The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

The fastai deep learning library

The fastai book, published as Jupyter Notebooks

Learn OpenCV : C++ and Python Examples

Neural Networks: Zero to Hero

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filte…

Public facing notes page

Reference PyTorch implementation and models for DINOv3

Collection of useful data science topics along with articles, videos, and code

NeRF (Neural Radiance Fields) and NeRF in the Wild using pytorch-lightning

Paper reading notes on Deep Learning and Machine Learning

[ECCV'2024] Gaussian Grouping for open-world Anything reconstruction, segmentation and editing.

The ApolloScape Open Dataset for Autonomous Driving and its Application.

A library for ML benchmarking. It's powerful.

Official code release for "GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis"

Code for "The One Where They Reconstructed 3D Humans and Environments in TV shows" appearing in ECCV 2022.

Official PyTorch Implementation of `Neural Feature Fusion Fields: 3D Distillation of Self-Supervised 2D Image Representations`

Car parts dataset for object detection and semantic segmentation tasks, provided by DSMLR lab, IT-KMITL.

Our method takes as input a collection of images (100 in our experiments) with known cameras, and outputs the volumetric density and normals, materials (BRDFs), and far-field illumination (environm…

Semantic Segmentation using U-Net