There are 2 repositories under weights-and-biases topic.
PyTorch implementation of the U-Net for image semantic segmentation with high quality images
PyTorch Lightning + Hydra. A very user-friendly template for rapid and reproducible ML experimentation with best practices. ⚡🔥⚡
Train a state-of-the-art yolov3 object detector from scratch!
Generic template to bootstrap your PyTorch project.
🚀 An end-to-end ML applications using PyTorch, W&B, FastAPI, Docker, Streamlit and Heroku → https://e2e-ml-app-pytorch.herokuapp.com/ (may take few minutes to spin up occasionally).
A clean and structured implementation of Transformer with wandb and pytorch-lightning
This repository focuses on training semantic segmentation models to predict the presence of floodwater for disaster prevention. Models were trained using SageMaker and Colab.
Tiny ResNet inspired FPN network (<2M params) for Rotated Object Detection using 5-parameter Modulated Rotation Loss
Python framework for artificial text detection: NLP approaches to compare natural text against generated by neural networks.
:detective::robot: Monitoring a PyTorch Lightning CNN with Weights & Biases
Solution to the SETI Breakthrough Listen Competition hosted on Kaggle
A template repository to quickly get started on a new Machine Learning project.
Example codes in the medium post titled "Optuna meets Weights and Biases."
Leveraging W&B to train a racecar!
Research Project based on Convolution Neural Networks to identify foliar diseases in apple trees. FGVC8 Plant Pathology Kaggle Challenge.
This repository contains the experiments to show certain capabilities of Weights and Biases.
Pipeline built on flyte for training Iris dataset using Pytorch and Weights and Biases
Predict Customer Churn
Research Project based on Convolution Neural Networks to identify foliar diseases in apple trees. FGVC8 Plant Pathology Kaggle Challenge.
Monitor SLURM jobs using Weights and Biases (wandb) 📊
Additional Callbacks for Weights & Biases to monitor your models even better :mag_right:
Example of integration of Neu.ro with W&B for hyperparameter tuning
Course 2 project of the Udacity ML DevOps Nanodegree Program
An In-depth study of the various popular Normalization methods used in deep-learning showcased as a series of Weights & Biases Reports 📝
Exploring pytorch-lightning's different experiment logging platforms
An ML Pipeline for Short-Term Rental Prices in NYC using Mlflow as orchestration tool and Weights&Biases as artefact storage tool.
This is a project for my Neural Networks graduate course involving the prediction of cryptocurrency prices.
Project for ML DevOps Engineer Nanodegree, unit 3 (Building a Reproducible Model Workflow).
Repository of ML research code @ NMSP (Cornell)
Starter Code for the Unit 3, ML DevOps Engineer Nanodegree.
First project to implement from data 2 deployment on streamlit
Project to test out experimental platforms MLflow and Weights and Bias
Final exercise of the Udacity class on Machine Learning workflows
Course 2 project of the Udacity ML DevOps Nanodegree Program