Facilitates implementing deep neural-network backbones, data augmentations

facilitates implementing deep neural-network backbones, data augmentations, optimizers and learning schedulers.

• backbones
• loss functions
• augumentation styles
• optimizers
• schedulers
• data types
• visualizations

Refer to docs/installation.md for installion of general_backbone package.

Model backone

Currently, general_backbone supports more than 70 type of resnet models such as: resnet18, resnet34, resnet50, resnet101, resnet152, resnext50.

All models is supported can be found in general_backbone.list_models() function:

import general_backbone
general_backbone.list_models()

Results

{'resnet': ['resnet18', 'resnet18d', 'resnet34', 'resnet34d', 'resnet26', 'resnet26d', 'resnet26t', 'resnet50', 'resnet50d', 'resnet50t', 'resnet101', 'resnet101d', 'resnet152', 'resnet152d', 'resnet200', 'resnet200d', 'tv_resnet34', 'tv_resnet50', 'tv_resnet101', 'tv_resnet152', 'wide_resnet50_2', 'wide_resnet101_2', 'resnext50_32x4d', 'resnext50d_32x4d', 'resnext101_32x4d', 'resnext101_32x8d', 'resnext101_64x4d', 'tv_resnext50_32x4d', 'ig_resnext101_32x8d', 'ig_resnext101_32x16d', 'ig_resnext101_32x32d', 'ig_resnext101_32x48d', 'ssl_resnet18', 'ssl_resnet50', 'ssl_resnext50_32x4d', 'ssl_resnext101_32x4d', 'ssl_resnext101_32x8d', 'ssl_resnext101_32x16d', 'swsl_resnet18', 'swsl_resnet50', 'swsl_resnext50_32x4d', 'swsl_resnext101_32x4d',