solo-learn

A library of self-supervised methods for unsupervised visual representation learning powered by PyTorch Lightning. We aim at providing SOTA self-supervised methods in a comparable environment while, at the same time, implementing training tricks. While the library is self-contained, it is possible to use the models outside of solo-learn.

News

[Feb 04 2022]: 🥳 Paper got accepted to JMLR.
[Jan 31 2022]: 👁️ Added ConvNeXt support with timm.
[Dec 20 2021]: 🌡️ Added ImageNet results, scripts and checkpoints for MoCo V2+.
[Dec 05 2021]: 🎶 Separated SupCon from SimCLR and added runs.
[Dec 01 2021]: ⛲ Added PoolFormer.
[Nov 29 2021]: ‼️ Breaking changes! Update your versions!!!
[Nov 29 2021]: 📖 New tutorials!
[Nov 29 2021]: 🏘️ Added offline K-NN and offline UMAP.
[Nov 29 2021]: 🚨 Updated PyTorch and PyTorch Lightning versions. 10% faster.
[Nov 29 2021]: 🍻 Added code of conduct, contribution instructions, issue templates and UMAP tutorial.
[Nov 23 2021]: 👾 Added VIbCReg.
[Oct 21 2021]: 😤 Added support for object recognition via Detectron v2 and auto resume functionally that automatically tries to resume an experiment that crashed/reached a timeout.
[Oct 10 2021]: 👹 Restructured augmentation pipelines to allow more flexibility and multicrop. Also added multicrop for BYOL.
[Sep 27 2021]: 🍕 Added NNSiam, NNBYOL, new tutorials for implementing new methods 1 and 2, more testing and fixed issues with custom data and linear evaluation.
[Sep 19 2021]: 🦘 Added online k-NN evaluation.
[Sep 17 2021]: 🤖 Added ViT and Swin.
[Sep 13 2021]: 📖 Improved Docs and added tutorials for pretraining and offline linear eval.
[Aug 13 2021]: 🐳 DeepCluster V2 is now available.

Methods available:

Extra flavor

Multiple backbones

Data

Increased data processing speed by up to 100% using Nvidia Dali.
Flexible augmentations.

Evaluation and logging

Online linear evaluation via stop-gradient for easier debugging and prototyping (optionally available for the momentum backbone as well).
Online and offline K-NN evaluation.
Normal offline linear evaluation.
All the perks of PyTorch Lightning (mixed precision, gradient accumulation, clipping, automatic logging and much more).
Easy-to-extend modular code structure.
Custom model logging with a simpler file organization.
Automatic feature space visualization with UMAP.
Offline UMAP.
Common metrics.

Training tricks

Multi-cropping dataloading following SwAV:
- Note: currently, only SimCLR and BYOL supports this.
Exclude batchnorm and biases from LARS.
No LR scheduler for the projection head in SimSiam.

Requirements

torch
torchvision
tqdm
einops
wandb
pytorch-lightning
lightning-bolts
torchmetrics
scipy
timm

Optional:

nvidia-dali
matplotlib
seaborn
pandas
umap-learn

Installation

First clone the repo.

Then, to install solo-learn with Dali and/or UMAP support, use:

pip3 install .[dali,umap] --extra-index-url https://developer.download.nvidia.com/compute/redist

If no Dali/UMAP support is needed, the repository can be installed as:

pip3 install .

NOTE: if you are having trouble with dali, install it following their guide.

NOTE 2: consider installing Pillow-SIMD for better loading times when not using Dali.

NOTE 3: Soon to be on pip.

Training

For pretraining the backbone, follow one of the many bash files in bash_files/pretrain/.

After that, for offline linear evaluation, follow the examples in bash_files/linear.

There are extra experiments on K-NN evaluation in bash_files/knn/ and feature visualization with UMAP in bash_files/umap/.

NOTE: Files try to be up-to-date and follow as closely as possible the recommended parameters of each paper, but check them before running.

Tutorials

Please, check out our documentation and tutorials:

If you want to contribute to solo-learn, make sure you take a look at how to contribute and follow the code of conduct

Model Zoo

All pretrained models avaiable can be downloaded directly via the tables below or programmatically by running one of the following scripts zoo/cifar10.sh, zoo/cifar100.sh, zoo/imagenet100.sh and zoo/imagenet.sh.

Results

Note: hyperparameters may not be the best, we will be re-running the methods with lower performance eventually.

CIFAR-10

Method	Backbone	Epochs	Dali	Acc@1	Acc@5	Checkpoint
Barlow Twins	ResNet18	1000	❌	92.10	99.73	🔗
BYOL	ResNet18	1000	❌	92.58	99.79	🔗
DeepCluster V2	ResNet18	1000	❌	88.85	99.58	🔗
DINO	ResNet18	1000	❌	89.52	99.71	🔗
MoCo V2+	ResNet18	1000	❌	92.94	99.79	🔗
NNCLR	ResNet18	1000	❌	91.88	99.78	🔗
ReSSL	ResNet18	1000	❌	90.63	99.62	🔗
SimCLR	ResNet18	1000	❌	90.74	99.75	🔗
Simsiam	ResNet18	1000	❌	90.51	99.72	🔗
SupCon	ResNet18	1000	❌	93.82	99.65	🔗
SwAV	ResNet18	1000	❌	89.17	99.68	🔗
VIbCReg	ResNet18	1000	❌	91.18	99.74	🔗
VICReg	ResNet18	1000	❌	92.07	99.74	🔗
W-MSE	ResNet18	1000	❌	88.67	99.68	🔗

