Holocron
PyTorch implementations of recent Computer Vision tricks (ReXNet, RepVGG, Unet3p, YOLOv4, CIoU loss, AdaBelief, PolyLoss, MobileOne). Other additions: AdEMAMix
Install / Use
/learn @frgfm/HolocronREADME
Implementations of recent Deep Learning tricks in Computer Vision, easily paired up with your favorite framework and model zoo.
Holocrons were information-storage datacron devices used by both the Jedi Order and the Sith that contained ancient lessons or valuable information in holographic form.
Source: Wookieepedia
Quick Tour
This project was created for quality implementations, increased developer flexibility and maximum compatibility with the PyTorch ecosystem. For instance, here is a short snippet to showcase how Holocron models are meant to be used:
from PIL import Image
from torchvision.transforms.v2 import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize
from torchvision.transforms.v2.functional import InterpolationMode
from holocron.models.classification import repvgg_a0
# Load your model
model = repvgg_a0(pretrained=True).eval()
# Read your image
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)
print(config['classes'][output.squeeze(0).argmax().item()], output.squeeze(0).softmax(dim=0).max())
Installation
Prerequisites
Python 3.11 (or higher) and uv/pip 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
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/.
Paper references
PyTorch layers for every need
- Activation: HardMish, NLReLU, FReLU
- Loss: Focal Loss, MultiLabelCrossEntropy, MixupLoss, ClassBalancedWrapper, ComplementCrossEntropy, MutualChannelLoss, DiceLoss, PolyLoss
- Convolutions: NormConv2d, Add2d, SlimConv2d, PyConv2d, Involution
- Regularization: DropBlock
- Pooling: BlurPool2d, SPP, ZPool
- Attention: SAM, LambdaLayer, TripletAttention
Models for vision tasks
- Image Classification: Res2Net (based on the great implementation from Ross Wightman), Darknet-24, Darknet-19, Darknet-53, CSPDarknet-53, ResNet, ResNeXt, TridentNet, PyConvResNet, ReXNet, SKNet, RepVGG, ConvNeXt, MobileOne.
- Object Detection: YOLOv1, YOLOv2, YOLOv4
- Semantic Segmentation: U-Net, UNet++, UNet3+
Vision-related operations
Trying something else than Adam
- Optimizer: LARS, Lamb, TAdam, AdamP, AdaBelief, Adan, and customized versions (RaLars), AdEMAMix
- Optimizer wrapper: Lookahead, Scout (experimental)
More goodies
Documentation
The full package documentation is available here for detailed specifications.
Demo app
The project includes a minimal demo app using Gradio
You can check the live demo, hosted on :hugs: HuggingFace Spaces :hugs: over here :point_down:
Reference scripts
Reference scripts are provided to train your models using holocron on famous public datasets. Those scripts currently support the following vision tasks:
Latency benchmark
You crave for SOTA performances, but you don't know whether it fits your needs in terms of latency?
In the table below, you will find a latency benchmark for all supported models:
| Arch | GPU mean (std) | CPU mean (std) | | ------------------------------------------------------------ | ----------------- | ------------------ | | repvgg_a0* | 3.14ms (0.87ms) | 23.28ms (1.21ms) | | repvgg_a1* | 4.13ms (1.00ms) | 29.61ms (0.46ms) | | repvgg_a2* | 7.35ms (1.11ms) | 46.87ms (1.27ms) | | [repvgg_b0](https://frgfm.github.io/Holocron/latest/models.html#holocron.models.repvgg_b
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