Datasets › Caltech-256

Caltech-256

Introduced in Caltech-256 object category dataset archive 2025-07-28

Caltech-256 is an object recognition dataset containing 30,607 real-world images, of different sizes, spanning 257 classes (256 object classes and an additional clutter class). Each class is represented by at least 80 images. The dataset is a superset of the Caltech-101 dataset.

Source: Exploiting Non-Linear Redundancy for Neural Model Compression

Image Source: ML4A

Benchmarks archive 2025-07-28

All 4 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

First row (archive order)PaperCode
Image Classification Caltech-256 AG-Net Accuracy 96.89% Attend and Guide (AG-Net): A Keypoints-driven... DanielKovach/AG-Net 5 Compare
Few-Shot Image Classification Caltech-256 5-way (1-shot) UL-Hopfield (ULH) Accuracy 74.7 Unsupervised Learning using Pretrained CNN and... — 3 Compare
Semi-Supervised Image Classification Caltech-256, 1024 Labels UL-Hopfield (ULH) Accuracy 77.40% Unsupervised Learning using Pretrained CNN and... — 1 Compare
Semi-Supervised Image Classification Caltech-256 UL-Hopfield (ULH) Accuracy 77.40% Unsupervised Learning using Pretrained CNN and... — 1 Compare

Papers archive 2025-07-28

6 shown of 6 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 401. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

Dataset loaders archive 2025-07-28

3 loaders as listed in the archive; links are outbound and not re-checked here.

Tasks archive 2025-07-28

License archive 2025-07-28

Unknown

Modalities archive 2025-07-28

Languages archive 2025-07-28

No language tagged.

Variants archive 2025-07-28

  • Caltech-256, 1024 Labels
  • Caltech-256
  • Caltech-256 5-way (1-shot)

3 variant names, as the archive lists them.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections