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.

Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media

Public facing notes page

T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis

Efficient Image Captioning code in Torch, runs on GPU

Lectures on scientific computing with python, as IPython notebooks.

An open-source project dedicated to tracking and segmenting any objects in videos, either automatically or interactively. The primary algorithms utilized include the Segment Anything Model (SAM) fo…

Machine Learning Containers for NVIDIA Jetson and JetPack-L4T

DeepFashion2 Dataset https://arxiv.org/pdf/1901.07973.pdf

Grounded SAM 2: Ground and Track Anything in Videos with Grounding DINO, Florence-2 and SAM 2

Lecture notes and code for Machine Learning practical course on CMC MSU

Unsupervised Semantic Segmentation by Distilling Feature Correspondences

AI Image SIgnal Processing and Computational Photography - Bokeh Rendering , Reversed ISP Challenge, Model-Based Image Signal Processors via Learnable Dictionaries. Official repo for NTIRE and AIM …

Improving transcription performance of OpenAI Whisper for CPU based deployment

Run Segment Anything Model 2 on a live video stream

🔥 Real-time Super Resolution enhancement (4x) with content loss and relativistic adversarial optimization 🔥

LibRosa port to Swift for ability using same prepossessing logic in iOS/MacOS platforms

Person Re-Identification (ReID) using lightweight Deep Learning model.

Implementation of the paper "ELSR: Extreme Low-Power Super Resolution Network For Mobile Devices" using PyTorch.