Pith Number

pith:NO6CY7DA

pith:2023:NO6CY7DAGK6FHI6SZCLGCUJYDR

not attested not anchored not stored refs resolved

Faster Segment Anything: Towards Lightweight SAM for Mobile Applications

Chaoning Zhang, Choong Seon Hong, Dongshen Han, Jung Uk Kim, Seungkyu Lee, Sung-Ho Bae, Yu Qiao

Distilling SAM's heavy encoder into a lightweight one creates MobileSAM, over 60 times smaller with matching zero-shot segmentation performance.

arxiv:2306.14289 v2 · 2023-06-25 · cs.CV

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\pithnumber{NO6CY7DAGK6FHI6SZCLGCUJYDR}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

the resulting lightweight SAM is termed MobileSAM which is more than 60 times smaller yet performs on par with the original SAM

C2weakest assumption

that a lightweight encoder distilled only from the frozen original encoder will remain compatible with the original mask decoder across diverse downstream tasks without further joint fine-tuning

C3one line summary

MobileSAM is a 60x smaller distilled version of SAM that matches original performance and runs 5x faster than concurrent FastSAM while supporting CPU inference.

References

18 extracted · 18 resolved · 4 Pith anchors

[1] One small step for generative ai, one giant leap for agi: A complete survey on chatgpt in aigc era

[2] On the Opportunities and Risks of Foundation Models · arXiv:2108.07258

[3] Mp-fedcl: Multi-prototype federated contrastive learning for edge intelligence

[4] Fast segment anything

[5] Segment anything in medical images

Formal links

2 machine-checked theorem links

Cited by

28 papers in Pith

On Efficient Variants of Segment Anything Model: A Survey

MapNav: A Novel Memory Representation via Annotated Semantic Maps for Vision-and-Language Navigation

A Deployable Embodied Vision-Language Navigation System with Hierarchical Cognition and Context-Aware Exploration

DarkLLM: Learning Language-Driven Adversarial Attacks with Large Language Models

SparseSAM: Structured Sparsification of Activations in Segment Anything Models

Receipt and verification

First computed	2026-05-17T23:38:12.782631Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

6bbc2c7c6032bc53a3d2c8966151381c64e83197a00b969fc960c1b941ae98f6

Aliases

arxiv: 2306.14289 · arxiv_version: 2306.14289v2 · doi: 10.48550/arxiv.2306.14289 · pith_short_12: NO6CY7DAGK6F · pith_short_16: NO6CY7DAGK6FHI6S · pith_short_8: NO6CY7DA

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/NO6CY7DAGK6FHI6SZCLGCUJYDR \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 6bbc2c7c6032bc53a3d2c8966151381c64e83197a00b969fc960c1b941ae98f6

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "262fac928c7e47438491724ca07d6e870797dc053df61f2173e6c21a926a9a38",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2023-06-25T16:37:25Z",
    "title_canon_sha256": "4becd44a08c66d2aaa9186af9b0864a26f2d60ff538c3b3d3185e17a643bc367"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2306.14289",
    "kind": "arxiv",
    "version": 2
  }
}