Code and resources for LMFM-12 microalgae image classification, comparing seven CNN architectures and evaluating transfer learning performance
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Updated
Nov 27, 2025 - Python
Code and resources for LMFM-12 microalgae image classification, comparing seven CNN architectures and evaluating transfer learning performance
Develop an AI-based image classification system using CNN and transfer learning. The project includes data preprocessing, model training, fine-tuning, evaluation with precision, recall, and F1-score, and testing.
Computer vision model for detecting whether workers are wearing safety helmets in images. Useful for automated safety monitoring in industrial environments.
An image classification project using convolutional neural networks to identify cats and dogs with high accuracy.
Implementation of the MCNN-14 model for fashion image classification, achieving 93.08% accuracy on Fashion-MNIST. Based on our paper “An Efficient Multiple Convolutional Neural Network Model (MCNN-14) for Fashion Image Classification.”
Image Classification Model, You can upload the following images: TRANSPORTS: Car, Boat, Airplane, Rocket, Helicopter, CARNIVORES: Raccoon, Otter, Dog, Lion, Tiger, Red_panda, Lynx, Jaguar, Bear, Fox, Cat FRUITS: Apple, Grape, Common_fig, Pear, Strawberry, Tomato, Lemon, Banana, Orange, Peach, Mango, Pineapple, Grapefruit, Pomegranate, Watermelon…
Custom deep learning model for binary image classification. Entirely built from scratch in Python with NumPy, including all ML functions, activations, and optimizations.
A project that involves using Machine Learning Models (such as Convolutional Neural Networks) to identify mathematical symbols and solve given expressions.
Real-time traffic sign recognition and classification system using hybrid Vision Transformer (ViT) and ResNet34 models. Achieves ~92% accuracy with YOLOv8-labeled data preprocessing and PyTorch-based inference pipeline.
An interactive web app that classifies images of cats and dogs. Built with Python, TensorFlow/Keras, and a user-friendly Streamlit interface.
Deep learning-based classification of 38 plant diseases using ResNet50
🖼️ End-to-end image classification — 🧱 custom CNN, 🔁 transfer learning, 🔭 Vision Transformer, and 🧩 XAI visualizations in a 🌐 Streamlit app.
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