+# MobileNet V3 - Small model
+
+Pretrained on a dataset for wildfire binary classification (soon to be shared). The MobileNet V3 architecture was introduced in [this paper](https://arxiv.org/pdf/1905.02244.pdf).
+
+
+## Model description
+
+The core idea of the author is to simplify the final stage, while using SiLU as activations and making Squeeze-and-Excite blocks larger.
+
+
+## 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 PyroVision.
+
+### Latest stable release
+
+You can install the last stable release of the package using [pypi](https://pypi.org/project/pyrovision/) as follows:
+
+```shell
+pip install pyrovision
+```
+
+or using [conda](https://anaconda.org/pyronear/pyrovision):
+
+```shell
+conda install -c pyronear pyrovision
+```
+
+### 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/pyronear/pyro-vision.git
+pip install -e pyro-vision/.
+```
+
+
+## Usage instructions
+
+```python
+from PIL import Image
+from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize
+from torchvision.transforms.functional import InterpolationMode
+from pyrovision.models import model_from_hf_hub
+
+model = model_from_hf_hub("pyronear/mobilenet_v3_small").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-1905-02244,
+  author    = {Andrew Howard and
+               Mark Sandler and
+               Grace Chu and
+               Liang{-}Chieh Chen and
+               Bo Chen and
+               Mingxing Tan and
+               Weijun Wang and
+               Yukun Zhu and
+               Ruoming Pang and
+               Vijay Vasudevan and
+               Quoc V. Le and
+               Hartwig Adam},
+  title     = {Searching for MobileNetV3},
+  journal   = {CoRR},
+  volume    = {abs/1905.02244},
+  year      = {2019},
+  url       = {http://arxiv.org/abs/1905.02244},
+  eprinttype = {arXiv},
+  eprint    = {1905.02244},
+  timestamp = {Thu, 27 May 2021 16:20:51 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-1905-02244.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```
+
+Source of this implementation
+
+```bibtex
+@software{chintala_torchvision_2017,
+author = {Chintala, Soumith},
+month = {4},
+title = {{Torchvision}},
+url = {https://github.com/pytorch/vision},
+year = {2017}
+}
+```

config.json

@@ -0,0 +1 @@
+{"mean": [0.485, 0.456, 0.406], "std": [0.229, 0.224, 0.225], "arch": "mobilenet_v3_small", "interpolation": "bilinear", "input_shape": [3, 224, 224], "classes": ["Wildfire"]}