• 2024/04/24: We updated our paper with the Direct Segment Anything Model (DirectSAM), which efficiently generates comprehensive subobject segmentations with a single forward pass! Checkout our latest arXiv (2402.14327v2) and 🎬Demo Video on YouTube or bilibili. The pretrained DirectSAM model is released on HuggingFace: 🤗DirectSAM-1800px-0424, and the training code is also available in this repo!

• 2024/02/23: Our paper is featured in AK's 🤗Huggingface Daily Papers.

• 2024/02/22: The first version of our paper is released on arXiv (2402.14327). Codes and models will be open-sourced at this repository.

• Clone the repository

  git clone https://github.com/ChenDelong1999/subobjects.git
  cd subobjects

• Install dependencies

  conda create -n subobjects python=3.11 -y
  conda activate subobjects
  pip install -r requirements.txt

• Run DirectSAM on an example image

  import requests
  from PIL import Image
  from transformers import AutoModelForSemanticSegmentation, AutoImageProcessor
  from utils import inference_single_image, visualize_direct_sam_result
  
  checkpoint = "chendelong/DirectSAM-1800px-0424"
  
  image_processor = AutoImageProcessor.from_pretrained(checkpoint, reduce_labels=True)
  model = AutoModelForSemanticSegmentation.from_pretrained(checkpoint).to('cuda').eval()
  
  url = "http:https://images.cocodataset.org/val2017/000000002149.jpg"
  image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
  
  probs = inference_single_image(image, image_processor, model, resolution=None, pyramid_layers=0)
  visualize_direct_sam_result(probs, image, threshold=0.25)

The probs is the predicted boundary probabilities of the image, which is an ndarray of shape (height, width) between 0 and 1. The visualize_direct_sam_result function will show visualizations using matplotlib, where the threshold controls the binarization of the boundary probabilities.

Quality of segmentation can be improved by increasing the input resolution and the number of pyramid layers. The above two groups of figures are generated using resolution=3600, pyramid_layers=1/pyramid_layers=2, and threshold=0.03.

Using half-precision model.half() can speed up the inference and reduce the GPU memory requirement.

We provide an example script to fine-tune DirectSAM on the ADE20K dataset. The implementation is based on 🤗 HuggingFace Trainer, please see this blog for a detailed tutorial.

The following command will start a distributed training with 512x512 resolution input and half-precision training, which takes around 9GB memory per GPU.

cd DirectSAM
CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node 4 trainer.py

The following figures compare the segmentation results of DirectSAM before and after the above finetuning on ADE20K.

If you find this work useful, please consider citing:

@article{chen2024subobject,
  author       = {Delong Chen and
                  Samuel Cahyawijaya and
                  Jianfeng Liu and
                  Baoyuan Wang and
                  Pascale Fung},
  title        = {Subobject-level Image Tokenization},
  journal      = {CoRR},
  volume       = {abs/2402.14327},
  year         = {2024},
  url          = {https://doi.org/10.48550/arXiv.2402.14327},
  doi          = {10.48550/ARXIV.2402.14327},
  eprinttype    = {arXiv},
  eprint       = {2402.14327}
}