A Comparative Evaluation of Self-Supervised Methods Applied to Rock
Images Classification
Van Thao Nguyen
1,2
, Dominique Fourer
2
and D
´
esir
´
e Sidib
´
e
2
and Jean-Franc¸ois Lecomte
1
and Souhail Youssef
1
1
IFPEN, Ruel-Malmaison, France
2
IBISC - Univ.
´
Evry Paris-Saclay,
´
Evry-Courcouronnes, France
Keywords:
Self-Supervised Learning, Representation Learning, Digital Rock Physics Digital Rock Physics (DRP),
Image Classification.
Abstract:
Digital Rock Physics DRP is a discipline that employs advanced computational techniques to analyze and sim-
ulate rock properties at the pore-scale level. Recently, Self-Supervised Learning (SSL) has shown promising
outcomes in various application domains, but its potential in DRP applications remains largely unexplored. In
this study, we propose to assess several self-supervised representation learning methods designed for automatic
rock category recognition. Hence, we demonstrate how different SSL approaches can be specifically adapted
for DRP, and comparatively evaluated on a new dataset. Our objective is to leverage unlabeled micro-CT
(Computed Tomography) image data to train models that capture intricate rock features and obtain representa-
tions that enhance the accuracy of classical machine-learning-based rock images classification. Experimental
results on a newly proposed rock images dataset indicate that a model initialized using SSL pretraining out-
performs its non-self-supervised learning counterpart. Particularly, we find that MoCo-v2 pretraining provides
the most benefit with limited labeled training data compared to other models, including supervised model.
1 INTRODUCTION
Understanding fluid flow and mass transport in porous
media holds paramount significance in various geo-
logical and engineering applications such as ground-
water management, soil mechanics and geotechni-
cal engineering (geological carbon storage, subsur-
face contaminant transport or CO
2
-sequestration to
name a few). However, characterizing complex rocks
remains a challenging endeavor due to inherent het-
erogeneities observed at all scales of observation and
measurement (Andr
¨
a et al., 2013). In recent decades,
the advancement of synchrotron X-ray tomography
has substantially enhanced the acquisition of highly
precise 3D images, thereby revolutionizing the field
of material research. The information acquired from
the images aids in determining petrophysical and flow
properties of porous media, crucial for understand-
ing for understanding their widespread presence in
soil, rock formations, and composite materials (Blunt
et al., 2013). For the successful development of digi-
tal rock models, it is crucial to provide a comprehen-
sive description and characterization of porous me-
dia such as rocks for accurate classification. Tra-
ditional approaches to rock image classification pre-
dominantly rely on manual operations, leading to both
high costs and variable degrees of accuracy.
Nowadays, machine learning techniques achieve
outstanding performances in various application ar-
eas, and the field of automatic classification of rock
images is not an exception. Many works have been
done in DRP such as using shallow neural networks
to classify rock images (Guojian et al., 2013), identi-
fying the rock granularity by a Convolutional Neural
Networks (Cheng and Guo, 2017), or building a neu-
ral network to identify the rock mineral (Liu et al.,
2021). Despite the success in those different appli-
cations, most of state-of-the-art methods learn fea-
tures in a supervised way, and are restricted to a given
specific task. Furthermore, the process of labeling
rock images through micro-CT is a difficult problem
which requires significant exertion, computation, and
annotations from geological specialists (Karimpouli
and Tahmasebi, 2019) (Shim et al., 2023). To ad-
dress this challenge, one can rely on SSL methods
which have shown successes in building large-scale
deep-learning-based approaches to learn the underly-
ing representations from unlabeled data (Dosovitskiy
Nguyen, V., Fourer, D., Sidibé, D., Lecomte, J. and Youssef, S.
A Comparative Evaluation of Self-Supervised Methods Applied to Rock Images Classification.
DOI: 10.5220/0012319400003660
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2024) - Volume 2: VISAPP, pages
393-400
ISBN: 978-989-758-679-8; ISSN: 2184-4321
Proceedings Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.
393

et al., 2014) (Misra and Maaten, 2020).
