AI Image Classifier
A deep learning image classification system built using PyTorch that predicts object categories from images.
Use Case & Problem Solved
This project solves the problem of automatically identifying objects from images using artificial intelligence. Manual classification of images is slow and error-prone. The model predicts categories instantly using a trained deep learning network. It is useful for educational AI demonstrations, dataset labeling automation, and understanding computer vision pipelines.
Key Features:
- Image classification using trained deep neural network
- Automatic preprocessing and normalization
- High accuracy predictions on test dataset
- Supports multiple image categories
- End-to-end ML pipeline (train → test → predict)
Project Description
The model is implemented using PyTorch and trained on an image dataset using a convolutional neural network (CNN). Images are resized, normalized, and converted into tensors before being passed into the neural network.
During training, the network learns patterns such as edges, shapes, and textures. After training, the model predicts the class of unseen images with probability scores.
Key Concepts Implemented:
• Convolutional Neural Networks (CNN)
• Dataset preprocessing
• Training & validation loop
• Loss optimization
• Model evaluation & inference