X-SAM addresses the limitations of existing segmentation models like SAM by extending capabilities to "any segmentation" through a unified multimodal large language model (MLLM) framework. It targets researchers and developers seeking advanced pixel-level perceptual understanding in MLLMs, offering state-of-the-art performance and a novel Visual GrounDed (VGD) segmentation task.

How It Works

X-SAM employs a unified MLLM framework to overcome LLMs' inherent deficiency in pixel-level understanding and SAM's limitations in multi-mask and category-specific segmentation. It introduces the Visual GrounDed (VGD) segmentation task, which segments all instance objects using interactive visual prompts, grounding MLLMs with pixel-wise interpretative capabilities. A unified training strategy enables co-training across diverse datasets, leading to state-of-the-art results on segmentation benchmarks.

Quick Start & Requirements

• Primary install: git clone --depth=1 https://github.com/wanghao9610/X-SAM.git, followed by environment setup and pip/conda installs.
• Prerequisites: Python 3.10, PyTorch 2.5.1 with CUDA 12.4, xtuner v0.2.0, deepspeed, flash-attention v2.7.3, and GCC 11 (recommended).
• Setup: Requires careful environment configuration, including CUDA path setup and specific library installations.
• Links: Online demos available; Technical Report and Model Weights on HuggingFace.

Highlighted Details

• Unified MLLM framework for advanced pixel-level perceptual understanding.
• Introduces Visual GrounDed (VGD) segmentation task for instance segmentation via visual prompts.
• Unified training strategy supports co-training across multiple datasets.
• Achieves state-of-the-art performance on various image segmentation benchmarks.

Maintenance & Community

The project is under active development with ongoing updates planned. Communication is encouraged in English via GitHub issues. No specific community channels or sponsorship details are provided.

Licensing & Compatibility

The repository's license is not explicitly stated in the README, which requires clarification for commercial or closed-source integration.

Limitations & Caveats

X-SAM is actively under development, with key features like mixed fine-tuning training code and transformer-compatible inference/demo code marked as TODOs. Current evaluation support is limited to generic and VGD segmentation. The project mandates specific library versions (e.g., PyTorch 2.5.1, CUDA 12.4), potentially impacting broader compatibility.