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https://github.com/gosuto-inzasheru/aml-densenet-varying-learning-rates
Re-training the deep neural network DenseNet using various learning rate strategies. Entry for the Food Recognition Challenge for the Master's course Applied Machine Learning.
https://github.com/gosuto-inzasheru/aml-densenet-varying-learning-rates
cyclical-learning-rate deep-neural-networks densenet image-classification learning-rate-benchmarking learning-rate-decay
Last synced: 10 days ago
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Re-training the deep neural network DenseNet using various learning rate strategies. Entry for the Food Recognition Challenge for the Master's course Applied Machine Learning.
- Host: GitHub
- URL: https://github.com/gosuto-inzasheru/aml-densenet-varying-learning-rates
- Owner: gosuto-inzasheru
- Created: 2020-10-29T09:00:36.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2020-10-29T09:10:25.000Z (about 4 years ago)
- Last Synced: 2024-11-14T21:38:30.360Z (2 months ago)
- Topics: cyclical-learning-rate, deep-neural-networks, densenet, image-classification, learning-rate-benchmarking, learning-rate-decay
- Language: Jupyter Notebook
- Homepage:
- Size: 212 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# aml-densenet-varying-learning-rates
Food classification is a challenging problem due to the large number of food categories, since the visual similarity between different food categories is high. The goal is to build a model to predict the fine-grained food-category label given an image. This dataset contains 80 food categories indicated with a number.
