A PyTorch‑based project that trains and compares classical ML models (HOG+SVM, k‑NN) and a fine‑tuned ResNet‑18 on CIFAR‑10, including deep‑feature extraction, rich evaluation metrics, and clear, reproducible code.

This project uses the CIFAR-10 dataset, a standard benchmark dataset containing 60,000 32x32 color images from 10 classes, such as airplanes, cars, and cats. VisionNet incorporates data augmentation, feature visualization, and architectural experimentation.

1) PersonaChatbot with 5 unique AI personalities (RoastBot, Shakespeare, Emoji, Philosopher, Pirate) using HuggingFace and Streamlit. And (2) CIFAR-10 Image Classifier comparing CNN from scratch (74.6%) vs ResNet18 transfer learning (94.6%). Demonstrates NLP and Computer Vision skills.

A Deep Q Network to classify the images present in the CIFAR 10 dataset

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Classification Model built on CIFAR10 images

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The CIFAR.py script trains a CNN on CIFAR-10. It imports libraries, loads data, and visualizes sample images. Labels are converted to categorical format, and data is augmented. A Sequential model with DenseNet121 as the base and a final dense layer is defined, compiled, and trained. The model is saved and test predictions are visualized.

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Training and Testing a network to classify images from the CIFAR10 Dataset using a Convolution Neural network built-in TensorFlow

A CNN deep learning image classification model able to classify images of 10 different images with 75% accuracy

PyTorch CNN for 10 classes prediction. Dataset CIFAR10

Image Classifier practice with the reliable TensorFlow

This repo contains a Machine Learning Program for the CIFAR-10 Dataset. It achieves over 93% accuracy and uses various machine learning techniques. There is a notebook for further details with the code.

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An image classification model training based on the CIFAR-10 dataset

Image classifier using CIFAR-10 dataset

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Image Classifier

ImageClassifier is a script designed to classify images from the CIFAR-10 dataset.

ImageClassifier is a machine learning project that classifies images into one of ten categories (airplane, automobile, bird, cat, etc.) using the CIFAR-10 dataset. The model is built using Convolutional Neural Networks (CNNs) and visualizes the predictions through an interactive, user-friendly interface.