https://github.com/richasavant/data-science-automatic-image-caption-generation-nov_2023
This project focuses on automatic image caption generation, leveraging advanced deep learning techniques to create descriptive captions for images. The Jupyter Notebook provided in the repository demonstrates the implementation of a neural network model designed to analyze and interpret the content of images.
https://github.com/richasavant/data-science-automatic-image-caption-generation-nov_2023
automatic-image-annotation automatic-image-captioning cnn data-science deep-learning feature-extraction language-generation machine-learning ml neural-network rnn
Last synced: 3 months ago
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This project focuses on automatic image caption generation, leveraging advanced deep learning techniques to create descriptive captions for images. The Jupyter Notebook provided in the repository demonstrates the implementation of a neural network model designed to analyze and interpret the content of images.
- Host: GitHub
- URL: https://github.com/richasavant/data-science-automatic-image-caption-generation-nov_2023
- Owner: RichaSavant
- Created: 2024-07-10T13:48:58.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-07-15T15:25:08.000Z (10 months ago)
- Last Synced: 2025-01-14T11:58:18.362Z (4 months ago)
- Topics: automatic-image-annotation, automatic-image-captioning, cnn, data-science, deep-learning, feature-extraction, language-generation, machine-learning, ml, neural-network, rnn
- Language: Jupyter Notebook
- Homepage:
- Size: 3.3 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
This project focuses on automatic image caption generation, leveraging advanced deep learning techniques to create descriptive captions for images. The Jupyter Notebook provided in the repository demonstrates the implementation of a neural network model designed to analyze and interpret the content of images. By combining convolutional neural networks (CNNs) for image feature extraction and recurrent neural networks (RNNs) for language generation, the system can produce coherent and contextually relevant captions. This project aims to bridge the gap between visual and textual data, enhancing applications in areas such as accessibility, search engine optimization, and social media automation.