Package: mlfactory
Aboutmlfactoryis a simple modular wrapper library that abstracts several different types of neural network architectures and techniques for computer vision (pytorch and tensorflow backend) providing seemless easy to use training in a few lines of code.Using the standard modular philosophy you can also define your own neural network in pytorch/tensorflow, and if youre lazy to write the data loaders or the training loop then pass the network to our submodules !, or vice versa.Table of contentsGetting StartedOut of box colab usageGetting Startedpip install mlfactoryOut of box colab usageMachine Learning and AI applications full pipelineHigh definition mapping using monocular camera (using monocular depth estimation and superglue feature extractor)https://colab.research.google.com/drive/1lZYHjYszROIvMjtZgAp7r5eL1YZC9x9M?usp=sharingSimple and fast visual odometry directly from MOV files and output pose trajectory in open3dhttps://colab.research.google.com/drive/1Nr1nYFBKieDQG6UeNsnrV4gHh-l-65G0?usp=sharingCompose machine learning applications in a modular way(NYUV2 dataloader) Easy monocular depth estimationhttps://colab.research.google.com/drive/1T2gONs_gst4zpdS7fBoIaQclgg3J2Jgk?usp=sharingFinetune deeplabv3 for any general binary segmentation using less than 100 sampleshttps://colab.research.google.com/drive/1y0wirrjuoha3_SQk52IMJmL99iPGT6DZ?usp=sharingAnnotation and other computer vision utilitiesPolygon annotation tool allowing to create polygon masks for segmentation directly in colabhttps://colab.research.google.com/drive/1YUoMU3H_m9KM6xTrAKAy9ebiDWWzXTle?usp=sharingEasy usage of superglue neural network based image feature matchinghttps://colab.research.google.com/drive/1fqnW1-Dlwz3fYlacTjMUmu5gxreGfjj6?usp=sharingEasy usage of holistically nested edge detection for generic high level edge detectionhttps://colab.research.google.com/drive/174STj0gsUZ4qOrbj_WFXhtSfwcef-oMi?usp=sharingMore examples and use casesFunctionality to record gameplay from the screen for training behavior cloning, and also play model policies using pyautogui. See -https://github.com/Homagn/mlfactory/blob/master/datacreators/utils/gameplay_recorder.pyDataloader class for images and labels using jsonfiles (Data needs to be saved in folders, each folder containing images and json labels). See -https://github.com/Homagn/mlfactory/blob/master/dataloaders/imgjsonloader.pyTraining high quality and low weight variational autoencoders for larger dimension images and using custom data (uses subpixel convolution for upsampling). See -https://github.com/Homagn/mlfactory/blob/master/examples/variational_encoders/main.pyLatent GPT models for behavior cloning. Uses encoded representations from vae to train a GPT model for behavior cloning. See -https://github.com/Homagn/mlfactory/blob/master/examples/behavior_cloning/train_latent_gpt.pyVision transformer minimal example for classification. See -https://github.com/Homagn/mlfactory/blob/master/models/pytorch/ViT.pyTimesformer model (pure attention based model for video classification ). See -https://github.com/Homagn/mlfactory/blob/master/models/pytorch/timesformer.pyUpcomingapplications/multiview_sba_recon integrate with the pip project as a functionality by creating demo in examples/ folderintegrate the photometric bundle adjustment part of applications/multiview_sba_recon in deep_modular_reconstruction moduleafter integrating applications/multiview_sba_recon use it to input estimated poses to construct nerfs (pytorch)examples/variational_encoders/main_b.py, integrate variational encoders with the pip project and remove results folder in it taking up spaceexamples/behavior_cloning/train_latent_gpt.py , test_latent_gpt.py integrate with the pip projectbehavior transformers and using GPT -https://github.com/notmahi/betandhttps://github.com/notmahi/miniBETbase transformer model definition + segformer model definitions pytorchdiffusion models from scratch pytorchMIRnet keras for low light image enhancement -https://keras.io/examples/vision/mirnet/SRGAN for image superresolution -https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Super-Resolutiondifferentiable pointcloud generation models -https://github.com/puhsu/point-cloudsimage animation generation models -https://github.com/snap-research/articulated-animation(code cloned in /ml/misctools/articulated-animation/, run the working demo-> demo_outofbox.py)FCOSnet pytorch -https://github.com/VectXmy/FCOS.Pytorchdataloader for ouster lidar datasetsCoco bounding box dataloader colabtum_rgbd dataloaderResnet finetunining module for 2D image regregression (pose estimation example)