nasnetmobile

Implementation of EfficientNet model. Keras and TensorFlow Keras.

Cancer Detection from Microscopic Images by Fine-tuning Pre-trained Models ("Inception") for new class labels

This repo contains my final year project on tea leaf diseases using deep learning as a partial fulfillment of my degree of master in science in information technology from the department of computer science, Gauhati university

This app contains and skin cancer android app whose model is created using transfer learning with inception_v3

MATLAB Fashion Classification with Deep Learning.

Proyek ini adalah sebuah implementasi deep learning untuk mengklasifikasikan berbagai jenis penyakit pada daun padi secara otomatis.

Brain tumor detection and classification based on MRI images using Convolutional neural networks.

Satellite Image Classification is a deep learning project that classifies satellite images into categories like "Cloudy", "Desert", "Green_Area", and "Water". By fine-tuning the NasNet Mobile architecture using Convolutional Neural Networks (CNNs) and transfer learning, the model achieves an accuracy of 95%.

Malaria Detection from Cell Images using Deep Learning - NasNetMobile Model

A Deep Learning solution that aims to help doctors in their decision making when it comes to diagnosing cancer patients.

Fine tuned NasNetmobile model for tea leaf disease detection

Dog breed recognition across 120 classes with comparative modelling and TensorFlow Lite deployment

Real-time trash classification using CNN and transfer learning (MobileNetV2 and NASNetMobile) to identify waste materials

This application allows users to identify plants by taking or uploading photos. It uses a deep learning model based on the NASNetMobile architecture, providing accurate plant recognition across different platforms (iOS, Android, and Web)

A deep-neural-network model for estimating the aesthetic quality of images

Garbage Classifier using Transfer Learning: ML-powered REST API for classifying waste. Built with FastAPI, TensorFlow, and Transfer Learning on NASNet.

The Plant Disease Classification project uses the NasNetMobile deep learning model to classify plant conditions into five categories: fungus, healthy, virus, pests , and bacteria . With a FastAPI backend, SQL Server database, and Streamlit frontend, it enables users to upload images and get quick, accurate disease predictions.