--- title: "align-anything vs SAM-Adapter-PyTorch" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/pku-alignment-align-anything-vs-tianrun-chen-sam-adapter-pytorch" tools: ["pku-alignment-align-anything", "tianrun-chen-sam-adapter-pytorch"] --- # align-anything vs SAM-Adapter-PyTorch *GraphCanon updated Aug 24, 2026* ## Verdict Pick align-anything if align Anything focuses on training large models with multiple forms of feedback across various data modalities, leveraging RLHF and DPO; pick SAM-Adapter-PyTorch if sAM-Adapter-PyTorch facilitates downstream task adaptation for SAM through adapters and prompts, specialized in camouflaged object detection with PyTorch. [align-anything](https://github.com/PKU-Alignment/align-anything) reports 4.7k GitHub stars, 505 forks, and 32 open issues, last pushed Nov 27, 2025. [SAM-Adapter-PyTorch](https://github.com/tianrun-chen/SAM-Adapter-PyTorch) has 1.6k stars, 123 forks, and 66 open issues, last pushed May 17, 2026. Figures are from public GitHub metadata via [align-anything's repository](https://github.com/PKU-Alignment/align-anything) and [SAM-Adapter-PyTorch's repository](https://github.com/tianrun-chen/SAM-Adapter-PyTorch). | | [align-anything](/tools/pku-alignment-align-anything.md) | [SAM-Adapter-PyTorch](/tools/tianrun-chen-sam-adapter-pytorch.md) | | --- | --- | --- | | Tagline | Training All-modality Model with Feedback | Adapting Meta AI's Segment Anything to Downstream Tasks with Adapters and Prompts | | Stars | 4,666 | 1,550 | | Forks | 505 | 123 | | Open issues | 32 | 66 | | Language | Python | Python | | Adopt for | Align Anything focuses on training large models with multiple forms of feedback across various data modalities, leveraging RLHF and DPO. | SAM-Adapter-PyTorch facilitates downstream task adaptation for SAM through adapters and prompts, specialized in camouflaged object detection with PyTorch. | | Persona | - | - | | Runtime | - | - | | License | This tool operates under Apache License 2.0, allowing free use, modification, and distribution provided copyright notices are preserved. | MIT | | Categories | LLM Frameworks, Model Training | Computer Vision, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [align-anything](/tools/pku-alignment-align-anything.md) | [SAM-Adapter-PyTorch](/tools/tianrun-chen-sam-adapter-pytorch.md) | | --- | --- | --- | | Days since push | 263d | 98d | | Open issues (now) | 32 | 66 | | Stars delta | +4 (30d) | +6 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/pku-alignment-align-anything/trust.md) | [trust report](/tools/tianrun-chen-sam-adapter-pytorch/trust.md) | ## Decision facts: align-anything - **Requirements:** Python execution environment - **Adopt for:** Align Anything focuses on training large models with multiple forms of feedback across various data modalities, leveraging RLHF and DPO. - **License detail:** This tool operates under Apache License 2.0, allowing free use, modification, and distribution provided copyright notices are preserved. ## Decision facts: SAM-Adapter-PyTorch - **Adopt for:** SAM-Adapter-PyTorch facilitates downstream task adaptation for SAM through adapters and prompts, specialized in camouflaged object detection with PyTorch. ## Choose when ### Choose align-anything if… - License: align-anything is Apache-2.0, SAM-Adapter-PyTorch is MIT. - Requirements: Python execution environment. - Tags unique to align-anything: chameleon, dpo, large language models, multimodal. - Also covers LLM Frameworks. - align-anything ships Docker support for self-hosted deployment. - - When you are developing a model that requires feedback from human evaluators and needs to handle different types of data (multimodal). ### Choose SAM-Adapter-PyTorch if… - License: SAM-Adapter-PyTorch is MIT, align-anything is Apache-2.0. - Tags unique to SAM-Adapter-PyTorch: 2d-segmentation, adapter, camouflage-images, camouflaged-object-detection. - Also covers Computer Vision. - Need to adapt SAM to specific tasks like detecting camouflaged objects ## When NOT to use align-anything - - When the model training does not benefit from advanced feedback mechanisms like RLHF or DPO. - - For projects that do not require support for multiple data modalities. ## When NOT to use SAM-Adapter-PyTorch - Looking for a toolset that primarily focuses on training from scratch rather than adapting pre-trained models - Interested in frameworks other than PyTorch ## Common questions ### What is the difference between align-anything and SAM-Adapter-PyTorch? align-anything: Training All-modality Model with Feedback. SAM-Adapter-PyTorch: Adapting Meta AI's Segment Anything to Downstream Tasks with Adapters and Prompts. See the comparison table for live GitHub stats and shared categories. ### When should I choose align-anything over SAM-Adapter-PyTorch? Choose align-anything over SAM-Adapter-PyTorch when License: align-anything is Apache-2.0, SAM-Adapter-PyTorch is MIT; Requirements: Python execution environment; Tags unique to align-anything: chameleon, dpo, large language models, multimodal; Also covers LLM Frameworks; align-anything ships Docker support for self-hosted deployment; - When you are developing a model that requires feedback from human evaluators and needs to handle different types of data (multimodal). ### When should I choose SAM-Adapter-PyTorch over align-anything? Choose SAM-Adapter-PyTorch over align-anything when License: SAM-Adapter-PyTorch is MIT, align-anything is Apache-2.0; Tags unique to SAM-Adapter-PyTorch: 2d-segmentation, adapter, camouflage-images, camouflaged-object-detection; Also covers Computer Vision; Need to adapt SAM to specific tasks like detecting camouflaged objects. ### When should I avoid align-anything? - When the model training does not benefit from advanced feedback mechanisms like RLHF or DPO. - For projects that do not require support for multiple data modalities. ### When should I avoid SAM-Adapter-PyTorch? Looking for a toolset that primarily focuses on training from scratch rather than adapting pre-trained models Interested in frameworks other than PyTorch ### Is align-anything or SAM-Adapter-PyTorch more popular on GitHub? align-anything has more GitHub stars (4,666 vs 1,550). Stars measure visibility, not whether either tool fits your constraints. ### Are align-anything and SAM-Adapter-PyTorch open source? Yes - both are open-source projects on GitHub (align-anything: Apache-2.0, SAM-Adapter-PyTorch: MIT). ### Where can I find alternatives to align-anything or SAM-Adapter-PyTorch? GraphCanon lists graph-backed alternatives at [align-anything alternatives](/tools/pku-alignment-align-anything/alternatives) and [SAM-Adapter-PyTorch alternatives](/tools/tianrun-chen-sam-adapter-pytorch/alternatives) ([align-anything markdown twin](/tools/pku-alignment-align-anything/alternatives.md), [SAM-Adapter-PyTorch markdown twin](/tools/tianrun-chen-sam-adapter-pytorch/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/pku-alignment-align-anything-vs-tianrun-chen-sam-adapter-pytorch.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, align-anything or SAM-Adapter-PyTorch? align-anything: Slowing. SAM-Adapter-PyTorch: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for align-anything and SAM-Adapter-PyTorch? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [align-anything trust report](/tools/pku-alignment-align-anything/trust); [SAM-Adapter-PyTorch trust report](/tools/tianrun-chen-sam-adapter-pytorch/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=pku-alignment-align-anything`](/api/graphcanon/graph?tool=pku-alignment-align-anything) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_