This repository presents an innovative approach to semantic segmentation using Transformers instead of traditional convolutional neural networks. Leveraging the power of Transformers in computer vision tasks, this project utilizes the Hugging Face Transformers library for semantic segmentation tasks.
Semantic segmentation remains a pivotal task in computer vision, and this project explores a novel paradigm by employing Transformers, specifically leveraging Hugging Face's Transformers library. This shift from traditional convolutional neural networks aims to demonstrate the versatility of Transformer architectures in semantic segmentation tasks.
The semantic segmentation model adopts a Transformer-based architecture, showcasing the adaptability of Transformers in pixel-wise classification tasks.
This project remains an ongoing effort, and potential future improvements include:
Fine-tuning for specific domains. Exploration of more advanced Transformer architectures. Integration with cloud services for scalable training. Contributions and suggestions for improvement are highly encouraged!
Feel free to open issues or pull requests. Contributions that enhance the model's performance or extend its capabilities are warmly welcomed.
To install the necessary dependencies, run the following command:
pip install transformers