Major Projects
- Trackers: Trackers is a unified library for object tracking featuring clean room re-implementations of leading multi-object tracking algorithms. I am one of the core maintainers actively implementing SoTA multi-object trackering techniques, Re-ID models, training, and fine-tuning pipelines. Check out the docs for more information.
- WandB Models: WandB Models is the AI developer platform used to train and fine-tune models, and manage models from experimentation to production. I have contributed mojor integrations of WandB Models with open-source ML libraries like Hugging Face Diffusers, Hugging Face AutoTrain, Keras, MMEngine, PyTorch Geometric, Ultralytics, YOLOv5, MONAI, etc. I have also authored numerous technical reports and developed experimental tooling for ML practitioners.
- WandB Weave: WandB Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. I have contributed mojor integrations of Weave with LLM SDKs like Groq, Hugging Face Hub, Google; and LLMOps frameworks like DSPy, Instructor, and SmolAgents.
- Hemm: Holistic Evaluation of Multi-modal Generative Models: Hemm is a library for performing comprehensive benchmark of text-to-image diffusion models on image quality and prompt comprehension integrated with Weights & Biases and Weave.I am currently actively working on this project. Check out the docs for more information.
- Weights & Biases Addons: Weights & Biases Addons is a repository that provides of integrations and utilities that will supercharge your Weights & Biases workflows. Its a repositpry built and maintained by WandB users for WandB users. The library hosts experimental utilities and integrations built using Weights & Biases. I am currently actively working on this project. Check out the docs for more information.
- Restorers: Restorers is a library provide out-of-the-box TensorFlow implementations of SoTA image and video restoration models for tasks such as low-light enhancement, denoising, deblurring, super-resolution, etc. You can read more about it in this WandB report.
Deep Learning examples published on keras.io
Other Interesting Projects
- Radium: A small and lightweight Ray Tracing Engine written in C++ that runs on the CPU using shared-memory multiprocessing.
- Colorization using Optimization: Python and C++ implementations of a user-guided image/video colorization technique as proposed by the paper Colorization Using Optimization. The algorithm is based on a simple premise; neighboring pixels in space-time that have similar intensities should have similar colors. This premise is formalized using a quadratic cost function that obtains an optimization problem that can be solved efficiently using standard techniques. While using this alogorithm, an artist only needs to annotate the image with a few color scribbles or visual clues, and the indicated colors are automatically propagated in both space and time to produce a fully colorized image or sequence. The annotation can be done using any drawing tool such as JSPaint or Gimp.
- Deep Deterministic Policy Gradients: Pytorch implementation of the Deep Deterministic Policy Gradients Algorithm for Continuous Control as described by the paper Continuous control with deep reinforcement learning.
- Twin Delayed DDGP: Pytorch Implementation of Twin Delayed Deep Deterministic Policy Gradients Algorithm for Continuous Control as described by the paper Addressing Function Approximation Error in Actor-Critic Methods.
- Arxiv2Kindle: Arxiv2Kindle is a simple script written in python that converts LaTeX source downloaded from Arxiv and recompiles it to better fit a reading device (such as a Kindle).
- Manga Scraper: A a python package that downloads Manga into chapterwise PDF files or a single PDF file from various sources. It basically adds a post-processing layer on top of the basic functionality by
mangadl-bash created by Akianonymus in order to convert the downloaded manga into chapter-wise PDF files or a single giant PDF file.