Advanced Concepts in Self-Supervised Learning

In this section, we will have a look at some more advanced topics around lightly. For the moment lightly focuses mostly on contrastive learning methods. In contrastive learning, we create multiple views of each sample and during the training of the model force similar views (originating from the same sample) to be close to each other respective different views (originating from different samples to be far away. Views are typically obtained using augmentation methods.

Through this procedure, we train invariances towards certain augmentations when training models using contrastive learning methods.

Different augmentations result in different invariances. The invariances you want to learn heavily depend on the type of downstream task you want to solve. Here, we group the augmentations by the type of invariance they induce and show examples of when such invariances can be useful.

For example, if we use color jittering and random grayscale during the training of a self-supervised model, we train the model to put the two augmented versions of the input image very close to each other in the feature space. We essentially train the model to ignore the color augmentations.

Shape Invariances

  • Random cropping E.g. We don’t care if an object is small or large or only partially in the image.

  • Random Horizontal Flip E.g. We don’t care about “left and right” in images.

  • Random Vertical Flip E.g. We don’t care about “up and down” in images. This can be useful for satellite images.

  • Random Rotation E.g. We don’t care about the orientation of the camera. This can be useful for satellite images.

Texture Invariances

  • Gaussian Blur E.g. We don’t care about the details of a person but the overall shape.

Color Invariances

  • Color Jittering E.g. We don’t care if a car is blue or red

  • Random Grayscale E.g. We don’t care about the color of a tree

  • Solarization E.g. We don’t care about color and brightness

Some interesting papers regarding invariances in self-supervised learning:


Picking the right augmentation method seems crucial for the outcome of training models using contrastive learning. For example, if we want to create a model classifying cats by color we should not use strong color augmentations such as color jittering or random grayscale.


Lightly uses the collate operation to apply augmentations when loading a batch of samples using the PyTorch dataloader.

The built-in collate class provides a set of common augmentations used in SimCLR and MoCo. Instead of a single batch of images, it returns a tuple of two batches of randomly transformed images.

If you use the Command-line tool you have access to all the SimCLR collate augmentations. You find the default parameters here: Default Settings.

Since gaussian blur, solarization and random rotations by 90 degrees are not supported in torchvision, we added them to lightly lightly.transforms

You can build your own collate function by inheriting from

# create a dataset using SimCLR augmentations
collate_fn =
dataloader_train_simclr =

# same augmentation but without blur and resize images to 128x128
collate_fn =
dataloader_train_simclr =


You can disable the augmentations by either setting the probability to 0.0 or making sure the augmentation has no effect. For example, random cropping can be disabled by setting min_scale=1.0.


Lightly supports at the moment the following models for self-supervised learning:

Do you know a model that should be on this list? Please add an issue on GitHub :)

All models have a backbone component. This could be a ResNet. When creating a self-supervised learning model you pass it a backbone. You need to make sure the backbone output dimension matches the input dimension of the head component for the respective self-supervised model.

Lightly has a built-in generator for ResNets. However, the model architecture slightly differs from the official ResNet implementatation. The difference is in the first few layers. Whereas the official ResNet starts with a 7x7 convolution the one from lightly has a 3x3 convolution.

  • The 3x3 convolution variant is more efficient (less parameters and faster processing) and is better suited for small input images (32x32 pixels or 64x64 pixels). We recommend to use the lighlty variant for cifar10 or running the model on a microcontroller (see

  • However, the 7x7 convolution variant is better suited for larger images since the number of features is smaller due to the stride and additional MaxPool2d layer. For benchmarking against other academic papers on datasets such as ImageNet, Pascal VOC, MOCO, etc. use the torchvision variant.

# create a lightly ResNet
resnet = lightly.models.ResNetGenerator('resnet-18')

# alternatively create a torchvision ResNet backbone
resnet_torchvision = torchvision.models.resnet18()

# remove the last linear layer and add an adaptive average pooling layer
backbone = nn.Sequential(

# create a simclr model based on ResNet
class SimCLR(torch.nn.Module):
    def __init__(self, backbone, hidden_dim, out_dim):
        self.backbone = backbone
        self.projection_head = SimCLRProjectionHead(hidden_dim, hidden_dim, out_dim)

    def forward(self, x):
        h = self.backbone(x).flatten(start_dim=1)
        z = self.projection_head(h)
        return z

resnet_simclr = SimCLR(backbone, hidden_dim=512, out_dim=128)

You can also use custom backbones with lightly. We provide a colab notebook to show how you can use torchvision or timm models.


