Visibility winner
huggingface/transformers
163.4K stars
Use this as the long-horizon mindshare read, not as the final answer.
Freshness winner
huggingface/transformers
+230 in the weekly window
Last commit Aug 5, 2026
Adoption winner
NVIDIA/Spatial-IQ
Package mapping still partial
GitStar keeps the comparison directional when linked package telemetry is missing.
Data completeness
Comparison confidence
0/2 package links
1/2 repos have weekly momentum in the current snapshot.
Compare up to three repositories with comma-separated owner/repo names. GitStar keeps missing package or momentum data explicit instead of flattening it into a false zero.
huggingface/transformers currently leads on long-horizon mindshare with the strongest star base in this comparison. Package mapping is still partial, so this comparison cannot declare a clean adoption winner yet.
GitStar keeps cross-repository gaps visible. Missing package mappings or partial momentum data lower confidence, but they do not stop the page from showing a directional read.
huggingface/transformers currently leads on long-horizon mindshare with the strongest star base in this comparison.
Stars are strongest at showing durable visibility, not direct production fit.
huggingface/transformers has the strongest current movement signal in this set.
Weekly movement is +230 in the weekly window.
Package mapping is still partial, so this comparison cannot declare a clean adoption winner yet.
Treat this as a directional evaluation and verify ecosystem usage directly from package registries.
This comparison lives in the same frameworks lane, so package usage and repo detail should carry more weight than raw stars alone.
Shared category overlap makes the comparison more decision-useful than a random popularity matchup.
A diagnostic framework that decomposes 3D object counting into nine hierarchical spatial perception and cognition sub-tasks. Analysis code for the paper Spatial-IQ: Deconstructing Spatial Intelligence via Hierarchical Capability Tests.
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.