SSL has recently emerged as a leading approach
in achieving state-of-the-art performance in visual
representation learning, eliminating the need for ex-
tensive dataset annotation. The SSL method fol-
lows a two-step process (as illustrated in Figure
1):Initially, the ConvNet is pretrained using unlabeled
data, addressing what is referred to as the pretext
task. Subsequently, fine-tuning on the target task
using labeled data is performed in the downstream
task. Notably, pretext task training is conducted au-
tonomously, without requiring external human super-
vision. Consequently, this approach leverages unla-
beled data to boost system performance. SSL tech-
niques have demonstrated strong performances, par-
ticularly in the field of natural image classification,
such as the ImageNet challenge (Jaiswal et al., 2020).
Nonetheless, these methods are typically designed for
color images, aiming to acquire color image represen-
tation. As a result, they may not be well-suited for
a DRP dataset, which is often based on a grayscale
conveying a specific physical information. In such
datasets, images exhibit similar spatial structures, and
obtaining annotated data is often more challenging
compared to natural images.
In this paper, we evaluate the effectiveness of
four distinct and promising SSL approaches, adapted
for the automatic classification of micro-CT rock im-
ages. We have chosen four methods, namely SimCLR
(Chen et al., 2020a), MoCo-v2 (Chen et al., 2020b),
BYOL (Grill et al., 2020) and NNCLR (Dwibedi
et al., 2021), based on their promising results in image
classification tasks and their demonstrated computa-
tional efficiency, as supported by existing literature.
Hence, each of these investigated models presents a
unique set of pros and cons that can complement one
another. The objective of this work is to provide
practical insights through a comparative evaluation of
these methods when adapted to the context of DRP.
The main contributions of this paper are summa-
rized as follows:
1. We show that our proposed Self-Supervised
Learning methods can yield superior representa-
tions and initialization when compared to those
obtained without SSL pretraining for the task of
micro-CT rock images analysis.
2. We conduct supervised fine-tuning experiments
using varying fractions of labeled data. Our find-
ings show that our pretrained representations are
of greater quality than those pretrained on Ima-
geNet. In particular, the SSL methods achieve
superior classification performances when dealing
with very limited labeled data.
Figure 1: Illustration of the general pipeline for Self-
Supervised Learning (SSL).
The rest of the paper is organized as follows. Sec-
tion 2 briefly describes the investigated representation
learning methods, as well as the training procedures.
The experimental setups and results are presented and
discussed in Section 3 and the paper ends with con-
cluding remarks in Section4.
2 METHODS
In order to learn a robust representation of rock im-
ages, our primary objective is to ensure that the
learned embeddings has the ability to capture dis-
tinct features and acquire morphological features
from high-resolution X-ray micro computed tomog-
raphy images of porous media. These morpholog-
ical features serve as invaluable building blocks for
our efforts, providing a wealth of information criti-
cal for next downstream tasks. To achieve this goal,
we employ four promising self-supervised learning
frameworks, namely SimCLR, MoCo-v2, BYOL, and
NNCLR, specifically designed for the context of DRP
Each model comes with its own unique set of ben-
efits such as SimCLR is renowned for its simplicity of
implementation and independence from specialized
architectures or memory banks (Chen et al., 2020a).
SimCLR exhibits exceptional performance, particu-
larly when used with large batch sizes,but at a higher
computational cost. To address this, we also consider
for Moco-v2, which uses a batch size of 256 and has
demonstrated performance comparable to SimCLR
on ImageNet, despite SimCLR’s larger batch size of
8192 (Chen et al., 2020b).
BYOL achieves impressive performance and
demonstrates robustness to the choice of image aug-
mentations compared to contrastive methods, primar-
ily by wiping out the reliance on negative pairs (Grill
et al., 2020). Moreover, given the unique nature of
our rock images dataset, the selection of appropri-
ate transformations in SSL models is crucial. Hence,
NNCLR is selected as it reduces the dependence on
complex augmentations by leveraging nearest neigh-
bors, resulting in superior performance compared to
other frameworks (Dwibedi et al., 2021).
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394

(a) SimCLR (b) MoCo-v2 (c) BYOL (d) NNCLR
Figure 2: Different architecture pipelines for self-supervised learning.
An illustration of SimCLR, MoCo-v2, BYOl and
NNCLR frameworks is presented in Figure 2. Our
code, inspired by (Da Costa et al., 2022), is available
at https://github.com/nguyenva04/drp ssl
All four frameworks consist of three primary com-
ponents: (1) a data augmentation module that trans-
forms a data sample x into two different views (x
i
,x
j
),
which is a positive pair (different augmented views of
the same image). (2) a neural encoder module which
consists of 2 encoders f
θ
(.) and f
ξ
(.) encoding the in-
put features into a fixed dimensional embedding. (3) a
projection head g(.) that maps embeddings (h
i
,h
j
) to
projections (z
i
,z
j
) in a latent space related to the cho-
sen loss function. In the following subsections, we
further describe each of the selected SSL methods.
