Verified claim · AI-ML · 82% confidence
Mask R-CNN introduced in paper: Mask R-CNN (He et al., 2017).
Last verified 2026-06-02 · Methodology veritas-v0.1 · 927b56032c69c6e5
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Structured fields
- Subject
- Mask R-CNN
- Predicate
introduced_in_paper- Object
- Mask R-CNN (He et al., 2017)
- Confidence
- 82%
- Tags
- mask-rcnn · instance-segmentation · object-detection · he · girshick · foundational · 2017
Sources (2)
[1] preprint · arXiv (Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick) · 2017-03-20
Mask R-CNN“Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition.”
[2] docs · Hugging Face
Mask R-CNN (Hugging Face Papers)Hugging Face is rated by SourceScore — see its reliability →
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curl https://sourcescore.org/api/v1/claims/927b56032c69c6e5.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/927b56032c69c6e5.json");
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// "Mask R-CNN introduced in paper: Mask R-CNN (He et al., 2017)."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/927b56032c69c6e5.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "Mask R-CNN introduced in paper: Mask R-CNN (He et al., 2017)."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_mask_r_cnn_fact() -> dict:
"""Fetch the verified SourceScore claim for Mask R-CNN."""
r = httpx.get("https://sourcescore.org/api/v1/claims/927b56032c69c6e5.json")
return r.json()