SAM2-Adapter-CDW: Class-wise Segmentation of Construction and Demolition Waste

Environment

This code was implemented with Python 3.9 and PyTorch 2.3.0. You can install all the requirements via:

pip install -r requirements.txt

Quick Start

1. Download the dataset and split into training, validation and testing.
2. Download the pre-trained SAM 2(Segment Anything) and put it in ./pretrained.
3. Training:

torchrun --nproc_per_node=2 --nnodes=1 train.py --config [CONFIG_PATH]

4. Evaluation:

torchrun --nproc_per_node=2 --nnodes=1 test.py --config [CONFIG_PATH] --model [MODEL_PATH]

5. Download trained model weights from this shared link.

Dataset

Construction and Demolition Waste Segmentation

• [CDW-Seg - to be added]

Annotations

Please refer to Labelme package for annotation protocols.

Acknowledgements

SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More

Paper: https://arxiv.org/abs/2408.04579

Code: https://github.com/tianrun-chen/SAM-Adapter-PyTorch/tree/SAM2-Adapter-for-Segment-Anything-2

Cite

If you find our work valuable for your research, we kindly ask you to consider citing it.

Sirimewan, D., Bazli, M., Raman, S., Mohandes, S. R., Kineber, A. F., & Arashpour, M. (2024).
    Deep learning-based models for environmental management: Recognizing construction, renovation, and demolition waste in-the-wild.
    Journal of environmental management, 351, 119908.

Sirimewan, D., Harandi, M., Peiris, H., & Arashpour, M. (2024).
    Semi-supervised segmentation for construction and demolition waste recognition in-the-wild: Adversarial dual-view networks.
    Resources, Conservation and Recycling, 202, 107399. 

Sirimewan, D., Kunananthaseelan, N., Raman, S., Garcia, R., & Arashpour, M. (2024).
    Optimizing waste handling with interactive AI: Prompt-guided segmentation of construction and demolition waste using computer vision.
    Waste Management, 190, 149-160.