PhysicxEntrepreneur's Stars
CompVis/stable-diffusion
A latent text-to-image diffusion model
facebookresearch/segment-anything
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.
TA-Lib/ta-lib-python
Python wrapper for TA-Lib (http://ta-lib.org/).
satellite-image-deep-learning/techniques
Techniques for deep learning with satellite & aerial imagery
unit8co/darts
A python library for user-friendly forecasting and anomaly detection on time series.
OSGeo/gdal
GDAL is an open source MIT licensed translator library for raster and vector geospatial data formats.
charlesq34/pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
googlemaps/google-maps-services-python
Python client library for Google Maps API Web Services
opengeos/segment-geospatial
A Python package for segmenting geospatial data with the Segment Anything Model (SAM)
fxia22/pointnet.pytorch
pytorch implementation for "PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation" https://arxiv.org/abs/1612.00593
isl-org/DPT
Dense Prediction Transformers
tensorflow/quantum
An open-source Python framework for hybrid quantum-classical machine learning.
kuleshov/cornell-cs5785-2020-applied-ml
Teaching materials for the applied machine learning course at Cornell Tech (online edition)
FEniCS/dolfinx
Next generation FEniCS problem solving environment
kennethleungty/Neural-Network-Architecture-Diagrams
Diagrams for visualizing neural network architecture (Created with diagrams.net)
qiskit-community/qiskit-community-tutorials
A collection of Jupyter notebooks developed by the community showing how to use Qiskit
kuleshov/cornell-cs5785-2024-applied-ml
Lecture materials for Cornell CS5785 Applied Machine Learning (Fall 2024)
yh08037/quantum-neural-network
Qiskit Hackathon Korea 2021 Community Choice Award Winner : Exploring Hybrid quantum-classical Neural Networks with PyTorch and Qiskit
venkanna37/Label-Pixels
Label-Pixels is the tool for semantic segmentation of remote sensing images using Fully Convolutional Networks. Initially, it is designed for extracting the road network from remote sensing imagery and now, it can be used to extract different features from remote sensing imagery.
qiyaoliang/Quantum-Deep-Learning
Recent advances in many fields have accelerated the demand for classification, regression, and detection problems from few 2D images/projections. Often, the heart of these modern techniques utilize neural networks, which can be implemented with deep learning algorithms. In our neural network architecture, we embed a dynamically programmable quantum circuit, acting as a hidden layer, to learn the correct parameters to correctly classify handwritten digits from the MNIST database. By starting small and making incremental improvements, we successfully reach a stunning ~95% accuracy on identifying previously unseen digits from 0 to 7 using this architecture!
rcdaudt/patch_based_change_detection
DRA-chaos/Quantum-Classical-Hyrid-Neural-Network-for-binary-image-classification-using-PyTorch-Qiskit-pipeline
Quantum Hybrid Neural Network model for image classification
khw11044/kalmanFilter
oshholail/EGY-BCD
EGYPT-Dataset