| _version_ | 1866902137193103360 |
|---|---|
| author | A.Dinesh Kumar, Dr.G.Vijay Kumar |
| author_facet | A.Dinesh Kumar, Dr.G.Vijay Kumar |
| contents | <p>In this project, we study image classification using the Fashion MNIST data with 70,000 grayscale<br>images classed into t-shirts, dresses and sneakers. We want to create a model that can spot various clothing<br>types correctly, as this will support fashion recommendation systems, automate tagging and handle<br>inventory tasks.<br>The work is performed using Convolutional Neural Networks (CNNs) and is carried out using TensorFlow<br>and Keras. Normalisation of data is done so that the model’s training process will use consistent pixel<br>values. In CNN, layers called convolutional are used to detect features, max-pooling for decrease in data<br>and dense layers for classifying what’s found. Using ReLU activations helps the neural network better<br>learn, also stopping overfitting. A sparse categorical cross entropy loss is used to compile the model and it<br>is optimised using the Adam optimizer, trained periodically using a validation split.<br>It is clear from the model that both accuracy and loss decrease during the training process. Following<br>testing on the test set, its strength shows with a 91% accuracy. This result confirms that CNNs can be<br>applied to perform real-world image classification tasks.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15622599 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AI Driven Fashion Accessories Identifier Using Machine Learning Techniques by Image Classification A.Dinesh Kumar, Dr.G.Vijay Kumar <p>In this project, we study image classification using the Fashion MNIST data with 70,000 grayscale<br>images classed into t-shirts, dresses and sneakers. We want to create a model that can spot various clothing<br>types correctly, as this will support fashion recommendation systems, automate tagging and handle<br>inventory tasks.<br>The work is performed using Convolutional Neural Networks (CNNs) and is carried out using TensorFlow<br>and Keras. Normalisation of data is done so that the model’s training process will use consistent pixel<br>values. In CNN, layers called convolutional are used to detect features, max-pooling for decrease in data<br>and dense layers for classifying what’s found. Using ReLU activations helps the neural network better<br>learn, also stopping overfitting. A sparse categorical cross entropy loss is used to compile the model and it<br>is optimised using the Adam optimizer, trained periodically using a validation split.<br>It is clear from the model that both accuracy and loss decrease during the training process. Following<br>testing on the test set, its strength shows with a 91% accuracy. This result confirms that CNNs can be<br>applied to perform real-world image classification tasks.</p> |
| title | AI Driven Fashion Accessories Identifier Using Machine Learning Techniques by Image Classification |
| url | https://doi.org/10.5281/zenodo.15622599 |