Face mask detection system using Deep learning.
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Updated
Jun 3, 2021 - Jupyter Notebook
Face mask detection system using Deep learning.
Weighted Skip Connections are Not Harmful for Deep Nets
Public repo for NImbleEdge
End-to-end deep learning pipeline that converts handwritten sheet music images into playable MIDI files using YOLOv8 object detection, Graph Attention Networks, and music theory rules.
densecap socket webcam server bridge script and client script implemented upon python and opencv
Un projet d'ids basé sur IA et apprentissage système(simplifié)
Deep learning-based motor imagery classification for brain-computer interfaces. Combines CNN spatial feature extraction with LSTM temporal modeling to decode imagined movements from EEG signals. Includes preprocessing pipeline, evaluation metrics, and visualization tools for BCI research.
Cat Dog Classification
Este proyecto se enfoca en realizar un análisis de la opinión pública en redes sociales respecto a las elecciones presidenciales en México 2024, a través de procesamiento de lenguaje natural, análisis de sentimientos y modelado temático
Built a CNN from scratch using only NumPy
Deep learning image classifier distinguishing cats and dogs using Transfer Learning with pre-trained MobileNet architecture on ImageNet, built with TensorFlow, Keras, and OpenCV.
This project focuses on the segmentation of low-grade gliomas in medical imaging and provides a framework for accurately identifying tumor regions in MRI scans. The notebook includes all the details needed to facilitate reproducible research and real-world application in medical diagnosis.
The official implementation for "Frequency-Adaptive Depth-Haze Consensus with Semantic Priors for Single Image Dehazing"
Hands-on ML & Deep Learning programs using scikit-learn, TensorFlow/Keras, and OpenCV — implemented as Jupyter Notebooks.
[ICRA 2022] The official repository for "LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition", In 2022 International Conference on Robotics and Automation (ICRA), pp. 2215-2221.
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