2.1 SimCLR
SimCLR, the Simple Framework for Contrastive
Learning of visual Representations, is a contrastive
learning approach that generates augmentation-
invariant embedding for input images (Chen et al.,
2020a). SimCLR relies on two fundamental concepts:
employing extensive data augmentation techniques to
create correlated views of the same input and using a
large batch size with numerous negative examples. In
a batch, besides the original image and its augmented
versions, all other images are considered as negative
samples.
SimCLR leverages a symmetric dual-encoder ar-
chitecture,wherein both encoders share parameters
with each other, as illustrated in Figure (2a). Dur-
ing training, both encoders are updated end-to-end
through backpropagation by an optimizer. The Sim-
CLR framework uses the InfoNCE contrastive loss,
also known as the normalized temperature-scaled
cross-entropy loss (NT-Xent) expressed as:
L
i, j
(z) = −log

exp(sim(z
i
,z
j
)/τ)
∑
∀k,k̸=i
exp(sim(z
i
,z
k
)/τ)

, (1)
where sim(x, y) is the cosine similarity between vec-
tors x and y, and τ is the temperature hyperparameter.
2.2 MoCo-v2
Similar to MoCo-v1 (He et al., 2020), MoCo-
v2 (Chen et al., 2020b) extends the concept of in-
stance discrimination and is designed to learn repre-
sentations using a contrastive learning criterion.
For each training sample, (x
i
,x
j
) pairs are gener-
ated through a set of data augmentations. These pairs,
x
i
and x
j
, are separately encoded to produce embed-
ding z
i
and z
j
by both an encoder and a momentum
encoder. The momentum encoder shares the same
architecture as the primary encoder, as illustrated in
Figure 2b. However, unlike the encoder, the momen-
tum encoder is not backpropagated after training each
mini-batch. Instead, it is updated using the parame-
ters of the encoder, following the approach outlined
in (Oord et al., 2018):
ξ ← mξ +(1 − m)θ, (2)
as represented by Eq. (2). ξ and θ are the parameters
of each encoder and m ∈ [0,1] is the momentum coef-
ficient. This setup allows the momentum encoder to
update slowly and smoothly compared to the primary
encoder. The core idea driving MoCo is to maintain a
dynamic dictionary as a queue of data samples. The
dictionary can be much larger than the mini-batch and
easy to adjust. At the end of each training step, we
update it by taking the embeddings of the momentum
encoder from the current training step and concatenat-
ing them at the end of the queue. Subsequently, we
discard the oldest embeddings from the queue. This
approach helps maintain the consistency of the dictio-
nary, as the oldest embeddings are often outdated and
inconsistent with the new entries. MoCo-v2 demon-
strates a simple improvement compared to MoCo-v1
on the ImageNet dataset by using a projection head.
A Comparative Evaluation of Self-Supervised Methods Applied to Rock Images Classification
395

2.3 BYOL
BYOL uses two neural networks for learning, namely
the online network and the target network, as illus-
trated in Figure 2c. The online network, defined by a
set of weights θ, comprises three stages: an encoder
f
θ
, a projector g
θ
, and a predictor q
θ
, all of which
are trainable. On the other hand, the target network,
sharing the same architecture as the online network
except for the prediction head, is non-trainable and
defined by a set of weights ξ. The regression targets
for training the online network are provided by the
target network, whose parameters ξ are updated as a
moving average of the online network’s parameters θ.
The mean squared error (MSE) loss is used by
comparing the normalized prediction q
θ
(z
i
) generated
by the online network and the projection z
ξ
produced
by the target network. In contrast to approaches like
MoCo and SimCLR, which use negative samples to
prevent collapse, BYOL takes a different approach.
BYOL encourages the online projection to encode
more and more information by adding a predictor to
the online network and using the moving average of
the online network parameters as the target network.
This strategy helps avoid collapsed solutions, such as
constant representations.
