SLiMe

SLiMe: Segment Like Me

SLiMe, short for Segment Like Me, represents an innovative leap in the field of image segmentation, specifically focusing on 1-shot segmentation techniques. Created using the power of Stable Diffusion, this PyTorch-based project provides a unique approach to segmenting images efficiently using minimal sample data.

What is SLiMe?

SLiMe is developed by a team of researchers from Simon Fraser University and Autodesk Research. It is presented as a method where both the training and implementation processes are designed to be straightforward and accessible to those familiar with Python and deep learning frameworks. The project offers a comprehensive method for segmenting images into distinct components based on single or limited samples.

Setup and Requirements

To get started with SLiMe, users need to set up a Python virtual environment and install the necessary dependencies. This ensures that all code runs smoothly without interference from other Python projects on your system.

python -m venv slime_venv
source slime_venv/bin/activate
pip install -r requirements.txt

Each image used in the project must have a corresponding mask, and file naming should be consistent. Images should be in PNG format, while masks are expected to be in NumPy format.

Training with SLiMe

Training SLiMe involves creating folders for storing training, validation, and test image data. SLiMe's command-line interface requires users to specify these directories, allowing for organized training and testing processes. An important part of training is segmenting particular parts in images. Users can define which parts to return (e.g., "background", "body", "head") to ensure proper segmentation during training.

python -m src.main --dataset sample \
                   --part_names {PARTNAMES} \
                   --train_data_dir {TRAIN_DATA_DIR} \
                   --val_data_dir {TRAIN_DATA_DIR} \
                   --test_data_dir {TEST_DATA_DIR} \
                   --train

Upon completion, results such as mean Intersection over Union (mIoU) for the segmented parts are displayed, and trained text embeddings are stored for future use.

Using Trained Text Embeddings

SLiMe allows users to test their images using pre-trained text embeddings, which can greatly enhance segmentation tasks by leveraging previously established training.

python -m src.main --dataset sample \
                   --checkpoint_dir {CHECKPOINT_DIR} \
                   --test_data_dir {TEST_DATA_DIR}

This flexible system can accommodate users who choose to utilize available trained embeddings, simplifying the testing process.

Patchifying Images

An interesting feature of SLiMe is the capacity to patchify images, breaking them down into smaller segments during processing. This allows for tailored image analysis by adjusting patch sizes and the number of patches per side, potentially optimizing segmentation results.

python -m src.main --dataset sample \
                   --checkpoint_dir {CHECKPOINT_DIR} \
                   --test_data_dir {TEST_DATA_DIR} \
                   --patch_size {PATCH_SIZE} \
                   --num_patches_per_side {NUM_PATCHES_PER_SIDE}

Different Dataset Applications

SLiMe offers functionality for 1-sample and 10-sample training on various datasets. This flexibility makes it suitable for needs ranging from small sample sizes to more robust dataset applications like PASCAL-Part and CelebAMask-HQ.

Conclusion

SLiMe: Segment Like Me is a dynamic and powerful tool for image segmentation with broad application potential. By providing detailed guidance for setup, training, and testing, it makes cutting-edge image segmentation accessible to a wide audience. This project is especially helpful for researchers and developers interested in exploring efficient and accurate image analysis methods using state-of-the-art techniques.

For any assistance or issues, users are encouraged to engage with the development community for support and improvements.