| | --- |
| | license: apache-2.0 |
| | tags: |
| | - image-classification |
| | - pytorch |
| | datasets: |
| | - frgfm/imagenette |
| | --- |
| | |
| |
|
| | # CSP-Darknet-53 Mish model |
| |
|
| | 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). |
| |
|
| |
|
| | ## Model description |
| |
|
| | 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. |
| |
|
| |
|
| | ## 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/cspdarknet53_mish").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-1911-11929, |
| | author = {Chien{-}Yao Wang and |
| | Hong{-}Yuan Mark Liao and |
| | I{-}Hau Yeh and |
| | Yueh{-}Hua Wu and |
| | Ping{-}Yang Chen and |
| | Jun{-}Wei Hsieh}, |
| | title = {CSPNet: {A} New Backbone that can Enhance Learning Capability of {CNN}}, |
| | journal = {CoRR}, |
| | volume = {abs/1911.11929}, |
| | year = {2019}, |
| | url = {http://arxiv.org/abs/1911.11929}, |
| | eprinttype = {arXiv}, |
| | eprint = {1911.11929}, |
| | timestamp = {Tue, 03 Dec 2019 20:41:07 +0100}, |
| | biburl = {https://dblp.org/rec/journals/corr/abs-1911-11929.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} |
| | } |
| | ``` |
| |
|
