Image Classification, Object Detection, Image Segmentation, Instance Segmentation and Pose Estimation

This project uses an ensemble of CNN, RNN, and VGG16 models to enhance CIFAR-10 image classification accuracy and robustness. By combining multiple architectures, we significantly outperform single-model approaches, achieving superior classification performance.

Early detection of blight is crucial for potato crop health. This project utilizes deep learning to classify potato leaf images into healthy, early blight, or late blight categories . This empowers farmers to take swift action and maximize yield.

Dive into the world of Signal and Image Processing with this repository. Explore a collection of Python programs covering Discrete Fourier Transform, Elementary Signals, Sampling, Point Processing Techniques, Histogram Processing, Frequency Domain Filtering, Edge Detection, Erosion and Dilation, and Morphological Operations.

An Image Classification project w/ MobileNetV2 and DenseNet-121. Leveraging techniques like Hyperparameter Tuning, Transfer Learning, Imagine Preprocessing Techniques and Ensemble Methods.

Aplikasi deteksi penyakit kulit menggunakan AI. Dibuat untuk menyelesaikan Tugas Akhir AI Mastery Program Orbit Future Academy X SIB Kampus Merdeka 2022.

This project is a simple image classification implementation using TensorFlow. It demonstrates how to train a neural network model to classify images of cats and dogs and make predictions on new images. This project is suitable for beginners looking to learn abo

WasteEasy is an app designed to streamline waste management. Developed during the Envision Hackathon, it focuses on waste classification and encourages proper waste segregation. Users can earn points or coupons by utilizing WasteEasy for their waste disposal needs.

Knowledge distillation pytorch lightning template for image classification task

CPSC 5305 01 22FQ Introduction to Data Science Project

This project focuses on building a powerful image classification model to distinguish between cats and dogs using a Convolutional Neural Network (CNN)

GECCO is a lightweight image classifier based on single MLP and graph convolutional layers. We find that our model can achieve up to 16x better latency than other state-of-the-art models. The paper for our model can be found at https://arxiv.org/abs/2402.00564

Repository for a deep learning model that classifies images as either cats or dogs using deep learning techniques. The model is trained on a diverse dataset and achieves high accuracy in distinguishing between these two popular pet categories. Includes pre-processing scripts, model architecture, and evaluation metrics for seamless implementation

This is all of my study about machine learning

Leveraging the power of transfer learning using the VGG19 network within TensorFlow to tackle the specific problem of image classification.

MNIST Digit Recognition repository offers a robust solution for recognizing handwritten digits using the MNIST dataset.

A white blood cell dataset contains four types of cells: Lymphocytes, Monocytes, Eosinophils and Basophils.

Using Image Classification ML Models to Classify Diabetic Retinopathy Images