CIFAR-100

Method	Backbone	Epochs	Dali	Acc@1	Acc@5	Checkpoint
Barlow Twins	ResNet18	1000	❌	70.90	91.91	🔗
BYOL	ResNet18	1000	❌	70.46	91.96	🔗
DeepCluster V2	ResNet18	1000	❌	63.61	88.09	🔗
DINO	ResNet18	1000	❌	66.76	90.34	🔗
MoCo V2+	ResNet18	1000	❌	69.89	91.65	🔗
NNCLR	ResNet18	1000	❌	69.62	91.52	🔗
ReSSL	ResNet18	1000	❌	65.92	89.73	🔗
SimCLR	ResNet18	1000	❌	65.78	89.04	🔗
Simsiam	ResNet18	1000	❌	66.04	89.62	🔗
SupCon	ResNet18	1000	❌	70.38	89.57	🔗
SwAV	ResNet18	1000	❌	64.88	88.78	🔗
VIbCReg	ResNet18	1000	❌	67.37	90.07	🔗
VICReg	ResNet18	1000	❌	68.54	90.83	🔗
W-MSE	ResNet18	1000	❌	61.33	87.26	🔗

ImageNet-100

Method	Backbone	Epochs	Dali	Acc@1 (online)	Acc@1 (offline)	Acc@5 (online)	Acc@5 (offline)	Checkpoint
Barlow Twins 🚀	ResNet18	400	✔️	80.38	80.16	95.28	95.14	🔗
BYOL 🚀	ResNet18	400	✔️	80.16	80.32	95.02	94.94	🔗
DeepCluster V2	ResNet18	400	❌	75.36	75.4	93.22	93.10	🔗
DINO	ResNet18	400	✔️	74.84	74.92	92.92	92.78	🔗
DINO 😪	ViT Tiny	400	❌	63.04	TODO	87.72	TODO	🔗
MoCo V2+ 🚀	ResNet18	400	✔️	78.20	79.28	95.50	95.18	🔗
NNCLR 🚀	ResNet18	400	✔️	79.80	80.16	95.28	95.30	🔗
ReSSL	ResNet18	400	✔️	76.92	78.48	94.20	94.24	🔗
SimCLR 🚀	ResNet18	400	✔️	77.04	77.48	94.02	93.42	🔗
Simsiam	ResNet18	400	✔️	74.54	78.72	93.16	94.78	🔗
SupCon	ResNet18	400	✔️	84.40	TODO	95.72	TODO	🔗
SwAV	ResNet18	400	✔️	74.04	74.28	92.70	92.84	🔗
VIbCReg	ResNet18	400	✔️	79.86	79.38	94.98	94.60	🔗
VICReg 🚀	ResNet18	400	✔️	79.22	79.40	95.06	95.02	🔗
W-MSE	ResNet18	400	✔️	67.60	69.06	90.94	91.22	🔗

🚀 methods where hyperparameters were heavily tuned.

😪 ViT is very compute intensive and unstable, so we are slowly running larger architectures and with a larger batch size. Atm, total batch size is 128 and we needed to use float32 precision. If you want to contribute by running it, let us know!

ImageNet

Method	Backbone	Epochs	Dali	Acc@1 (online)	Acc@1 (offline)	Acc@5 (online)	Acc@5 (offline)	Checkpoint
Barlow Twins	ResNet50	100	✔️	67.18	67.23	87.69	87.98	🔗
BYOL	ResNet50	100	✔️	68.63	68.37	88.80	88.66	🔗
DeepCluster V2	ResNet50	100	✔️
DINO	ResNet50	100	✔️
MoCo V2+	ResNet50	100	✔️	62.61	66.84	85.40	87.60	🔗
NNCLR	ResNet50	100	✔️
ReSSL	ResNet50	100	✔️
SimCLR	ResNet50	100	✔️
Simsiam	ResNet50	100	✔️
SupCon	ResNet50	100	✔️
SwAV	ResNet50	100	✔️
VIbCReg	ResNet50	100	✔️
VICReg	ResNet50	100	✔️
W-MSE	ResNet50	100	✔️

Training efficiency for DALI

We report the training efficiency of some methods using a ResNet18 with and without DALI (4 workers per GPU) in a server with an Intel i9-9820X and two RTX2080ti.

Method	Dali	Total time for 20 epochs	Time for a 1 epoch	GPU memory (per GPU)
Barlow Twins	❌	1h 38m 27s	4m 55s	5097 MB
	✔️	43m 2s	2m 10s (56% faster)	9292 MB
BYOL	❌	1h 38m 46s	4m 56s	5409 MB
	✔️	50m 33s	2m 31s (49% faster)	9521 MB
NNCLR	❌	1h 38m 30s	4m 55s	5060 MB
	✔️	42m 3s	2m 6s (64% faster)	9244 MB

Note: GPU memory increase doesn't scale with the model, rather it scales with the number of workers.

Citation

If you use solo-learn, please cite our paper:

@article{JMLR:v23:21-1155,
  author  = {Victor Guilherme Turrisi da Costa and Enrico Fini and Moin Nabi and Nicu Sebe and Elisa Ricci},
  title   = {solo-learn: A Library of Self-supervised Methods for Visual Representation Learning},
  journal = {Journal of Machine Learning Research},
  year    = {2022},
  volume  = {23},
  number  = {56},
  pages   = {1-6},
  url     = {http://jmlr.org/papers/v23/21-1155.html}
}

wgchang/solo-learn