2.4 NNCLR
In contrast to other mentioned self-supervised learn-
ing (SSL) methods, NNCLR (Dwibedi et al., 2021)
introduced a novel concept regarding the selection
of positive samples. Instead of generating positive
samples for an image through augmentation, NNCLR
demonstrated that positive samples can be obtained
by using the nearest neighbors of the sample in the
support set of embeddings, creating more diversi-
fied positive pairs. NNCLR acquired positive sam-
ples by determining the nearest neighbors in terms
of Euclidean distance of a given sample within the
learned latent space of the dataset. This approach
offers more semantic class-wise variations compared
to pre-defined transformations, which tend to provide
more geometric information.
NNCLR shares similarities with SimCLR in terms
of its straightforward architecture and with MoCo for
the utilization of dynamic dictionary as a support set
(first-in-first-out), as shown in Figure 2d. This sup-
port set is dynamically updated at the end of each
training step by concatenating the current embeddings
at the end of the queue. Unlike approaches that re-
quire extensive computational resources due to a di-
verse set of negative samples, NNCLR does not heav-
ily rely on predefined data augmentation. It has the
capability to establish connections among multiple
samples that may potentially belong to the same se-
mantic class.
3 EXPERIMENTAL RESULTS
In this section, we conduct several experiments us-
ing our own DRP Dataset. We begin by introduc-
ing the dataset, followed by pre-training adaptation,
pre-training protocal, and transfer learning strategies.
Then, we compare the SSL models with a supervised
baseline method (ResNet-50) (Deng et al., 2009).
3.1 Dataset
In this paper, we use a subset of a larger collec-
tion of high-resolution 3D images of rock samples
obtained through a campaign of IFP Energies Nou-
velles (IFPEN). The images were acquired using to-
mography technology, which produces a series of 3D
volumes showing the spatial distribution of the pore
space in a rock sample. The dataset comprises large
tomograms of fifty sandstone samples, each measur-
ing 1100 x 1100 x 2800, are divided into smaller Re-
gions of Interest (ROI) to create a dataset. These large
tomograms are divided into ROI, each consisting of
128 × 128 pixels, capturing various structures present
in the larger tomograms. The creation of each ROI in-
volves randomly cutting it in any of the three dimen-
sions. As illustrated in Figure 3, there are nine distinct
sandstones in the dataset, and the number of images
varies according to the sandstone. Our dataset con-
sists of 62.500 images, with each sample producing
1.250 images. To enhance data quality, the dataset un-
dergoes a cleaning process to remove outliers and ar-
tifacts, involving an examination of the average gray
level of each image. Finally, for training and testing
purposes, the dataset is split into two distinct sets in
an 8:2 ratio.
The characteristic quantities for the sandstones
used in this paper are listed in Table 1.
Table 1: Characteristics quantities of sandstone samples.
Type Porosity
exp
Permeability
exp
(mD) Nb samples
Bentheimer 24% 2000 6
Berea Upper Gray 21% 390 6
Boise 28% 1800 5
Briarhill 24% 3500 5
Castlegate 28% 1000 6
Idaho Gray 30% 6000 6
Leopard Sandstone 21% 1200 4
Michigan 21% 900 6
Liver Rock 24% 1050 6
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396

(a) Bentheimer (b) Berea Upper Gray (c) Boise (d) Briarhill (e) Liver Rock
(f) Castlegate (g) Idaho Gray (h) Leopard Sanstone (i) Michigan
Figure 3: A representative example from each class in the DRP dataset.
3.2 Contrastive Pretraining for DRP
Adaptation
We use ResNet (Deng et al., 2009) as ConvNet for all
our SSL models. This decision enables a more ob-
jective comparison of the representations learned by
various approaches. Our experiments use the entire
DRP training dataset, and to leverage potential con-
vergence advantages, we initialize the models with
ImageNet weights. The use of ImageNet weights
is advantageous due to their widespread availabil-
ity, eliminating the need for additional computational
costs during model initialization. Given that our data
is in grayscale, a specific initialization step is imple-
mented. We initialize the weights of the first hid-
den layer by computing the mean of the pre-trained
weights from the RGB channels (Ahmad and Shin,
2021).
