nasnetmobile
There are 12 repositories under nasnetmobile topic.
qubvel/efficientnet
Implementation of EfficientNet model. Keras and TensorFlow Keras.
anjanatiha/Cancer-Detection-from-Microscopic-Tissue-Images-with-Deep-Learning
Cancer Detection from Microscopic Images by Fine-tuning Pre-trained Models ("Inception") for new class labels
jenishborah/Tea-Leaf-Disease-Detection
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
syed-hamza/Skin-Cancer-Android-App
This app contains and skin cancer android app whose model is created using transfer learning with inception_v3
taliegemen/Fashion-Classification
MATLAB Fashion Classification with Deep Learning.
karunagujar13/MRI-based-brain-tumor-classification
Brain tumor detection and classification based on MRI images using Convolutional neural networks.
anjanatiha/Malaria-Parasite-Detection-in-Thin-Blood-Smear-Images-with-Convolutional-Neural-Networks
Malaria Detection from Cell Images using Deep Learning - NasNetMobile Model
taherromdhane/histopathologic-cancer-detection
A Deep Learning solution that aims to help doctors in their decision making when it comes to diagnosing cancer patients.
grknc/Satellite-Image-Classification-CNN-Fine-Tuning-NasNet-Mobile
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%.
jenishborah/TLP_leafDiseaseDetection
Fine tuned NasNetmobile model for tea leaf disease detection
omega-rg/Qpixl-Image-Quality-Inspector
A deep-neural-network model for estimating the aesthetic quality of images
AnshKGoyal/Plant-Disease-Classification
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.