image-classification and image-classification-mnist
These are ecosystem siblings—both are reference implementations of the same full-stack image classification architecture (Django backend + frontend framework + ML model) using different ML models (Inception-ResNet-v2 vs. MNIST/TensorFlow) and frontend frameworks (Next.js vs. React) to demonstrate alternative technical choices.
About image-classification
BobsProgrammingAcademy/image-classification
An image classification app built using Django 3, Django REST Framework 3, Next.js 12, and Material UI 5. The app uses Inception-ResNet-v2 to classify images selected by the user.
This application helps developers integrate image classification capabilities into their own projects. You provide it with images, and it outputs classifications for those images, powered by the Inception-ResNet-v2 model. It's intended for web developers or full-stack developers looking for a pre-built foundation to implement image recognition features.
About image-classification-mnist
BobsProgrammingAcademy/image-classification-mnist
An image classification app built using TensorFlow 2, Django 3, Django REST Framework 3, React 17, and Material UI 5.
This is a basic template for building web applications that classify handwritten digits. You input a drawn digit, and the application identifies which number it is. It's designed for developers who want a full-stack example of an image classification app.
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