My first Python repo with codes in Machine Learning, NLP and Deep Learning with Keras and Theano
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Updated
Dec 6, 2021 - Python
My first Python repo with codes in Machine Learning, NLP and Deep Learning with Keras and Theano
Tools to Design or Visualize Architecture of Neural Network
StyleGAN Encoder - converts real images to latent space
ImageNet pre-trained models with batch normalization for the Caffe framework
Keras implementation of a ResNet-CAM model
RetinaFace (Single-stage Dense Face Localisation in the Wild, 2019) implemented (ResNet50, MobileNetV2 trained on single GPU) in Tensorflow 2.0+. This is an unofficial implementation. With Colab.
A Multiclass Weed Species Image Dataset for Deep Learning
An easy implementation of Faster R-CNN (https://arxiv.org/pdf/1506.01497.pdf) in PyTorch.
Deep-learning seismic facies on state-of-the-art CNN architectures
A fashion Recommender system using deep learning Resnet50 and Nearest neighbour algorithm
Detecting cinema shot types using a ResNet-50
A Multi-Class Brain Tumor Classifier using Convolutional Neural Network with 99% Accuracy achieved by applying the method of Transfer Learning using Python and Pytorch Deep Learning Framework
Deployed bird classification webapp using Deep Learning, Docker, and Streamlit. Users can go onto the webapp and either upload their own images of birds or select from a set of images to feed through a Deep Learning model and display a prediction.
Bone Fracture Detection using deep learning (Resnet50) - Final project in the fourth year of the degree
Deep neural network trained to detect eye contact from facial image
Computer Vision - Impemented algorithms - Hybrid image, Corner detection, Scale space blob detection, Scene classifiers, Vanishing point detection, Finding height of an object, Image stitching.
A one stop shop for all of your activity recognition needs.
CP and Tucker decomposition for Convolutional Neural Networks
An easy implementation of FPN (https://arxiv.org/pdf/1612.03144.pdf) in PyTorch.
Implementation of Resnet-50 with and without CBAM in PyTorch v1.8. Implementation tested on Intel Image Classification dataset from https://www.kaggle.com/puneet6060/intel-image-classification.
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