Trains convo neural network, converts to onnx, infer in Unity with Sentis NN
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Updated
Jul 19, 2024 - C#
Trains convo neural network, converts to onnx, infer in Unity with Sentis NN
Projects from AWS & Udacity Machine Learning Scholarship
Development for peak detection at CXLS/CXFEL. Mainly focusing on deep learning CNN networks.
edepth is an open-source, trainable CNN-based model for depth estimation from single images, videos, and live camera feeds.
This Repository hosts all my CNN related projects.
Official repo for the following paper: Traffic Forecasting on New Roads Unseen in the Training Data Using Spatial Contrastive Pre-Training (SCPT) (ECML PKDD DAMI '23)
A suite of Python scripts allowing the end-user to use Deep Learning to detect objects in georeferenced raster images.
The notebook contents implementation of image classification using different CNN models: self-written & pretrained, using data Intel Image Classification.
Neuroimaging Informatics Technology Initiative (NIfTI) RM (T1w & T2-FLAIR) segmentation of epilectic seizures using YOLOv8
Diagnosing ‘silent’ heart attack using ECG waveforms (A Nightingale Open Science dataset)
Transforming 2D images into 3D semantically segmented scenes using innovative CNN architecture and COLMAP reconstruction.
Official repository of my book: "Deep Learning with PyTorch Step-by-Step: A Beginner's Guide"
HAM10000 Skin Lesion Classification
A comparative analysis of deep learning algorithms for multi label image classification using microscopic images.
This is a warehouse for DLinear-Pytorch-model, can be used to train your text dataset for time series forecasting tasks.
The third phase of the 'Deep Learning' course I took on Udemy.
Custom deep learning architectures for Sentiment Analysis and Image Classfication
Profile Face Recognition project
This project utilizes various computer vision techniques to track two tennis players, a court's key-points, and a tennis ball. It also measures the players' ball shot speed, movement speed and number of shots that they have taken.
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