I BIBI's starred repositories
Automated-Detection-of-Defects-on-Metal-Surfaces-using-Deep-Learning-Techniques-and-Vision-Transform
The project focuses on utilizing deep learning and transformers to detect defects in metals. Tensorflow has been employed to design the model architecture. Among the various approaches explored, the Inception model, a custom CNN model, and the VIT feature extractor have yielded the most promising results.
pytorch-segmentation
:art: Semantic segmentation models, datasets and losses implemented in PyTorch.
learn-with-other-kaggle-users
Python code for a Kaggle beginner-friendly competition (top 4% finish)
Multi-Input-ConvLSTM
TensorFlow implementation of a Multi-Input ConvLSTM for predicting flood extent.
climate-data-science
Climate Data Science and Earth Observation with Python.
Deep-math-machine-learning.ai
A blog which talks about machine learning, deep learning algorithms and the Math. and Machine learning algorithms written from scratch.
Hands-On-Reinforcement-Learning-With-Python
Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow
awesome-quantum-machine-learning
Here you can get all the Quantum Machine learning Basics, Algorithms ,Study Materials ,Projects and the descriptions of the projects around the web
Plant-Disease-Prediction-Using-MobileNet
Plant disease classification using deep learning. Detect plant issues from images using CNNs. Jupyter notebook, model, and dataset included.
PDF-Extract-Kit
A Comprehensive Toolkit for High-Quality PDF Content Extraction
crack_detection_CNN_masonry
This GitHub Repository was produced to share material relevant to the Journal paper "Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning" by D. Dais, İ. E. Bal, E. Smyrou, and V. Sarhosis published in "Automation in Construction".
Transformer-MaskRCNN
Enhanced Mask R-CNN model for apple disease instance segmentation by integrating Transformer's multihead-attention mechanism for improved object detection accuracy.
CNN-Vs-SVM-
An analysis and comparison of Convolution Neural Network and Support Vector Machine Model for identifying colored images.
Algo_Ds_Notes
It is a repository that is a collection of algorithms and data structures with implementation in various languages.
Road-Cracks-Detection-Neural-Network-
A convolutional neural network built using keras to detect cracks on road with 97.5% accuracy.
crack_segmentation
This repository contains code and dataset for the task crack segmentation using two architectures UNet_VGG16, UNet_Resnet and DenseNet-Tiramusu
CSBSR
Official PyTorch implementation of the paper "Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded Images", IEEE Transactions on Instrumentation and Measurement (TIM) 2024. CSBSR is an advanced version of our previous work CSSR [MVA'21].
RGBD_Processing
Repository for extracting RGB images, depth images, metadata, and processing of RGBD images for depth filtering, error correction, and 3D reconstruction.
ssl-wearables
Self-supervised learning for wearables using the UK-Biobank (>700,000 person-days)
stereo_pothole_datasets
Pothole Detection Based on Disparity Transformation and Road Surface Modeling (T-IP)
FabricDefect
A weakly annotated fabric defect dataset. Contains 24 512x512 images. Every image has a 512x512 mask. The mask has one rectangular bounding box covering the entire defect but has pixels of fabric without defect. This makes the defect detection task challenging.
visioncolab
Google colab's for image processing, pattern recognition and computer vision