Datasets › Visual Domain Decathlon
Visual Domain Decathlon
The goal of this challenge is to solve simultaneously ten image classification problems representative of very different visual domains. The data for each domain is obtained from the following image classification benchmarks:
ImageNet
CIFAR-100
Aircraft
Daimler pedestrian classification
Describable textures
German traffic signs
Omniglot
SVHN
UCF101 Dynamic Images
VGG-Flowers
The union of the images from the ten datasets is split in training, validation, and test subsets. Different domains contain different image categories as well as a different number of images.
The task is to train the best possible classifier to address all ten classification tasks using the training and validation subsets, apply the classifier to the test set, and send us the resulting annotation file for assessment. The winner will be determined based on a weighted average of the classification performance on each domain, using the scoring scheme described below. At test time, your model is allowed to know the ground-truth domain of each test image (ImageNet, CIFAR-100, ...) but, of course, not its category.
Benchmarks archive 2025-07-28
All 1 leaderboard 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) | Paper | Code | ||||
|---|---|---|---|---|---|---|
| Continual Learning | visual domain decathlon (10 tasks) | NetTailor decathlon discipline (Score) 3744 | NetTailor: Tuning the Architecture, Not Just the Weights | pedro-morgado/nettailor | 14 | Compare |
Papers archive 2025-07-28
8 shown of 8 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 8. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.
| Date | Samples run Syntology | |||
|---|---|---|---|---|
| NetTailor: Tuning the Architecture, Not Just the Weights | 1 | 1 | 29 Jun 2019 | not harvested |
| Depthwise Convolution is All You Need for Learning Multiple Visual Domains | 1 | 2 | 3 Feb 2019 | not harvested |
| Efficient parametrization of multi-domain deep neural networks | 3 | 2 | 27 Mar 2018 | ran 2 of 3 samples (1 unverified) |
| Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights | 1 | 1 | 19 Jan 2018 | not harvested |
| Learning multiple visual domains with residual adapters | 2 | 5 | 22 May 2017 | not harvested |
| Incremental Learning Through Deep Adaptation | 0 | 1 | 11 May 2017 | not harvested |
| Universal representations:The missing link between faces, text, planktons, and cat breeds | 0 | 1 | 25 Jan 2017 | not harvested |
| Learning without Forgetting | 12 | 1 | 29 Jun 2016 | ran 9 of 15 samples (6 unverified; 1 pointer-only for licence) |
Dataset loaders archive 2025-07-28
1 loader as listed in the archive; links are outbound and not re-checked here.
Tasks archive 2025-07-28
License archive 2025-07-28
No licence recorded in the archive. Absence here is not a statement about the dataset's terms.
Modalities archive 2025-07-28
No modality tagged.
Languages archive 2025-07-28
No language tagged.
Variants archive 2025-07-28
- visual domain decathlon (10 tasks)
- Visual Domain Decathlon
2 variant names, as the archive lists them.
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