Bug Fix Release

FIxes Memory Leak Errror #102

0.2.1rc2

Update setup.py

0.2.1rc1

rc1 for 0.2.1

New Backbones, PyTorch 1.8.1 PyTorch Lightning 1.2.6 Compatibility

The library now requires PyTorch == 1.8.1. Also torchvision==0.9.1 and PyTorchLightning 1.2.6.
This is a minor release with few new features.
We have standardized the codebase, as well as added more test coverage.

New Backbones

#94 Support for Mobilenet v3 large Backbones For Classification and Object Detection.
#99 Add support For PyTorch Lightning 1.2

BC Breaking

#89 PascalCase all the Classes.

Build

#95 Code QL Analysis

0.2.0rc1

Update setup.py

0.1.1post: Post release 0.1.1 (#91)

* Post release 0.1

* update setup

New Features, PyTorch 1.7.1 PyTorch Lightning 1.1 Support

This new release cleans up our codebases internally. This makes the library more robust and easier to maintain.
Also, we have new examples and prototype APIs.

Code Cleanup and Bug Fixes

• We cleaned up code internally and created a stable requirements.txt file. Now, we pin versions in our requirements instead of hardcoding them in CI checks.
• It is recommended to install PyTorch as mentioned on PyTorch website and NOT run pip install -r requirements.txt.
• Our requirements.txt file helps us to view Dependabot alerts as well as see sub dependencies graph.

#56 Cleaning up Type Annotations
#60 Fixed some typos and small fixes.
#63 Drop timm requirements.
#65 Clean up of codebase like Pl_bolts, making it easier to create a model zoo
#77 Fixing CI and file encoding issue over MacOS and windows.
#69 Enhanced Dummy Detection dataset for better internal testing
#79 Refactored tests to check for normalized bounding boxes and class starts.
#88 Added requirements.txt file which makes it easier to keep consistency.
#90 Fixed Detr Name and weights.

New Models and Tutorials

#76 Supports Wide Resnet for Classification as well as FPN based backbone in Detection.
#81 Example to train models with Weights and Biases.
#74 Example to train DETR over COCO Dataset.
#68 SWAV Weights for Resnet models in Detection and Classification tasks.

Prototype APIs

As a small release, we have started prototyping new APIs. We expect them to be stable in subsequent releases.

#86 Losses API for common losses in Computer Vision tasks.
#80 Layers API for layers pertaining to Computer Vision.

Documentation

• We are planning to build Sphinx Documentation with autodoc features. Thanks to @SauravMaheshkar .
• This is work in progress and contributions are welcome

#54 Adds Sphinx Docs
#67 Fixes a Few issues with Sphinx
#58 Added CI to deploy Docs over GitHub pages.

Since this release is fully compatible with PyTorch Lightning 1.1, people can use all features of Lightning such as Shared Training, etc.

Super thanks to @hassiahk @zhiqwang @zlapp @SauravMaheshkar for making this release possible!

0.1.1rc3

Update setup.py

0.1.1rc2: Fixes requirements to be installable, fixes CUDA Tests (#88)

* fixes requirements to be installable

* fixes CI and req

* retry install

* explicit to cpu

0.1.1rc1: Updates CI and docs for 0.1.1 (#84)

* updates CI and docs for 0.1.1

* update support sheet