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---
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license: apache-2.0
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tags:
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- image-classification
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- pytorch
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datasets:
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- imagenette
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---
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# CSP-Darknet-53 Mish model
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Pretrained on [ImageNette](https://github.com/fastai/imagenette). The CSP-Darknet-53 Mish architecture was introduced in [this paper](https://arxiv.org/pdf/1911.11929.pdf).
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## Model description
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The core idea of the author is to change the convolutional stage by adding cross stage partial blocks in the architecture and replace activations with Mish.
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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/)/[conda](https://docs.conda.io/en/latest/miniconda.html) are required to install Holocron.
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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/pylocron/) as follows:
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```shell
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pip install pylocron
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```
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or using [conda](https://anaconda.org/frgfm/pylocron):
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```shell
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conda install -c frgfm pylocron
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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/frgfm/Holocron.git
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pip install -e Holocron/.
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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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from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize
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from torchvision.transforms.functional import InterpolationMode
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from holocron.models import model_from_hf_hub
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model = model_from_hf_hub("frgfm/cspdarknet53_mish").eval()
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img = Image.open(path_to_an_image).convert("RGB")
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# Preprocessing
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config = model.default_cfg
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transform = Compose([
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Resize(config['input_shape'][1:], interpolation=InterpolationMode.BILINEAR),
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PILToTensor(),
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ConvertImageDtype(torch.float32),
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Normalize(config['mean'], config['std'])
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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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probs = output.squeeze(0).softmax(dim=0)
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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/abs-1911-11929,
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author = {Chien{-}Yao Wang and
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Hong{-}Yuan Mark Liao and
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I{-}Hau Yeh and
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Yueh{-}Hua Wu and
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Ping{-}Yang Chen and
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Jun{-}Wei Hsieh},
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title = {CSPNet: {A} New Backbone that can Enhance Learning Capability of {CNN}},
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journal = {CoRR},
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volume = {abs/1911.11929},
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year = {2019},
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url = {http://arxiv.org/abs/1911.11929},
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eprinttype = {arXiv},
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eprint = {1911.11929},
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timestamp = {Tue, 03 Dec 2019 20:41:07 +0100},
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biburl = {https://dblp.org/rec/journals/corr/abs-1911-11929.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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@software{Fernandez_Holocron_2020,
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author = {Fernandez, François-Guillaume},
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month = {5},
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title = {{Holocron}},
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url = {https://github.com/frgfm/Holocron},
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year = {2020}
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}
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```
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