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Vision / CIFAR-10

Image classification

Image → one of ten object classes

Model size vs score

Model size versus accuracyopenai/clip-vit-base-patch32: 151.28 million parameters; score 93.8%. All points use the same pinned protocol.00%3520%7040%10460%13980%174100%openai/clip-vit-base-patch32 · 151.28M parameters · 93.8%Model parameters (millions)accuracy · higher is better

1 compatible measurements. Parameter counts come from the architecture instantiated from each pinned model configuration; no weights are loaded for this count. Click a point to inspect its evidence.

Measured results

Pilot comparison · first 32 valid examples · accuracy higher is better. No global SOTA claim.

SystemScoreSamplesElapsed¹Evidence
openai/clip-vit-base-patch32
3d74acf9a28c
93.8%322sInspect run →
16 Oct 2026, 23:29 UTC

¹ Evaluation elapsed time includes model loading after dataset selection. It is not serving latency or a fair speed comparison.

What this measures

Benchmark origin
Dataset / split
Immutable dataset revision
0b2714987fa478483af9968de7c934580d0bb9a2
Upstream license metadata
unknown · consult upstream terms
Protocol
codesota-pilot-v1 / vision
Selection
First 32 valid examples in the pinned split order. SNLI rows without a valid label are excluded.
Configuration
Seed 42. Greedy generation for math and code. Mean answer-token log likelihood for text classification, completion and declarative multiple-choice recipes. No model judge.
Read scoring and comparison rules →

Planned runs

0 qualified visits in 30 days · priority 35.1. Interest changes execution order, within the approved baseline list.

SystemStatus
openai/clip-vit-base-patch32completed