Segment Anything Model — SAM: zero-shot image segmentation via points, boxes, masks
Segment Anything Model — SAM: zero-shot image segmentation via points, boxes, masks
Start with meaning, then move to detail.
This lesson explains Segment Anything Model — SAM: zero-shot image segmentation via points, boxes, masks as part of Hermes internals and extension points. You will learn what it does, when it matters, and the smallest safe test that proves it works.
If you are new, do not memorize names. Focus on three questions: what problem does this solve, what access does it need, and how can you verify the result?
For practice, inspect the first example, identify its effects, run it on test data, and compare the result with the source claim.
For advanced readers, inspect Skill metadata, Reference: full SKILL.md, When to use SAM, then verify failure modes and version compatibility.
Know Python, Git, and basic project structure before changing code.
A clear outcome before you read.
- Understand Segment Anything Model — SAM: zero-shot image segmentation via points, boxes, masks without assumed prior knowledge.
- Separate the source description from what still needs testing in your environment.
- Read the first command and identify its inputs and outputs before copying it.
Short definitions before the details.
- Provider
- The service that runs or provides access and authentication to a model.
- Skill
- An instruction bundle that teaches Hermes a repeatable workflow without necessarily adding an external service.
SAM: zero-shot image segmentation via points, boxes, masks
What does the source say, and in what order?
- 01Skill metadata
Start here to understand the core idea or structure.
- 02Reference: full SKILL.md
Read this after the foundation, then connect it to the previous step.
- 03When to use SAM
Read this after the foundation, then connect it to the previous step.
- 04Quick start
Read this after the foundation, then connect it to the previous step.
- 05Installation
Read this after the foundation, then connect it to the previous step.
- 06Download checkpoints
Read this after the foundation, then connect it to the previous step.
- 07Basic usage with SamPredictor
Read this after the foundation, then connect it to the previous step.
- 08HuggingFace Transformers
Read this after the foundation, then connect it to the previous step.
- 09Core concepts
Read this after the foundation, then connect it to the previous step.
- 10Model architecture
Finish here to verify the result and special cases.
Copy only after you understand the effect.
# From GitHub
pip install git+https://github.com/facebookresearch/segment-anything.git
# Optional dependencies
pip install opencv-python pycocotools matplotlib
# Or use HuggingFace transformers
pip install transformers# ViT-H (largest, most accurate) - 2.4GB
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth
# ViT-L (medium) - 1.2GB
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth
# ViT-B (smallest, fastest) - 375MB
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth### HuggingFace TransformersRead the first command and identify its inputs and outputs before copying it.
Match every command to your installed Hermes version, review the files and accounts it can reach, and use non-sensitive data for the first test. If this explanation differs from the source, the official source wins.