We adapt our data augmentation strategy to create
views suitable for the rock classification task. Tra-
ditional data augmentations commonly used in SSL
for natural images are unsuitable for our dataset. For
example, color jittering and random grayscale trans-
formations are ineffective for micro-CT grayscale im-
ages, as these techniques do not yield significant im-
provements within the context of the DRP dataset. In-
stead, our augmentation strategy includes horizontal
flipping, vertical flipping, Gaussian blur, and coarse
dropout, as shown in Figure 4. These transformations
are specifically chosen to enhance the representation
learning process for the DRP dataset.
3.3 Pretraining Protocol
For SimCLR, we used the LARS optimizer to stabi-
lize the pre-training, setting the learning rate of 0.01,
a temperature parameter τ = 0.2, and a batch size
of 128. A linear warmup is applied for the first 10
epochs, followed by learning rate decay using the co-
sine decay schedule without restart.
For MoCo-v2, BYOL, NNCLR, and the baseline
model, we used the SGD optimizer with a weight de-
cay of 10
−5
and a momentum value of 0.9. MoCo
used a fixed momentum update coefficient of 0.996,
while BYOL used an exponential moving average of
momentum, starting at 0.996 and reaching 1.
The batch size is set to 128 for MoCo-v2 and 64
for BYOL, NNCLR, and the baseline. The training
of SimCLR, BYOL, and NNCLR is conducted on
two NVIDIA Tesla V100-PCIE-16GB GPUs, while
MoCo-v2 is trained on a single NVIDIA RTX A2000
8GB GPU. MoCo-v2 and NNCLR use a dynamic dic-
tionary of size 4096. Further details, including the
number of epochs and training times for our models,
are presented in Table 2.
Table 2: Training time.
Framework nb. epochs times
SimClr 100 4h50
MoCo-v2 50 4h30
BYOL 50 9h
NNCLR 50 6h
A Comparative Evaluation of Self-Supervised Methods Applied to Rock Images Classification
397

(a) Original Image (b) Vertical Flip (c) Horizontal Flip (d) Gaussian Blur (e) Cutout
Figure 4: Illustration of different data transformations.
3.4 Transfer Learning
In this section, we evaluate the quality of represen-
tations by fine-tuning on downstream tasks. The fine-
tuning process involves using different fractions of la-
beled training data, where the label fraction denotes
the proportion of retained data labels during training.
For example, a model trained with a 1% label frac-
tion has access to only 1% of all labels, while the re-
maining 99% are hidden. This simulation is reflective
of real-world scenarios where large amounts of data
remain unlabeled, and only a small fraction of well-
labeled data is available for supervised training. In
our experiments, we use label fractions of 1%, 10%,
and 50% for the training set in our dataset.
In order to evaluate the quality of the represen-
tations, we use two distinct approaches. Firstly, we
freeze the backbone model’s parameters and con-
duct training of a linear classifier using the labeled
data. Secondly, we unfreeze all layers and carried
out an End-to-End fine-tuning of the entire model,
again using the labeled data. We also conduct a base-
line for comparison, contrasting classical supervised
methods with self-supervised models. This baseline
uses ImageNet-pretrained models without undergoing
contrastive pretraining but shares the same backbone
network as the contrastive models.
The linear evaluation, End-to-End, and baseline
approaches all share identical configurations. Our
model undergoes training for 50 epochs, utilizing a
batch size of 64, an initial learning rate of 0.01, and
a scheduled learning rate decrease of 0.1 after every
20 epochs. The SGD optimizer is employed for opti-
mization.
3.5 Results
Through the comparison of classifiers relying on pre-
trained networks such as SimCLR, MoCo-v2, BYOL
and NNCLR, as opposed to a classifier initialized with
ImageNet, we aim to show how well a pretrained
neural network provides useful representations. We
started with linear evaluation (Oord et al., 2018) and
we visualize the performance of different approaches
by using linear evaluation in Table 3 and by End-to-
End in Table 4 at various fractions.
The results in Table 3 show that MoCo-v2 out-
performs the other self-supervised learning models
and the supervised baseline on all metrics (recall,
precision, F-score, and top-1 accuracy) for all label
fractions (1%, 10%, and 50%). Specifically, MoCo-
v2 achieves a top-1 accuracy of 63.64% with a la-
bel fraction of 1%, 69.09% with a label fraction of
10%, and 72.96% with a label fraction of 50%. This
is significantly better than the supervised baseline,
which achieves a top-1 accuracy of 43.29% with a
label fraction of 1%, 53.56% with a label fraction
of 10%, and 55.75% with a label fraction of 50%.
