PASSL is a Paddle based vision library for state-of-the-art Self-Supervised Learning research with PaddlePaddle. PASSL aims to accelerate research cycle in self-supervised learning: from designing a new self-supervised task to evaluating the learned representations.
- Reproducible implementation of SOTA in Self-Supervision: Existing SOTA in Self-Supervision are implemented - SimCLR, MoCo(v1),MoCo(v2), MoCo-BYOL, CLIP. BYOL is coming soon. Also supports supervised trainings.
- Modular: Easy to build new tasks and reuse the existing components from other tasks (Trainer, models and heads, data transforms, etc.).
- See INSTALL.md.
Benchmark Linear Image Classification on ImageNet-1K
| epochs | official results | passl results | Backbone | Model | |
|---|---|---|---|---|---|
| MoCo | 200 | 60.6 | 60.64 | ResNet-50 | download |
| SimCLR | 100 | 64.5 | 65.3 | ResNet-50 | download |
| MoCo v2 | 200 | 67.7 | 67.72 | ResNet-50 | download |
| MoCo-BYOL | 300 | 71.56 | 72.10 | ResNet-50 | download |
| BYOL | 300 | 72.50 | 71.62 | ResNet-50 | download |
| PixPro | 100 | 55.1(fp16) | 57.2(fp32) | ResNet-50 | download |
Please see GETTING_STARTED.md for the basic usage of PASSL.