scumechanics's repositories
Landslide-Susceptibility-Prediction-Using-Machine-Learning-Algorithms
Landslide Susceptibility Prediction and Identification of Most Significant Factors that best describe an area is susceptible to Landslides or not based on Explainable Artificial Intelligence to solve the challenging BlackBox problem of state art Artificial Intelligence based methods.
ML_Projrct_Concrete_Strength_Prediction
Perform These algorithms: - Linear Regression - Lasso Regression - Ridge Regression - Decision Tree Regressor - Random Forest Regressor - KNN Regressor - SVM Regressor AND Pick each of the algorithm and perform These steps: o Split your data between train and test steps. Build your model List down the evaluation metrics you would use to evaluate the performance of the model? Evaluate the model on training data o Predict the response variables for the test data How are the two scores? Are they significantly different? Are they the same? Is the test score better than training score?
sed-yamnet-raspberrypi
Sound Event Detection with YAMNet+tw on Raspberry Pi
TFLite-ModelMaker-EfficientDet-Colab-Hands-On
TensorFlow Lite Model Makerで物体検出を行うハンズオン用資料です(Hands-on for object detection with TensorFlow Lite Model Maker)
burned-area-detection
Detection of burned areas using deep learning from satellite images
Change-Detection-Review
A review of change detection methods, including codes and open data sets for deep learning. From paper: change detection based on artificial intelligence: state-of-the-art and challenges.
Crack_Detection
VGG + Autoencoders + YOLO_v4 + Detectronn2 (Instance Segmentation)
CVPR2021-Papers-with-Code
CVPR 2021 论文和开源项目合集
DeepLearning-500-questions
深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06
Defect-Detection-of-PCB
A repository for creating a Deep Learning model for Defect Detection in PCBs and to design a Web Application for it.
Detectron2
In this repository, I have implemented several state-of-the-art computer vision tasks using Detectron 2.
Doing_bayesian_data_analysis
Python/PyMC3 versions of the programs described in Doing bayesian data analysis by John K. Kruschke
EmotionCube
🐾 EmotionCube: An intelligent companion robot is designed based on expression recognition and intelligent speech.
Food201
On device image segmentation of meals in Android application. Trained on the Food201 dataset.
Machine-vision-based-defect-detection-in-welding-process
Implementation of automatic computer-aided identification system to recognize different types of welding defects in radiographic images which includes defect detection and classification using Deep Neural Network
nested-transformer
Aggregating Nested Transformer https://arxiv.org/pdf/2105.12723.pdf
PAB
This project aims to promote Organic farming by reducing usage of weedicide and pesticide which results in soil degradation. · With help of proper Image segmentation and Machine Learning technique i.e. CNN, we are able to distinguish Ladyfinger plant with weeds on field. · We implemented YOLO algorithm to make real time Object detection possible. · We have 4 wheel drive for PAB which enables it to move in field easily we used K mean clustering algorithm for lane detection and autonomous drive.
Pre-trained-Deep-Learning-Models-For-Rapid-Analysis-Of-Piezoelectric-Hysteresis-Loops-SHO-Fitting
Temporary repository for research paper about Accelerated Fitting of BEPS
protein-ebm
Energy-based models for atomic-resolution protein conformations
simclr
SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners
torchsde
Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
tsoutlier
time series outlier detection
VBDLDSCC
Vision Based Document Layout Detection, Segmentation and context classification using MaskRCNN on Tensorflow-Keras, PyTorch & Detectron2.