Pretrained for image classification

This release adds implementations of both image classification and object detection models.

Note: holocron 0.1.0 requires PyTorch 1.2 and torchvision 0.4 or newer.

Highlights

models

Implementations of deep learning models
New

• Add implementations of Darknet-24, Darknet-19 and Darknet-53 (#20, #22, #23, #24)
• Add implementations of YOLOv1 and YOLOv2 (#22, #23).

nn

Neural networks building blocks
New

• Add weight initialization function (#24)
• Add mish & nl_relu activations
• Add implementations of focal loss, multi label cross-entropy loss and label smoothing cross-entropy loss (#16, #17, #25)
• Add mixup loss wrapper (#27)

ops

High-performance batch operations
New

• Add implementations of distance IoU and complete IoU losses (#12)

optim

Optimizer and learning rate schedulers
New

• Add implementations for LARS, Lamb, RAdam, and Lookahead (#6)
• Add an implementation of OneCycle scheduler

Documentation

Online resources for potential users
New

• Add sphinx automatic documentation build for existing features (#7, #8, #13, #21)
• Add contribution guidelines (#1)
• Add installation & usage instructions in readme (#1, #2)

References

Verifications of the package well-being before release
New

• Add a training script for Imagenette (#28)

Others

Other tools and implementations

• Add ̀lr_finder` to estimate the optimal starting learning rate (#26).
• Add 'mixup_collate` to use Mixup on existing DataLoader (#27)