SAM-Adapter-PyTorch
Adapting Meta AI's Segment Anything to Downstream Tasks with Adapters and Prompts
GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
SAM-Adapter-PyTorch facilitates downstream task adaptation for SAM through adapters and prompts, specialized in camouflaged object detection with PyTorch.
Good fit when
- Need to adapt SAM to specific tasks like detecting camouflaged objects
- Working on fine-tuning models using adapter modules within the PyTorch framework
Avoid when
- Looking for a toolset that primarily focuses on training from scratch rather than adapting pre-trained models
- Interested in frameworks other than PyTorch
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Slowing (125d since push)
- As of Sep 19, 2026
- Provenance
- Not a fork · Personal account
- As of Sep 19, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
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Install
pip install SAM-Adapter-PyTorchHow it fits your stack(1)
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Evidence and technical details
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Overview
A PyTorch-based repository that facilitates the adaptation of the Segment Anything Model (SAM) for downstream tasks such as camouflaged object detection through the use of adapters and prompts.
Capability facts
- Languages
- python
Source: github.language · Sep 19, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 19, 2026)
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nnodes 1 --nproc_per_node 4 loadddptrain.py --conSource link
Tags
README
Quick Start 1. Download the dataset and put it in ./load. 2. Download the pre trained SAM(Segment Anything) and put it in ./pretrained. 3. Training: !Please note that the SAM model consume much memory. We use 4 x A100 graphics card for training. If you encounter the memory issue,...
For agents
This page has a .md twin and JSON over the API.