1.rohitg00 / ai-engineering-from-scratch
Learn AI Engineering! Learn it. Build it. Ship it for others.
Ranked by Attention Score: stars gained over the last 30 complete days
48 repositories by Attention Score · Oct 4, 2026 – Nov 2, 2026 (UTC)
Learn AI Engineering! Learn it. Build it. Ship it for others.
An agent skill that finds where a photo was taken — OpenStreetMap geometry, elevation skylines, satellite imagery and street view — and shows its work. Works with Claude Code, Codex, Cursor, Gemini CLI, OpenCode and GitHub Copilot.
Official PyTorch implementation of VisionHOPE: Visual Backbones as Self-Modifying Learning Systems.
《动手学深度学习》:面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。
Ultralytics YOLO27, YOLO26, YOLO11, YOLOv8 — object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking
We write your reusable computer vision tools. 💜
🐍 Geometric Computer Vision Library for Spatial AI
Original reference implementation of "3D Gaussian Splatting for Real-Time Radiance Field Rendering"
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.
CVAT is a leading data annotation platform for image, video, audio, and 3D datasets. It offers open-source, cloud, and enterprise products, as well as labeling services, with AI-assisted labeling, quality assurance, team collaboration, analytics, and developer APIs.
A collaboration friendly studio for NeRFs
🤗 The largest hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation tools
Node-based Visual Programming Toolbox
An open source implementation of CLIP.
Image annotation with Python. Supports polygon, rectangle, circle, line, point, and AI-assisted annotation.
Datasets, Transforms and Models specific to Computer Vision
Fast and Accurate ML in 3 Lines of Code
Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.
A MNIST-like fashion product database. Benchmark :point_down:
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
Image processing in Python
Image-to-Image Translation in PyTorch
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Low-code framework for building custom LLMs, neural networks, and other AI models
PyTorch implementation of YOLOv3, YOLOv3-SPP, and YOLOv3-tiny for real-time object detection with training, validation, inference, and multi-format export.
A python library for self-supervised learning and latent world models on images.
SenseTime Research platform for single object tracking, implementing algorithms like SiamRPN and SiamMask.
Visual tracking library based on PyTorch.
Face recognition using Tensorflow
OpenMMLab Foundational Library for Training Deep Learning Models
Learn Robotics with JdeRobot
Upscale an image by a factor of 4, while generating photo-realistic details.
Game Agent Framework. Helping you create AIs / Bots that learn to play any game you own!
(IROS 2020, ECCVW 2020) Official Python Implementation for "3D Multi-Object Tracking: A Baseline and New Evaluation Metrics"
🔥RandLA-Net in Tensorflow (CVPR 2020, Oral & IEEE TPAMI 2021)
Automatic colorization using deep neural networks. "Colorful Image Colorization." In ECCV, 2016.
:unlock: Lip Reading - Cross Audio-Visual Recognition using 3D Architectures
AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.
An open source library and framework for deep learning on satellite and aerial imagery.
Interactive Image Generation via Generative Adversarial Networks
Accelerated deep learning R&D
Build fully-functioning computer vision models with PyTorch
LEDNet: A Lightweight Encoder-Decoder Network for Real-time Semantic Segmentation
A 3D vision library from 2D keypoints: monocular and stereo 3D detection for humans, social distancing, and body orientation.
[IROS 2020] se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains
Automated Testing for Set-Top Boxes and Smart TVs
The implementation code for "DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction"