image-classification-model

Code and resources for LMFM-12 microalgae image classification, comparing seven CNN architectures and evaluating transfer learning performance

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.”

Computer vision model for detecting whether workers are wearing safety helmets in images. Useful for automated safety monitoring in industrial environments.

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.

Modular PyTorch image classification framework supporting multiple models and datasets with automatic input adaptation

An end-to-end Deep Learning project that classifies plant leaf images into 38 distinct categories (covering 14 species) to detect health status and specific diseases. Leveraging MobileNetV2 with Transfer Learning.

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.

dcpvaml 🚗🔩 : Automotive & Insurance # Vertex AutoML Vision # Damaged Car Parts

Deep learning project using CNNs to classify fruit and tire images.

A project that involves using Machine Learning Models (such as Convolutional Neural Networks) to identify mathematical symbols and solve given expressions.

🖼️ End-to-end image classification - 🧱 custom CNN, 🔁 transfer learning, 🔭 Vision Transformer, and 🧩 XAI visualizations in a 🌐 Streamlit app.

Custom deep learning model for binary image classification. Entirely built from scratch in Python with NumPy, including all ML functions, activations, and optimizations.

An image classification project using convolutional neural networks to identify cats and dogs with high accuracy.

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…

An AI-powered system using CNN and Grad-CAM to detect and visualize brain tumors from MRI scans, offering accurate predictions and interpretable medical imaging insights.

Deep learning-based classification of 38 plant diseases using ResNet50

End-to-end CNN pipeline using TensorFlow/Keras for image classification (dogs vs. cats). Includes data preprocessing, training, evaluation, and inference scripts.

🍅 CNN & Streamlit based Tomato Leaf Disease Detection with 11-class classification, real-time prediction and Docker deployment.

An interactive web app that classifies images of cats and dogs. Built with Python, TensorFlow/Keras, and a user-friendly Streamlit interface.

Browser-based image classification example application using OpenAI CLIP and Transformers.js. Upload images, run zero-shot classification with custom labels directly in the browser, and store images plus results in S3-compatible Backblaze B2 cloud object storage using pre-signed uploads.