This project aims to generate descriptive captions for images using a pre-trained image captioning model. It utilizes deep learning techniques to associate images with relevant textual descriptions.
Image captioning is the task of generating natural language descriptions for images. This project focuses on utilizing a pre-trained captioning model to generate captions for a given image dataset.
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Clone the repository:
git clone https://github.com/your-username/image-captioning.git
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Install the required dependencies:
pip install -r requirements.txt
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Ensure that your image dataset and caption dataset are prepared as described in the Dataset section.
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Modify the pretrained_captioning_model.py script with the appropriate paths and configurations for your pre-trained model.
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Run the pretrained_captioning_model.py script to generate captions for your images:
python pretrained_captioning_model.pyThe generated captions will be displayed in the console output.
The image dataset and caption dataset used for training the captioning model should be prepared as follows:
Image Dataset: Place your collection of images inside the images directory. Ensure the images are in a suitable format (e.g., JPG or PNG).
Caption Dataset: Create a file named captions.txt that contains the captions corresponding to each image in your dataset. Each line should follow the format: image_filename\tcaption1, caption2, ....
If you wish to train your own captioning model, refer to the Model Training guide for detailed instructions.
The results and performance metrics achieved by the pre-trained model on our dataset can be found in the Results document.
Contributions to this project are welcome! If you have any ideas, improvements, or bug fixes, please submit a pull request.
This project is licensed under the MIT License.