The other self-supervised learning models (SimCLR,
BYOL and NNCLR) also outperform the supervised
baseline, but they do not perform as well as MoCo-v2.
These results are promising since it shows the ability
of SSL models to learn representations that capture
rich data semantics and informative features.
Table 3: ResNet-50 Linear Evaluation.
Method name Recall Precision F-Score top-1 Acc
SimClr 1% 58.48 57.65 58.06 58.48
MoCo-v2 1% 61.73 62.35 62.04 63.64
BYOL 1% 56.07 55.04 55.55 56.07
NNCLR 1% 55.17 55.58 55.37 55.17
Baseline 1% 43.29 39.48 41.30 43.29
SimClr 10% 64.60 63.90 64.25 64.60
MoCo-v2 10% 67.64 67.20 67.42 69.09
BYOL 10% 64.54 64.32 64.45 64.54
NNCLR 10% 66.74 66.56 66.65 66.74
Baseline 10% 53.56 51.99 52.76 53.56
SimClr 50% 69.18 68.84 69.01 69.18
MoCo-v2 50% 71.91 71.56 71.73 72.96
BYOL 50% 69.05 68.66 68.85 69.05
NNCLR 10% 70.34 69.79 70.06 70.34
Baseline 50% 55.75 55.31 55.53 55.75
Subsequently, an empirical investigation is con-
ducted to assess the usefulness of our SSL pre-
trained models in providing enhanced representations
through End-to-End strategies. Our findings indi-
cate that pre-training using all SSL fine-tuning mod-
VISAPP 2024 - 19th International Conference on Computer Vision Theory and Applications
398

Table 4: ResNet-50 End-to-End.
Method name Recall Precision F-Score top-1 Acc
SimClr 1% 60.78 61.10 60.94 60.78
MoCo-v2 1% 66.53 68.28 67.39 66.53
BYOL 1% 62.05 64.40 63.20 62.05
NNCLR 1% 60.40 62.05 61.21 60.40
Baseline 1% 58.21 58.36 58.29 58.21
SimClr 10% 75.58 75.30 75.44 75.58
MoCo-v2 10% 79.84 79.69 79.77 79.84
BYOL 10% 77.84 77.80 76.81 77.84
NNCLR 10% 75.43 75.43 75.43 75.43
Baseline 10% 73.81 73.73 73.77 73.81
SimClr 50% 89.42 89.43 89.43 89.42
MoCo-v2 50% 90.05 90.20 90.13 90.37
BYOL 50% 90.16 90.11 90.14 90.16
NNCLR 50% 90.45 90.46 90.46 90.45
Baseline 50% 90.55 90.55 90.55 90.55
Table 5: Accuracy improvements achieved by SSL pre-
trained models against model without SSL pretrained on the
DRP dataset.
SSL-framework SSL-pretrained ImageNet-pretrained 1% 10% 50%
SimClr Linear Model Linear Model 0.3508 0.2061 0.2409
MoCo-v2 Linear Model Linear Model 0.4700 0.2900 0.3087
BYOL Linear Model Linear Model 0.2952 0.2050 0.2386
NNCLR Linear Model Linear Model 0.2744 0.2461 0.2617
SimClr End-to-End End-to-End 0.042 0.024 -0.009
MoCo-v2 End-to-End End-to-End 0.1429 0.082 -0.002
BYOL End-to-End End-to-End 0.0660 0.055 -0.004
NNCLR End-to-End End-to-End 0.0376 0.022 -0.001
els significantly improve the accuracy with label-
efficiency for micro-CT images of rock classifica-
tion, self-supervised models outperform the super-
vised baseline by a larger margin when trained on
smaller datasets. In the results of fine-tuning, as
shown in Table 5, MoCo-v2 outperforms again the su-
pervised baseline and the other SSL models for small
label fractions (1%, 10%). The other SSL models
are similar to MoCo-v2 by outperforming the baseline
model when trained on small datasets. These results
suggest that self-supervised model yields proportion-
ally larger gains when End-to-End with fewer label
samples and less significant at larger label fraction.
This result is consistent with (Chen et al., 2020a),
which shows that self-supervised models trained on
smaller fractions of the ImageNet dataset gain better
improvement.
