My first Python repo with codes in Machine Learning, NLP and Deep Learning with Keras and Theano
-
Updated
Dec 6, 2021 - Python
My first Python repo with codes in Machine Learning, NLP and Deep Learning with Keras and Theano
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.
An easy implementation of Faster R-CNN (https://arxiv.org/pdf/1506.01497.pdf) in PyTorch.
Unofficial implementation with pytorch DistributedDataParallel for "MoCo: Momentum Contrast for Unsupervised Visual Representation Learning"
An easy implementation of FPN (https://arxiv.org/pdf/1612.03144.pdf) in PyTorch.
code for ICCV19 paper "Deep Meta Metric Learning"
A one stop shop for all of your activity recognition needs.
Segmentation for vertebra in MR images
A fashion Recommender system using deep learning Resnet50 and Nearest neighbour algorithm
Deep neural network trained to detect eye contact from facial image
Official implementation for CVPR2023 Paper "Re-IQA : Unsupervised Learning for Image Quality Assessment in the Wild"
Learning Fine-grained Image Similarity with Deep Ranking is a novel application of neural networks, where the authors use a new multi scale architecture combined with a triplet loss to create a neural network that is able to perform image search. This repository is a simplified implementation of the same
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.
Bone Fracture Detection using deep learning (Resnet50) - Final project in the fourth year of the degree
CP and Tucker decomposition for Convolutional Neural Networks
Implementation of a Neural Network that can detect whether a video is in-game or not
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.
Add a description, image, and links to the resnet-50 topic page so that developers can more easily learn about it.
To associate your repository with the resnet-50 topic, visit your repo's landing page and select "manage topics."