Tasks SotA Datasets Papers Methods Submit About

Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable Benchmarks All SotA Datasets Papers Methods

Community

Submit Results About

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Models/Model soups (BASIC-L)

Model soups (BASIC-L)

Reported on 7 benchmarks across 3 tasks · 1 paper · 7 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision4 results

Image ClassificationonImageNet V2
Top 1 Accuracy· uses extra data· 2022-03-10
84.63
SOTA
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time arXiv:2203.05482
Domain GeneralizationonImageNet-R
Top-1 Error Rate· uses extra data· 2022-03-10
3.9
SOTA
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time arXiv:2203.05482
Domain GeneralizationonImageNet-A
Top-1 accuracy %· uses extra data· 2022-03-10
94.17
SOTA
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time arXiv:2203.05482
Domain GeneralizationonImageNet-Sketch
Top-1 accuracy· uses extra data· 2022-03-10
77.18
SOTA
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time arXiv:2203.05482

Methodology3 results

Domain AdaptationonImageNet-R
Top-1 Error Rate· uses extra data· 2022-03-10
3.9
SOTA
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time arXiv:2203.05482
Domain AdaptationonImageNet-A
Top-1 accuracy %· uses extra data· 2022-03-10
94.17
SOTA
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time arXiv:2203.05482
Domain AdaptationonImageNet-Sketch
Top-1 accuracy· uses extra data· 2022-03-10
77.18
SOTA
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time arXiv:2203.05482