Our test results confirm that MoCo-v2 is a high-
performing SSL model. We also observe that BYOL
performs worse than expected, despite its high accu-
racy on ImageNet. One possible explanation is that
MoCo-v2 may learn better representations with our
chosen set of strong data augmentations. (Huang
et al., 2022) has shown that MoCo-v2 catches up
to BYOL in terms of linear accuracy when trained
with more complex data augmentations. However,
it is important to note that we trained BYOL with a
smaller batch size (64), while BYOL has been shown
to achieve optimal results with a larger batch size of
4096, reaching 74.3% accuracy after training on 512
TPUs. Therefore, it is not fair to directly compare the
performance of MoCo-v2 and BYOL in this case.
The additional results for each class are presented
in Table 6. It becomes evident that Idaho Gray stands
out as the most easily discernible rock type, while
Leopard proves to be the most challenging for recog-
nition. Notably, compared to the other classes in our
dataset, the Leopard rock has the least amount com-
pared to other classes in our dataset, which could
potentially explain its comparatively lower accuracy
score. In limited labeled DRP data scenarios, SSL
frameworks outperform supervised methods signifi-
cantly. However, when abundant labeled data is avail-
able, the improvement from SSL models in the DRP
context diminishes compared to situations with fewer
data samples.
Table 6: Additional results: Accuracy per class by End-to-End.
Method name Bentheimer Berea UG Boise Briarhill Castlegate Idaho Gray Leopard Michigan Liver Rock
SimClr 1% 71.70 66.40 72.62 55.61 73.63 73.61 35.67 41.35 49.10
MoCo-v2 1% 71.83 73.60 69.42 56.10 77.44 85.37 42.59 44.16 59.16
BYOL 1% 59.28 62.20 58.29 61.46 74.90 83.43 30.66 54.44 62.61
NNCLR 1% 66.10 53.40 58.85 56.10 70.50 84.57 28.86 54.44 59.33
Baseline 1% 65.42 59.27 58.53 55.85 73.43 79.49 35.97 36.54 45.62
SimClr 10% 84.65 81.27 76.86 70.91 89.92 85.91 47.40 72.55 60.54
MoCo-v2 10% 86.98 83.73 81.10 76.12 93.79 87.44 56.41 73.88 70.84
BYOL 10% 83.11 82.33 71.74 72.92 90.12 97.64 49.00 68.27 64.41
NNCLR 10% 80.51 81.73 78.78 69.79 90.05 86.17 54.61 63.73 66.15
Baseline 10% 86.45 79.80 78.70 67.31 89.65 84.70 48.90 68.67 60.87
SimClr 50% 90.99 92.80 89.43 87.42 97.13 92.92 80.66 88.31 84.48
MoCo-v2 50% 90.12 92.07 89.60 88.86 96.53 91.98 83.53 90.05 86.09
BYOL 50% 93.93 93.33 86.55 89.42 97.66 95.73 82.16 89.31 82.01
NNCLR 10% 93.39 90.93 88.39 87.82 97.93 94.46 81.66 90.72 85.02
Baseline 50% 94.26 93.47 88.63 87.74 97.60 95.12 78.86 88.71 84.15
A Comparative Evaluation of Self-Supervised Methods Applied to Rock Images Classification
399

4 CONCLUSION
In this study, we have discovered that SSL frame-
works can provide suitable representations for the
analysis of micro-CT images of rock. These repre-
sentations significantly enhance the performance of
downstream tasks compared to traditional supervised
learning methods across all label fractions. The chal-
lenges posed by the scarcity and cost associated with
manual annotation in the domain of DRP make it diffi-
cult to acquire datasets of a scale comparable to those
in well-established computer vision domains. Our
success in showcasing performance improvements
over traditional supervised learning methods, particu-
larly in scenarios with limited labeled data, holds the
potential for broader applications in micro-CT image
analysis, encompassing both 2D and 3D representa-
tions characterized by intricate textures. A pretrained
network tailored for DRP can be used for different
purposes, including classification, segmentation, and
the estimation of rock properties.
Based on our analysis, we anticipate that in-
creased computational resources allocated to model
training could result in improved performance. Addi-
tionally, we identify the potential for further research,
particularly in the exploration of various downstream
tasks such as regression which are of interest for 3D
tomography of rock images.
REFERENCES
Ahmad, I. and Shin, S. (2021). An approach to run pre-
trained deep learning models on grayscale images.
In 2021 International Conference on Artificial Intel-
ligence in Information and Communication (ICAIIC),
pages 177–180.
