Image Classification
Transformers
PyTorch
Inference Endpoints
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---

license: apache-2.0
tags:
- image-classification
- pytorch
- onnx
datasets:
- imagenette
---



# Darknet-19 model

Pretrained on [ImageNette](https://github.com/fastai/imagenette). The Darknet-19 architecture was introduced in [this paper](https://pjreddie.com/media/files/papers/YOLO9000.pdf).


## Model description

The core idea of the author is to combine high throughput of a highway net with performance gains using better activations (Leaky ReLU) and batch normalization. This architecture is used as a backbone for YOLOv2.


## 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/darknet19").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/RedmonF16,

  author    = {Joseph Redmon and

               Ali Farhadi},

  title     = {{YOLO9000:} Better, Faster, Stronger},

  journal   = {CoRR},

  volume    = {abs/1612.08242},

  year      = {2016},

  url       = {http://arxiv.org/abs/1612.08242},

  eprinttype = {arXiv},

  eprint    = {1612.08242},

  timestamp = {Mon, 13 Aug 2018 16:48:25 +0200},

  biburl    = {https://dblp.org/rec/journals/corr/RedmonF16.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}

}

```