Article Link: https://www.analyticsvidhya.com/blog/2023/07/depth-prediction-transformers/

This repository contains the implementation of Depth Prediction Transformers (DPT), a deep learning model for accurate depth estimation in computer vision tasks. DPT leverages the transformer architecture and an encoder-decoder framework to capture fine-grained details, model long-range dependencies, and generate precise depth predictions.

• Depth estimation from 2D images using Depth Prediction Transformers.
• Integration of transformer-based encoder and decoder components for accurate depth prediction.
• Implementation of self-attention mechanisms, upsampling, and convolutional layers.
• Support for various computer vision tasks, including autonomous navigation, augmented reality, 3D reconstruction, and robotics.

1. Import the necessary modules: import dpt
2. Load the pre-trained model: model = dpt.load_model('path/to/model.weights')
3. Preprocess the input image: image = dpt.preprocess_image('path/to/image.jpg')
4. Perform depth estimation: depth_map = model.predict(image)
5. Visualize the depth map or use it for further analysis.

Contributions to the project are welcome. If you find any issues or have suggestions for improvements, please leave a comment on the accompanying blog article.

We would like to acknowledge the contributions and research efforts of the original authors of Depth Prediction Transformers. Their work serves as the foundation for this implementation.

https://huggingface.co/docs/transformers/main/en/model_doc/dpt