Image Aesthetic Assessment in PyTorch with implemented popular datasets and models (possibly providing the pretrained ones).
We will use keras fashion MNIST dataset. This consist of 60000 28X28 pixel images and 10000 test images, these images are classified in one of the 10 categories
This repository contains the implementation of the Liver Tumor Segmentation and Detection model using the ResUNET architecture. The goal of this project is to develop a deep learning model that can accurately segment liver tumors from medical images, aiding in diagnosis and treatment planning.
This is a Potato Disease Classification Mobile Application Using Flutter Framework and Convolutional Neural Network Artificial Intelligent Model. this is a simple and tech base solution in agriculture sector. this application is free to download Hope you like it. link below. 👇👇👇
Digit Recognizer using MNIST dataset with Deep Learning Model : CNN.
Kinship Detection Network Project - Multimedia Signal Processing (BE), supervised by Prof. L. Verdoliva (2023).
This is a copy version from the original source of OctSurf: Efficient Hierarchical Voxel-based Molecular Surface Representation for the Protein-Ligand Affinity Prediction..
FaceSense is a machine learning project where when a face is detected through a image or webcam then it can predict its age and gender. Maybe perfect prediction of age can't be made but it can predict close to the actual age.
a hands-on approach to real-life challenges and covers exactly what you need to succeed in the real world of Data Science
Face identification/recognition model using convolutional neural networks
The Data Engineering and Visualisation project goal is to develop an MVP of a CNN model with a GUI that predicts heart disease from ECG images, measures the model's performance, and displays the predictions in the GUI.
Applying CNN (Convolutional Neural Network) for crop disease classification, aiding precision agriculture and yield protection.
Machine Learning project for emotion recognition
CNN that classifies fresh and rotten fruit. Transfer learning using the VGG16 model and data provided by Kaggle.
Basics to Advanced level Deep Learning using Keras and TensorFlow.
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