Parameter-Efficient-Transfer-Learning-Benchmark
Investigate a benchmark for parameter-efficient transfer learning in computer vision, assessing 25 leading algorithms on 30 varied datasets. The platform provides a modular codebase for comprehensive analysis in image recognition, video action recognition, and dense prediction. Pre-trained models like ViT and Swin are used to attain high performance with fewer parameters. The benchmark facilitates easy evaluation and continuous updates for new PETL methods and applications.
