README.md

metadata

title: HyperView VisA Manufacturing
emoji: 🏭
colorFrom: gray
colorTo: blue
sdk: docker
app_port: 7860
pinned: false

HyperView - VisA Manufacturing Reference Retrieval

This Space builds a balanced subset of the VisA industrial visual anomaly dataset and opens HyperView with two side-by-side embedding spaces:

• CLIP ViT-B/32 in a Euclidean 2D layout
• Hyper3-CLIP hyper3-clip-v0.5 from the public hyper-models provider in a Poincare 2D layout

The workflow is inspection reference retrieval: given a production-line inspection image, retrieve the right normal references for the same SKU or product family. This maps to a real manufacturing QA pain point: engineers need to compare a questionable camera frame against the correct part library rather than a visually similar but wrong line or variant.

Benchmark Context

Fresh local probe on 600 VisA samples, using test inspection images as queries and train split images as the normal reference library:

Metric	Hyper3-CLIP	CLIP-B/32
Same-SKU mAP	0.9995	0.9924
Macaroni2 same-SKU mAP	1.0000	0.9377
Same-SKU P@10	1.0000	0.9983
Family P@10	1.0000	0.9997
Off-family leakage@10	0.0000	0.0003

The live Space uses a smaller cached interactive subset by default (VISA_SAMPLES_PER_CATEGORY=4 in local smoke runs) so the maps open quickly. The benchmark table above is the full 600-image protocol.

Keep the claim narrow: this is not defect segmentation or anomaly AUROC. It is a reference-retrieval workflow for inspection image libraries, where Hyper3-CLIP keeps same-SKU and same-family references slightly cleaner than CLIP on this sample.

Pilot framing: two lines, four SKUs, two weeks, using the plant's normal-reference image library. Success metrics should be wrong-SKU reference retrieval and QA lookup time, not defect segmentation accuracy.

Suggested acceptance thresholds: 30%+ faster QA reference lookup, 50%+ fewer wrong-SKU top-10 references versus the current baseline, at least three hard negative SKU families, and a failure report with abstentions/top misses.

Run locally from the HyperView repo:

VISA_SAMPLES_PER_CATEGORY=12 HYPERVIEW_PORT=6265 \
  uv run python hyperview-spaces/spaces/manufacturing-visa-reference-clip-hyper3clip/demo.py

The Docker image installs the bundled latest HyperView wheel for this repo and uses HyperView's public dataset, UI, and panel command APIs. Hyper3-CLIP loads through the public hyper-models provider catalog entry for the gated hyper3labs/hyper3-clip-v0.5 model repository. The Space needs an HF_TOKEN secret with access to that model. If unavailable, the Space can start with a clearly labeled CLIP fallback unless HYPERVIEW_ALLOW_CANDIDATE_FALLBACK=0 is set.