Dog breed classification web app built with Streamlit and TensorFlow. Upload a dog image and get predicted breed using a trained CNN model.

A basic web-app for image classification using Streamlit and Tensorflow

Vegetable Classification & Detection, a web-based tool, leverages Streamlit, TensorFlow, and OpenCV. It employs CNN and YOLO models to classify and detect vegetables from images and live feeds, benefiting agriculture and food processing with accurate identification & detection tasks.

A basic web-app for image classification using Streamlit and TensorFlow.

This project utilizes MobileNet and the TrashNet dataset to classify waste into different categories and provide recycling suggestions. Built with TensorFlow and Streamlit, it allows users to upload an image of trash and get instant classification results along with eco-friendly disposal recommendations.

Cat and Dog image classification using TensorFlow with a Streamlit-based web demo.

Image classification using deep learning techniques with TensorFlow, PyTorch, and ResNet50. Includes an interactive Streamlit app for real-time image-based predictions.

Deep learning system for lung cancer classification from histopathology images using CNN, VGG16 & ResNet50 with Grad-CAM explainability · TensorFlow · Streamlit

Welcome to the Food Image Classification project! This repository showcases an end-to-end system for food image classification using TensorFlow, Streamlit, Docker, Kubernetes, cloudbuild, GitHub, and Google Cloud, driven by MLOps principles.

The MNIST Handwritten Digits Classification project uses Python, TensorFlow, Keras, and Streamlit to classify 28x28 images of handwritten digits. A trained CNN model predicts digits, and a Streamlit-based frontend allows users to upload images for real-time predictions. This project highlights deep learning's application in image classification

A powerful, beginner-friendly Streamlit-based image classification web application using TensorFlow's MobileNetV2 pre-trained deep learning model for real-time object recognition.

AICTE Project - implementation of ml for image classification and hosting it on streamlit web config using CNN model and using Sklearn and tensorflow for framework, backend being streamlit api and python

Image-based Plant Disease Detection web application using CNN Deep Learning model. Detects diseases from plant leaf images with 38 disease classifications. Built with Python, TensorFlow, Keras and Streamlit.

As part of my journey learning AI engineering, I built an AI image classification web application using Python, Streamlit, and TensorFlow with the MobileNetV2 model.

A fast and lightweight AI-powered image classifier built with MobileNetV2, TensorFlow, and Streamlit. Easily identify panda images through a sleek web interface. Perfect for quick demos, ML learning, and image classification projects.

AI Image Classifier 🖼: A Streamlit web app that classifies images using TensorFlow’s MobileNetV2. Upload an image (JPG, JPEG, PNG) and get the top 3 predictions with probabilities. Built with Python 3.10, TensorFlow 2.20, OpenCV, and Pillow for fast and easy image classification.

For the Bytewise ML Fellowship Program, I built an image classification system using TensorFlow and Streamlit. It classifies vegetables and fruits into predefined categories using a CNN model. The project included data preprocessing, model training, and deploying a web app for real-time image classification.

This is an image classification project using TensorFlow and OpenCV. The model is trained to classify images into categories based on their visual features. The application is deployed using Streamlit, making it accessible via a web interface.

AI Pet Classifier: Instantly identify cats vs. dogs using deep learning. Upload any image for accurate classification with confidence scores. Built with Streamlit and TensorFlow, featuring a modern UI and detailed prediction insights.

The Vegetable Image Classification Project is an application of deep learning and web development that leverages the TensorFlow framework and Streamlit library. Users can submit images of different vegetables, which are instantly provided with reliable predictions on the vegetable name.

"Image classification project using a trained CNN model on the CIFAR-10 dataset. The application predicts the class of uploaded images (e.g., dog, cat, airplane) with confidence scores. Built with TensorFlow, Keras, and Streamlit for an interactive user experience."

Upload images to this Streamlit app powered by TensorFlow. Predict the CIFAR-10 dataset class probabilities using a pre-trained model. The user-friendly interface allows quick interaction with deep learning-based image classification. Resizes images, displays predictions with a bar chart. Ideal for learning and experimenting

Image classification project using TensorFlow/Keras with the CIFAR dataset and MobileNet architecture. Images are preprocessed, trained with Adam, categorical crossentropy, dropout, and early stopping for better accuracy. The model is saved in .keras format and deployed with Streamlit for real-time predictions.

🧠 Deep learning project for brain tumor classification using MRI images. Built with transfer learning (VGG16 + fine-tuning), TensorFlow/Keras, and deployed via Streamlit. Dataset & model loaded dynamically from KaggleHub. Includes training notebook, evaluation, and interactive web app.

This project is a Plant Classification System built using Convolutional Neural Networks (CNN) to identify plant species from leaf images. Developed as a final year project, it features a TensorFlow-based model and a Streamlit web app for real-time predictions. Sample images are included for demonstration.

A deep learning project for fruit classification using the Fruits-360 dataset. Built with TensorFlow/Keras, the CNN model classifies images of fruits into multiple categories. Includes preprocessing with ImageDataGenerator, training model, and deployment via Streamlit for interactive predictions.

Repository associated with the interactive WebApp (https://share.streamlit.io/paulinomoskwa/dl-code-generator/main.py) with the goal to autonomously generate the python code needed to build and train a neural network for image classification in Tensorflow 👩‍💻

An AI-powered poultry disease detection system that uses deep learning to classify chicken feces images. Built with TensorFlow and Streamlit, it provides quick, accessible diagnosis for farmers. Trained on PCR-validated data for accurate classification of diseases like Salmonella and Coccidiosis.

SpudScan is a machine learning-powered web application designed to detect diseases in potato leaves using image classification. Built using TensorFlow and deployed with Streamlit, SpudScan helps farmers and agricultural experts quickly identify diseases such as Early Blight and Late Blight by simply uploading an image of a potato leaf.

🗑️ Garbage Classification Using CNN This project is a deep learning-based web app that classifies garbage images into six categories using a Convolutional Neural Network (CNN). Built with TensorFlow and Streamlit, the model predicts the type of waste from an uploaded image, helping to promote efficient waste sorting and recycling.