242 skills found · Page 1 of 9
hiukim / Mind Ar JsWeb Augmented Reality. Image Tracking, Face Tracking. Tensorflow.js
sceneview / Sceneform AndroidSceneform Maintained is an ARCore Android SDK with Google Filament as 3D engine. This is the continuation of the archived Sceneform
UjjwalSaxena / Automold Road Augmentation LibraryThis library augments road images to introduce various real world scenarios that pose challenges for training neural networks of Autonomous vehicles. Automold is created to train CNNs in specific weather and road conditions.
TencentARC / ColorFlowThe official implementation of paper "ColorFlow: Retrieval-Augmented Image Sequence Colorization". ColorFlow：基于检索增强的图像序列上色
henrydaum / Second BrainSecond Brain is a desktop application that acts as a personal knowledge base, using retrieval-augmented generation (RAG), multimodal AI models, and a hybrid lexical/semantic search algorithm to interact with local text files and images.
eliahuhorwitz / DeepSIMOfficial PyTorch implementation of the paper: "DeepSIM: Image Shape Manipulation from a Single Augmented Training Sample" (ICCV 2021 Oral)
pytorch / Accimagehigh performance image loading and augmenting routines mimicking PIL.Image interface
3DTopia / Phidias Diffusion[ICLR 2025] Phidias: A Generative Model for Creating 3D Content from Text, Image, and 3D Conditions with Reference-Augmented Diffusion
riccqi / ARImageTrackingAugmented Reality image tracking with SwiftUI, RealityKit and ARKit 4.
piyushpathak03 / Recommendation SystemsRecommendation Systems This is a workshop on using Machine Learning and Deep Learning Techniques to build Recommendation Systesm Theory: ML & DL Formulation, Prediction vs. Ranking, Similiarity, Biased vs. Unbiased Paradigms: Content-based, Collaborative filtering, Knowledge-based, Hybrid and Ensembles Data: Tabular, Images, Text (Sequences) Models: (Deep) Matrix Factorisation, Auto-Encoders, Wide & Deep, Rank-Learning, Sequence Modelling Methods: Explicit vs. implicit feedback, User-Item matrix, Embeddings, Convolution, Recurrent, Domain Signals: location, time, context, social, Process: Setup, Encode & Embed, Design, Train & Select, Serve & Scale, Measure, Test & Improve Tools: python-data-stack: numpy, pandas, scikit-learn, keras, spacy, implicit, lightfm Notes & Slides Basics: Deep Learning AI Conference 2019: WhiteBoard Notes | In-Class Notebooks Notebooks Movies - Movielens 01-Acquire 02-Augment 03-Refine 04-Transform 05-Evaluation 06-Model-Baseline 07-Feature-extractor 08-Model-Matrix-Factorization 09-Model-Matrix-Factorization-with-Bias 10-Model-MF-NNMF 11-Model-Deep-Matrix-Factorization 12-Model-Neural-Collaborative-Filtering 13-Model-Implicit-Matrix-Factorization 14-Features-Image 15-Features-NLP Ecommerce - YooChoose 01-Data-Preparation 02-Models News - Hackernews Product - Groceries Python Libraries Deep Recommender Libraries Tensorrec - Built on Tensorflow Spotlight - Built on PyTorch TFranking - Built on TensorFlow (Learning to Rank) Matrix Factorisation Based Libraries Implicit - Implicit Matrix Factorisation QMF - Implicit Matrix Factorisation Lightfm - For Hybrid Recommedations Surprise - Scikit-learn type api for traditional alogrithms Similarity Search Libraries Annoy - Approximate Nearest Neighbour NMSLib - kNN methods FAISS - Similarity search and clustering Learning Resources Reference Slides Deep Learning in RecSys by Balázs Hidasi Lessons from Industry RecSys by Xavier Amatriain Architecting Recommendation Systems by James Kirk Recommendation Systems Overview by Raimon and Basilico Benchmarks MovieLens Benchmarks for Traditional Setup Microsoft Tutorial on Recommendation System at KDD 2019 Algorithms & Approaches Collaborative Filtering for Implicit Feedback Datasets Bayesian Personalised Ranking for Implicit Data Logistic Matrix Factorisation Neural Network Matrix Factorisation Neural Collaborative Filtering Variational Autoencoders for Collaborative Filtering Evaluations Evaluating Recommendation Systems
mahmoudnafifi / WB Color AugmenterWB color augmenter improves the accuracy of image classification and image semantic segmentation methods by emulating different WB effects (ICCV 2019) [Python & Matlab].
bcmi / Image Composition Assessment Dataset CADB[BMVC2021] The first image composition assessment dataset. Used in the paper "Image Composition Assessment with Saliency-augmented Multi-pattern Pooling". Useful for image composition assessment, image aesthetic assesment, etc.
HasnainRaz / SemSegPipelineA simpler way of reading and augmenting image segmentation data into TensorFlow
robomex / ARKit 2 Image Tracking DemoiOS 12 + ARKit 2 + Image tracking means: Harry Potter style moving pictures, living movie posters, video postcards, paper-thin “displays,” and lots more augmented reality fun.
YaN9-Y / D4Implementation of CVPR2022 "Self-augmented Unpaired Image Dehazing via Density and Depth Decomposition"
iamarunbrahma / Pdf To MarkdownConversion of PDF documents to structured Markdown, optimized for Retrieval Augmented Generation (RAG) and other NLP tasks. Extract text, tables, and images with preserved formatting for enhanced information retrieval and processing.
zhangbaijin / MemoryNetCode for paper:Memory Augment is All Your Need for image restoration. TCE 2025
deadskull7 / Pneumonia Diagnosis Using XRays 96 Percent RecallBEST SCORE ON KAGGLE SO FAR , EVEN BETTER THAN THE KAGGLE TEAM MEMBER WHO DID BEST SO FAR. The project is about diagnosing pneumonia from XRay images of lungs of a person using self laid convolutional neural network and tranfer learning via inceptionV3. The images were of size greater than 1000 pixels per dimension and the total dataset was tagged large and had a space of 1GB+ . My work includes self laid neural network which was repeatedly tuned for one of the best hyperparameters and used variety of utility function of keras like callbacks for learning rate and checkpointing. Could have augmented the image data for even better modelling but was short of RAM on kaggle kernel. Other metrics like precision , recall and f1 score using confusion matrix were taken off special care. The other part included a brief introduction of transfer learning via InceptionV3 and was tuned entirely rather than partially after loading the inceptionv3 weights for the maximum achieved accuracy on kaggle till date. This achieved even a higher precision than before.
Azure-Samples / Multimodal Rag Code ExecutionA multimodal Retrieval Augmented Generation with code execution capabilities. Process multiple complex documents with images, table, charts to distill insights or generate new documents.
lisadunlap / ALIAAugmenting with Language-guided Image Augmentation (ALIA)
