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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. The library is self-contained, but it is possible to use the models outside of solo-learn. More details in our paper.

News

Roadmap and help needed

Methods available

Extra flavor

Backbones

Data

Evaluation

Training tricks

Logging

Requirements

Optional:

Installation

First clone the repo.

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

pip3 install .[dali,umap,h5] --extra-index-url https://developer.download.nvidia.com/compute/redist

If no Dali/UMAP/H5 support is needed, the repository can be installed as:

pip3 install .

For local development:

pip3 install -e .[umap,h5]
# Make sure you have pre-commit hooks installed
pre-commit 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 scripts/pretrain/. We are now using Hydra to handle the config files, so the common syntax is something like:

python3 main_pretrain.py 
    # path to training script folder
    --config-path scripts/pretrain/imagenet-100/ 
    # training config name
    --config-name barlow.yaml
    # add new arguments (e.g. those not defined in the yaml files)
    # by doing ++new_argument=VALUE
    # pytorch lightning's arguments can be added here as well.

After that, for offline linear evaluation, follow the examples in scripts/linear or scripts/finetune for finetuning the whole backbone.

For k-NN evaluation and UMAP visualization check the scripts in scripts/{knn,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
All4One ResNet18 1000 93.24 99.88 ?
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 ?
MoCo V3 ResNet18 1000 93.10 99.80 ?
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
All4One ResNet18 1000 72.17 93.35 ?
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 ?
MoCo V3 ResNet18 1000 68.83 90.57 ?
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
All4One ResNet18 400 ✔️ 81.93 - 96.23 - ?
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 ?
MoCo V3 ResNet18 400 ✔️ 80.36 80.36 95.18 94.96 ?
MoCo V3 ResNet50 400 ✔️ 85.48 84.58 96.82 96.70 ?
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 ✔️ 77.64 TODO 94.06 TODO ?
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 Finetuned Checkpoint
Barlow Twins ResNet50 100 ✔️ 67.18 67.23 87.69 87.98 ?
BYOL ResNet50 100 ✔️ 68.63 68.37 88.80 88.66 ?
MoCo V2+ ResNet50 100 ✔️ 62.61 66.84 85.40 87.60 ?
MAE ViT-B/16 100 ~ 81.60 (finetuned) ~ 95.50 (finetuned) ? ?

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 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}
}
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