This project uses PyTorch to classify bone fractures. As well as fine-tuning some famous CNN architectures (like VGG 19, MobileNetV3, RegNet,...), we designed our own architecture. Additionally, we used Transformer architectures (such as Vision Transformer and Swin Transformer). This dataset is Bone Fracture Multi-Region X-ray, available on Kaggle.

Bushfires detector using mobilenet as feature extractor

Deep convolutional neural network implementation for brain tumor segmentation.

Biometric face tracking attendance system for tracking and verifying employee’s or student’s attendance using their unique facial characteristics

By comparing the performance of 24 custom trained models with various configurations, I aim to identify the most effective model architecture and configuration for accurate gel electrophoresis image classification.

Introduction Course to Deep Learning using Google Colab.

대충 TFJS 쓰던 사람이 Python TF 배워보겠다고 만든 이미지 인식 프로젝트

GUI testing automation and testing tools automation attempt

PyTorch Implementation of MobileNetV3 large and small

Beans leaf dataset with images of diseased and health leaves.

Implementation of MobileNetV3 in pytorch

Emotion prediction and Spotify music recommendation using MobileNetV3-Large and Streamlit.

Raspberry Pi Object Detection using MobileNetV3

Processing image advertisements to predict the context conveyed through them using CNNs. The images are further visualised using GradCAM to understand how the first and the last layers perceive the image dataset for the classification.

Vehicle Identify

Simple app used for detecting objects in images and videos.

How AI enhances parking management. In a team of four, we leveraged deep CNNs to create a system that detects (YOLO) and classifies (ensemble of SqueezeNet and ResNet) parking spots as free or occupied from images obtained by common surveillance cameras. It is able to employ pre-installed cameras, optimizing space utilization and reducing costs.

PyTorch implementation of MobileNet_v3

Habiba Omran Thesis