Darknet-19 model
Pretrained on ImageNette. The Darknet-19 architecture was introduced in this paper.
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/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 instructions
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
@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
@software{Fernandez_Holocron_2020,
author = {Fernandez, François-Guillaume},
month = {5},
title = {{Holocron}},
url = {https://github.com/frgfm/Holocron},
year = {2020}
}
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