Cspdarknet53 Mish

🚀 CSP-Darknet-53 Mish model

This model is pretrained on ImageNette. The CSP-Darknet-53 Mish architecture was introduced in this paper. It is designed for image classification tasks.

🚀 Quick Start

This CSP-Darknet-53 Mish model is pretrained on ImageNette and can be used for image classification tasks. You can follow the installation and usage instructions below to get started.

✨ Features

• Pretrained on ImageNette.
• The architecture adds cross stage partial blocks in the convolutional stage and replaces activations with Mish.

📦 Installation

Prerequisites

Python 3.6 (or higher) and pip/conda are required to install Holocron.

Latest stable release

You can install the last stable release of the package using pypi as follows:

pip install pylocron

or using conda:

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 first):

git clone https://github.com/frgfm/Holocron.git
pip install -e Holocron/.

💻 Usage Examples

Basic Usage

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)

📚 Documentation

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.

📄 License

This project is licensed under the Apache-2.0 license.

📚 Citation

Original paper

@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

@software{Fernandez_Holocron_2020,
author = {Fernandez, François-Guillaume},
month = {5},
title = {{Holocron}},
url = {https://github.com/frgfm/Holocron},
year = {2020}
}