Caffe Implementation of Google's MobileNets (v1 and v2)

The official PyTorch implementation for paper "Hierarchical Domain-Adapted Feature Learning for Video Saliency Prediction"

Android pet detector with tensorflow models

Covid19 Face Mask Detector

Implementing and comparing ResNet50 and MobileNetV2 transfer learning models using the MNIST:HAM10000 image dataset. Resulting classification accuracy of ~90%.

The TasterAI is a neural network model for food categorization. It consists of a database of 15 types of food.

This project is a work of fiction written from the perspective of a 2020 researcher traveling back in time to mid 2013 to share some 2020 xNNbased application ideas; references to credit the actual inventors of the various ideas is provided at the end

Classification for flowers Images using Deep learning [ Convolutional Neural Network] and using Transfer learning [ Mobilnet ] .

Обнаружие маски на лице в реальном времени

This application detects a speed sign board on the roads live. It warns the driver when a 30 zone board is detected.

A real-time driver monitoring system using OpenCV and deep learning to detect drowsiness and alertness.

The Project is about detecting Face Mask using MobileNetv2 and a Caffee Model for detecting a face.

This project uses deep learning models to classify plant leaf diseases using the PlantVillage dataset, evaluating four architectures: Simple CNN, VGG16 (Transfer Learning), Deep CNN, MobileNetV2 (Transfer Learning + Fine-tuning)

This project benchmarks vision (MobileNet, ResNet20) and NLP (DistilBERT) models across server vs. client inference, using backend servers and ONNX-converted local models. It evaluates latency, UX, accuracy, and performance, dedicated to fulfilling the Master’s thesis in AI & ML.

for binary image classification: detecting real vs forged handwritten signatures.