Implementation of popular deep learning model using keras.

Classification of Dog breeds through images using CNN.

Dog_Breed_Identification_Using_Pretrained_Pytorch_Model_Resnet50

Object detection with Resnet50 implemented using CoreML

Anime Image Captioning with ResNet-50 in PyTorch

I created this project as my Bachelor's degree graduation project with the aim of providing assistance to students and teachers amidst the COVID-19 crisis.

A ResNet50 based model to tackle the multi-class classification problem of detecting leaf wilting levels from plant images.

Fine-tuning pre-trained ResNet50 model to classify the brain scan different types of tumor

Indian food classifier modelled after finetuning ResNet50 with PyTorch

An image classification application using resnet model

WIT Bot is an innovative AI bot that can classify images uploaded to it, other than human faces. As its name suggests, it stands for What is this Bot, and is designed to identify and label images with high accuracy. Created using the advanced concepts of Python, this bot utilizes a powerful neural model called ResNet50 from the Tensorflow library.

A Bangkit 2023 capstone project with the theme of sustainable living by utilizing FastAPI as the backend and Android (Kotlin) as the frontend.

Human Data Analytics Course Project

In the project, within the scope of the Computer Vision course with the code 02010012 given by the Faculty of Computer Engineering, crack and no crack classification was made with the VGG-16 and ResNet-50 architecture.

A collection of datasets and neural networks for microorganism image classification

We have proposed a multimodal approach. Where we first took the best unimodal for textual and visual data classification by testing and automation process. Then we fusion of the two models which can successfully classify the materials that have been damaged using the image and text data. EfficientNetB3+BERT multimodal better accuracy with 94.18%

Image Captioning is the task of describing the content of an image in words. This task lies at the intersection of computer vision and natural language processing.

Accurate monitoring of fetal heart rate (FHR) is essential for assessing fetal well-being during pregnancy and labour. However, there is an important challenge: FHR sound recordings include noise. Current solutions tend to discount memory simplicity and real-time processing in favour of traditional signal processing techniques. Our study addresses

Creating a Model that can diagnose pneumonia with an xray image