vandit15/Class-balanced-loss-pytorch
Pytorch implementation of the paper "Class-Balanced Loss Based on Effective Number of Samples"
PythonMIT
Stargazers
- AdilZouitineIRT Saint-Exupéry
- bityangke
- bourbakisShanghai
- BowieHsuAlibaba
- cpoptic
- d0ng1ee
- diggerduBytedance AI Lab
- dnanhkhoa@aistairc
- FelixZhang7shenzhen
- fly51flyPRIS
- fuxuliuHome
- gazay@playbook-labs
- kenshoharaNational Institute of Advanced Industrial Science and Technology
- khursani8Kuala Lumpur
- L1aoXingyuBeijing, China
- lolongcovas
- mhiro2@mc-digital
- michalwolsNew York
- NikronicEarth
- rainfalj
- rhythm92Japan
- santialferez@UPC
- seefunShanghai, China
- SeuTaoShanghai/Shenzhen
- SikaStarPeking University
- snakers4
- spillaiAutonomi AI, Inc.
- ssdyueJD
- TheShadow29Meta
- tkianai别怕失败，大不了从头来过
- Venka97MILA
- vfdev-5@Quansight
- xuanhan863Los Angeles, USA
- yaoqingyuan
- zhijlXidian University
- zxt881108Qiniu Atlab