This app uses trained neural network to classify images of cats and dogs

Build a image classification python web app with Streamlit and PyTorch :cat: :dog:

With recent advances in both Artificial Intelligence (AI) and Internet of Things (IoT) capabilities, it is more possible than ever to implement surveillance systems that can automatically identify people who might represent a potential security threat to the public in real-time. Imagine a surveillance camera system that can detect various on-body weapons, suspicious objects, and traffic. This system could transform surveillance cameras from passive sentries into active observers, which would help prevent a possible mass shooting in a school, stadium, or mall. In this project, we tried to realize such systems by implementing Smart-Monitor, an AI-powered threat detector for intelligent surveillance cameras. The developed system can be deployed locally on the surveillance cameras at the network edge. Deploying AI-enabled surveillance applications at the edge enables the initial analysis of the captured images on-site, reducing the communication overheads and enabling swift security actions. We developed a mobile app that users can detect suspicious objects in an image and video captured by several cameras at the network edge. Also, the model can generate a high-quality segmentation mask for each object instance in the photo, along with the confidence percentage. The camera side used a Raspberry Pi 4 device, Neural Compute Stick 2 (NCS 2), Logitech C920 webcam, motion sensors, buzzers, pushbuttons, LED lights, Python Face recognition, and TensorFlow Custom Object Detection. When the system detects a motion in the surrounding environment, the motion sensors send a signal to the Raspberry Pi device notifying it to start capturing images for such physical activity. Using Python’s face recognition and TensorFlow 2 custom object detection Smart-Monitor can recognize eight classes, including a baseball bat, bird, cat, dog, gun, hammer, knife, and human faces. Finally, we evaluated our system using various performance metrics such as classification time and accuracy, scalability, etc.

Cat and Dog image classification web app with streamlit

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It is a computer vision project for cat and dog classification using flask app.

This project is a Streamlit web app that classifies images as either a cat or a dog using a CNN model. The model, trained on a dataset of cat and dog images, expects inputs resized to 64x64 pixels. The app provides an intuitive interface for image uploads and displays the classification results.

Created and analyzed a machine learning project for determining if an image is a picture of a dog or of a cat. 1,170 images from the Kaggle data set were used for feature selection performed by a pre-trained Convolutional Neural Network to create a Cosine K-Nearest Neighbor classifier and a Cubic Support Vector Machine classifier. 25,000 images were then used for a second feature selection to create another Cubic Support Vector Machine since it was the better performing classifier. The entire machine learning workflow was performed including partitioning the data, feature selection, classifier selection, training, cross-validation and testing. The MATLAB Classification Learner App was used to help create the classifiers. This project was performed by two partners and myself for my Introduction to Artificial Intelligence elective course (CAP4630).

Cats and Dogs Classification PyTorch Deep Learning

I have built a mobile app that will classify an image as a dog or a cat using Machine Learning and Android Studio.

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Cat and Dog pictures classification using flutter app

This is a cat and dog classification mobile app.

Simple cat and dog image display/classification web app

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Cat vs. Dog image classification web app using PyTorch, FastAPI, and Plotly

CNN-based Cat vs Dog image classification web app using Flask and TensorFlow

构建深度学习图像分类模型识别 37 种猫狗品种 猫狗品种分类数据集的训练及应用

Cats vs Dogs Image Classification Web App using MobileNetV2 and Streamlit. Users can upload an image and the model predicts whether it is a cat or a dog.

This project is an image classification app where users upload an image for content predictions using different AI models: lung diagnosis and cat vs. dog classification.

A CNN-based Cat vs Dog image classification web app built using TensorFlow/Keras and Flask, allowing users to upload images and get real-time predictions.

CNN-based image classification app that distinguishes between Human, Cat, Dog, and Pig. Built with TensorFlow/Keras, it includes training scripts and a simple Flask web interface for uploading images and predicting class labels.

This app uses a custom made Cat, Dog and Rabbit ml model created using Create ML. It uses the ML Model within an app that captures live images from the user's camera for classification.

🐶🐱 A deep learning-based image classification web app that predicts whether an uploaded image is of a dog or a cat using a CNN model built with TensorFlow and Flask.

A Python image classification app using TensorFlow and Tkinter to identify images as "cat" or "dog" with a pre-trained CNN model. Features include a Tkinter GUI for refreshing and displaying random test images, predicting their classes, and uploading new images for real-time classification.

AI-powered web app for real-time image classification (e.g., cat vs dog). Built using TensorFlow, FastAPI, and Flask, with a responsive Bootstrap UI. Features include image preview, confidence scoring, and a RESTful backend.

A Gradio-based image classification app using a custom CNN model built with PyTorch. It predicts whether an uploaded image contains a cat, dog, or person, and returns the labeled image with the prediction overlayed.

This is a deep learning-based image classification app that predicts whether an uploaded image is of a cat or a dog. Built using Streamlit and a trained CNN model, the app provides real-time predictions with confidence scores.

This is a simple image classification project to identify whether an uploaded image is a cat or a dog. It uses TensorFlow (Keras) for training the model and Streamlit for a clean web app UI.