Andr
¨
a, H., Combaret, N., Dvorkin, J., Glatt, E., Han, J., Ka-
bel, M., Keehm, Y., Krzikalla, F., Lee, M., Madonna,
C., et al. (2013). Digital rock physics bench-
marks—part i: Imaging and segmentation. Computers
& Geosciences, 50:25–32.
Blunt, M. J., Bijeljic, B., Dong, H., Gharbi, O., Iglauer, S.,
Mostaghimi, P., Paluszny, A., and Pentland, C. (2013).
Pore-scale imaging and modelling. Advances in Water
resources, 51:197–216.
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G.
(2020a). A simple framework for contrastive learning
of visual representations. In International conference
on machine learning, pages 1597–1607. PMLR.
Chen, X., Fan, H., Girshick, R., and He, K. (2020b). Im-
proved baselines with momentum contrastive learn-
ing. arXiv preprint arXiv:2003.04297.
Cheng, G. and Guo, W. (2017). Rock images classification
by using deep convolution neural network. In Jour-
nal of Physics: Conference Series, volume 887, page
012089. IOP Publishing.
Da Costa, V. G. T., Fini, E., Nabi, M., Sebe, N., and Ricci,
E. (2022). solo-learn: A library of self-supervised
methods for visual representation learning. Journal
of Machine Learning Research, 23(56):1–6.
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-
Fei, L. (2009). Imagenet: A large-scale hierarchical
image database. In 2009 IEEE conference on com-
puter vision and pattern recognition, pages 248–255.
Ieee.
Dosovitskiy, A., Springenberg, J. T., Riedmiller, M., and
Brox, T. (2014). Discriminative unsupervised fea-
ture learning with convolutional neural networks. Ad-
vances in neural information processing systems, 27.
Dwibedi, D., Aytar, Y., Tompson, J., Sermanet, P., and Zis-
serman, A. (2021). With a little help from my friends:
Nearest-neighbor contrastive learning of visual repre-
sentations. In Proceedings of the IEEE/CVF Interna-
tional Conference on Computer Vision, pages 9588–
9597.
Grill, J.-B., Strub, F., Altch
´
e, F., Tallec, C., Richemond, P.,
Buchatskaya, E., Doersch, C., Avila Pires, B., Guo,
Z., Gheshlaghi Azar, M., et al. (2020). Bootstrap your
own latent-a new approach to self-supervised learn-
ing. Advances in neural information processing sys-
tems, 33:21271–21284.
Guojian, C., Wei, M., Xinshan, W., Chunlong, R., and Junx-
iang, N. (2013). Research of rock texture identifica-
tion based on image processing and neural network. J
Xi’an Shiyou Univ (Nat Sci Edition).
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R. (2020).
Momentum contrast for unsupervised visual represen-
tation learning. In Proceedings of the IEEE/CVF con-
ference on computer vision and pattern recognition,
pages 9729–9738.
Huang, J., Kong, X., and Zhang, X. (2022). Revisiting the
critical factors of augmentation-invariant representa-
tion learning. In European Conference on Computer
Vision, pages 42–58. Springer.
Jaiswal, A., Babu, A. R., Zadeh, M. Z., Banerjee, D., and
Makedon, F. (2020). A survey on contrastive self-
supervised learning. Technologies, 9(1):2.
Karimpouli, S. and Tahmasebi, P. (2019). Segmentation of
digital rock images using deep convolutional autoen-
coder networks. Computers & geosciences, 126:142–
150.
Liu, Y., Zhang, Z., Liu, X., Wang, L., and Xia, X. (2021).
Ore image classification based on small deep learning
model: Evaluation and optimization of model depth,
model structure and data size. Minerals Engineering,
172:107020.
Misra, I. and Maaten, L. v. d. (2020). Self-supervised learn-
ing of pretext-invariant representations. In Proceed-
ings of the IEEE/CVF conference on computer vision
and pattern recognition, pages 6707–6717.
Oord, A. v. d., Li, Y., and Vinyals, O. (2018). Representa-
tion learning with contrastive predictive coding. arXiv
preprint arXiv:1807.03748.
Shim, M. S., Thiele, C., Vila, J., Saxena, N., and Hohl, D.
(2023). Content-based image retrieval for industrial
material images with deep learning and encoded phys-
ical properties. Data-Centric Engineering, 4:e21.
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