A Tensorflow model for text recognition (CNN + seq2seq with visual attention) available as a Python package and compatible with Google Cloud ML Engine.

No description available

real-time fire detection in video imagery using a convolutional neural network (deep learning) - from our ICIP 2018 paper (Dunnings / Breckon) + ICMLA 2019 paper (Samarth / Bhowmik / Breckon)

This is an implementation of Bilinear CNN for fine grained visual recognition using TensorFlow.

Vectorized implementation of convolutional neural networks (CNN) in Matlab for both visual recognition and image processing.

My assignment solutions for Stanford’s CS231n (CNNs for Visual Recognition) and Michigan’s EECS 498-007/598-005 (Deep Learning for Computer Vision), version 2020.

Projects from the Deep Learning Specialization from deeplearning.ai provided by Coursera

Using DUCK-Net for polyp image segmentation. ( Nature Scientific Reports 2023 )

This deep learning application can detect Facial Keypoints (15 unique points). They mark important areas of the face - the eyes, corners of the mouth, the nose, etc.

Implementation of Logistic Regression, MLP, CNN, RNN & LSTM from scratch in python. Training of deep learning models for image classification, object detection, and sequence processing (including transformers implementation) in TensorFlow.

This repositary contain all my exercises and projects of Udacity Computer Vision Nanodegree Program

Assignments: CNN for Visual Recognition.

PyTorch/Tensorflow solutions for Stanford's CS231n: "CNNs for Visual Recognition"

A pytorch implementation of Bilinear CNNs for Fine-grained Visual Recognition(BCNN).

Google MediaPipe Javascript Demos (including live demos)

Pre-trained VGG-Net Model for image classification using tensorflow

Genre Classification using Convolutional Neural Networks

TensorFlow Lite object detection example for Raspberry Pi Zero

"Omnidirectional CNN for Visual Place Recognition and Navigation", ICRA 2018

CNN for visual speech recognition

No description available

Python module for face recognition with OpenCV and Deep Learning.

Computer Vision Case Study in image recognition to classify an image to a binary class, based on Convolutional Neural Networks (CNN), with TensorFlow and Keras in Python, to identify from an image whether it is an image of a dog or cat. (Includes: Data, Case Study Paper, Code)

Segmenting WSIs using Deep Convolutional Neural Networks

Detect road anomalies such as cracks, potholes, and bumps using our trained YOLOv8 models with visual demo. Real-time detection via Streamlit and Flask app

For this project, we are going to detect rice leaf disease using CNN and serve the result via messenger chatbot. We will also implement this to an independent Android app.

Used Convolutional Deep Neural nets to extract features from the image

Resume Parsing app to extract information using AI

Violence recognition in streaming video using Transfer Learning and MoViNets. The project leverages state-of-the-art deep learning techniques to create an efficient and accurate violence detection system.

Multimodal Speech Recognition for phoneme level prediction using Audio-Visual data from TCDTIMIT dataset implementing RNNs with LSTMs for the audio subnetwork and CNN-LSTMs for the video subnetwork.