We provide the most common loss function for contrastive learning and a symmetric negative cosine similarity loss for non-contrastive methods.

Memory Bank

Since contrastive learning methods benefit from many negative examples, larger batch sizes are preferred. However, not everyone has a multi GPU cluster at hand. Therefore, alternative tricks and methods have been derived in research. One of them is a memory bank keeping past examples as additional negatives.

For an example of the memory bank in action have a look at Tutorial 2: Train MoCo on CIFAR-10.

For more information check the documentation: lightly.loss.memory_bank.MemoryBankModule.

# to create a NTXentLoss with a memory bank (like for MoCo) set the
# memory_bank_size parameter to a value > 0
criterion = lightly.loss.NTXentLoss(memory_bank_size=4096)
# the memory bank is used automatically for every forward pass
y0, y1 = resnet_moco(x0, x1)
loss = criterion(y0, y1)

Obtaining Good Embeddings

We optimize the workflow of selecting only important datapoints by using low-dimensional embeddings. This has two benefits:

  • Low-dimensional embeddings have more meaningful distance metrics. We know that the data usually lies on a manifold in high-dimensional spaces (see curse of dimensionality). Even very similar samples might have a high L2-distance or low cosine similarity in high embeddings.

  • Most algorithms to select a subset based on the embeddings scale with the dimensionality. Therefore low-dimensional embeddings can significantly reduce computing time.

We leverage self-supervised learning to obtain good features/representations/embedddings of your unlabeled data. The quality of the representations depends heavily on the chosen augmentations. For example, imagine you want to train a classifier to detect healthy and unhealthy leaves. Training self-supervised models with color augmentation enabled would make the model and therefore the embeddings invariant towards different colors. However, the color might be a very important feature of the leave to determine whether it is healthy (green) or not (brown).

Extracting specific Video Frames

When working with videos, it is preferred not to have to extract all the frames beforehand. With lightly we can not only subsample the video to find interesting frames for annotation but also extract only these frames.

Let’s have a look at how this works:

import os
import lightly

# read the list of filenames (e.g. from the Lightly Docker output)
with open('selected_filenames.txt', 'r') as f:
    filenames = [line.rstrip() for line in f]

# let's have a look at the first 5 filenames
# >>> '068536-mp4.png'
# >>> '138032-mp4.png'
# >>> '151774-mp4.png'
# >>> '074234-mp4.png'
# >>> '264863-mp4.png'

path_to_video_data = 'video/'
dataset =

# let's get the total number of frames
# >>> 341965

# Now we have to extract the frame number from the filename.
# Since the length of the filename should always be the same,
# we can extract the substring simply using indexing.

# we can experiment until we find the right match
# >>> '068536'

# let's get all the substrings
frame_numbers = [fname[-14:-8] for fname in filenames]

# let's check whether the first 5 frame numbers make sense
# >>> ['068536', '138032', '151774', '074234', '264863']

# now we convert the strings into integers so we can use them for indexing
frame_numbers = [int(frame_number) for frame_number in frame_numbers]

# let's get the first frame number
img, label, fname = dataset[frame_numbers[0]]

# a quick sanity check
# fname should again be the filename from our list
print(fname == filenames[0])
# >>> True

# before saving the images make sure an output folder exists
out_dir = 'save_here_my_images'
if not os.path.exists(out_dir):

# let's get all the frames and dump them into a new folder
for frame_number in frame_numbers:
    img, label, fname = dataset[frame_number]
    dst_fname = os.path.join(out_dir, fname)

# want to save the images as jpgs instead of pngs?
# we can simply replace the file engine .png with .jpg

#for frame_number in frame_numbers:
#    img, label, fname = dataset[frame_number]
#    dst_fname = os.path.join(out_dir, fname)
#    dst_fname = dst_fname.replace('.png', '.jpg')

The example has been tested on a system running Python 3.7 and lightly 1.0.6