Build, integrate, fine-tune, and evaluate Meta SAM 3 with reproducible commands and minimal setup friction.
| Install SAM 3 and run first inference | Follow setup in references/setup-and-inference.md | | Add SAM 3 to an existing Python app | Generate starter code with scripts/createinferencestarter.py and adapt API calls | | Verify environment before setup/inference | Run scripts/sam3preflightcheck.py |
| Fine-tune on custom data | Use references/training-and-eval.md training flow and config guidance | | Run SA-Co benchmarks or eval custom predictions | Use eval commands in references/training-and-eval.md and upstream scripts/eval/ docs | | Debug runtime failures | Run the troubleshooting checklist in references/setup-and-inference.md |
Create and work with Meta SAM 3 (facebookresearch/sam3) for open-vocabulary image and video segmentation with text, point, box, and mask prompts. Use when setting up SAM3 environments, requesting Hugging Face checkpoint access, generating inference scripts, integrating SAM3 into Python apps, fine-tuning with sam3/train configs, running SA-Co or custom evaluations, or debugging CUDA/checkpoint/prompt pipeline issues. Source: jakerains/agentskills.