https://github.com/codename-detective/neural-image-classification
Neural Image Classification repository, where cutting-edge deep learning models have been crafted and fine-tuned for diverse image classification tasks. Leveraging state-of-the-art architectures and innovative techniques, this repository stands as a testament to high-performance image recognition.
https://github.com/codename-detective/neural-image-classification
alexnet computer-vision deep-learning image-classification vgg16
Last synced: 4 months ago
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Neural Image Classification repository, where cutting-edge deep learning models have been crafted and fine-tuned for diverse image classification tasks. Leveraging state-of-the-art architectures and innovative techniques, this repository stands as a testament to high-performance image recognition.
- Host: GitHub
- URL: https://github.com/codename-detective/neural-image-classification
- Owner: CodeName-Detective
- Created: 2023-08-11T21:58:58.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-08-11T22:45:18.000Z (almost 2 years ago)
- Last Synced: 2025-01-15T20:37:30.977Z (5 months ago)
- Topics: alexnet, computer-vision, deep-learning, image-classification, vgg16
- Language: Jupyter Notebook
- Homepage:
- Size: 1.25 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Neural-Image-Classification
* Developed and trained an initial AlexNet model, and then I modified the AlexNet architecture to enable the classification of three
categories: Dogs, Food, and Vehicles. This modification resulted in impressive accuracy rates of 90% and 92.6% for the respective
categories. Subsequently, I implemented the VGG-13 model, employing Mixed Precision Training techniques. This approach yielded a
remarkable accuracy of 91.4%
* Executed the implementation and training of a customized AlexNet model for the classification of Google Street View House
Numbers, resulting in an impressive accuracy of 91.4%.
* Developed and trained a customized AlexNet model for classifying the OCTMNIST dataset, yielding an accuracy of 71%.
* Created and trained a customized AlexNet model to classify a 10-class ImageNet dataset, achieving an accuracy of 68.4%. Then, I
implemented the VGG-13 model and employed Mixed Precision Training, which led to a notable accuracy improvement to 71.8%