sam-vit-huge
by facebook
Promptable foundation model for image segmentation
facebook/sam-vit-hugemixpeek://image_extractor@v1/facebook_sam_vit_huge_v1Overview
SAM (Segment Anything Model) is Meta's foundation model for image segmentation. Given prompts like points, boxes, or text, it produces high-quality object masks. Trained on SA-1B — the largest segmentation dataset with 1 billion masks on 11M images.
On Mixpeek, SAM powers pixel-level object segmentation for precise content understanding, enabling mask-based filtering and region-specific feature extraction.
Architecture
ViT-H image encoder (632M params) with a lightweight mask decoder. Produces 256x256 low-res masks refined to full resolution. Supports multiple prompt types: points, boxes, and masks.
Mixpeek SDK Integration
import { Mixpeek } from "mixpeek";
const mx = new Mixpeek({ apiKey: "API_KEY" });
// Managed: create a collection over a bucket; Mixpeek runs this model's extractor
const collection = await mx.collections.create({
namespace_id: "my-namespace",
collection_name: "my-collection",
source: { type: "bucket", bucket_ids: ["bkt_your_bucket"] },
feature_extractor: {
feature_extractor_name: "segmentation",
version: "v1",
parameters: { model_id: "facebook/sam-vit-huge" },
},
});Capabilities
- Promptable segmentation with points, boxes, or masks
- Automatic mask generation for everything in an image
- Zero-shot transfer competitive with supervised models
- Trained on 1 billion masks (SA-1B dataset)
Use Cases on Mixpeek
Benchmarks
| Dataset | Metric | Score | Source |
|---|---|---|---|
| SA-1B (segmentation) | mIoU | 79.3 | Kirillov et al., 2023 — Table 1 |
| COCO (instance seg.) | AP | 46.5 | Kirillov et al., 2023 — Table 7 |
Performance
Image encoder runs once; mask decoder runs per prompt (~6ms)
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Specification
Research Paper
Segment Anything
arxiv.orgBuild a pipeline with sam-vit-huge
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